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Peroni P, Liu Q, Lizarazu WZ, Xue S, Yi Z, Von Cossel M, Mastroberardino R, Papazoglou EG, Monti A, Iqbal Y. Biostimulant and Arbuscular Mycorrhizae Application on Four Major Biomass Crops as the Base of Phytomanagement Strategies in Metal-Contaminated Soils. PLANTS (BASEL, SWITZERLAND) 2024; 13:1866. [PMID: 38999706 DOI: 10.3390/plants13131866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 06/25/2024] [Accepted: 07/03/2024] [Indexed: 07/14/2024]
Abstract
Using contaminated land to grow lignocellulosic crops can deliver biomass and, in the long term, improve soil quality. Biostimulants and microorganisms are nowadays an innovative approach to define appropriate phytomanagement strategies to promote plant growth and metal uptake. This study evaluated biostimulants and mycorrhizae application on biomass production and phytoextraction potential of four lignocellulosic crops grown under two metal-contaminated soils. Two greenhouse pot trials were setup to evaluate two annual species (sorghum, hemp) in Italy and two perennial ones (miscanthus, switchgrass) in China, under mycorrhizae (M), root (B2) and foliar (B1) biostimulants treatments, based on humic substances and protein hydrolysates, respectively, applied both alone and in combination (MB1, MB2). MB2 increased the shoot dry weight (DW) yield in hemp (1.9 times more), sorghum (3.6 times more) and miscanthus (tripled) with additional positive effects on sorghum and miscanthus Zn and Cd accumulation, respectively, but no effects on hemp metal accumulation. No treatment promoted switchgrass shoot DW, but M enhanced Cd and Cr shoot concentrations (+84%, 1.6 times more, respectively) and the phytoextraction efficiency. Root biostimulants and mycorrhizae were demonstrated to be more efficient inputs than foliar biostimulants to enhance plant development and productivity in order to design effective phytomanagement strategies in metal-contaminated soil.
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Affiliation(s)
- Pietro Peroni
- Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy
| | - Qiao Liu
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China
| | - Walter Zegada Lizarazu
- Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy
| | - Shuai Xue
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China
| | - Zili Yi
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China
| | - Moritz Von Cossel
- Department of Biobased Resources in the Bioeconomy (340b), Institute of Crop Science, University of Hohenheim, Fruwirthstr 23, 70599 Stuttgart, Germany
| | | | - Eleni G Papazoglou
- Department of Crop Science, Agricultural University of Athens, 11855 Athens, Greece
| | - Andrea Monti
- Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy
| | - Yasir Iqbal
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China
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Geng T, Yu H, Yuan X, Ma R, Li P. Research on Segmentation Method of Maize Seedling Plant Instances Based on UAV Multispectral Remote Sensing Images. PLANTS (BASEL, SWITZERLAND) 2024; 13:1842. [PMID: 38999682 DOI: 10.3390/plants13131842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/14/2024] [Accepted: 07/02/2024] [Indexed: 07/14/2024]
Abstract
The accurate instance segmentation of individual crop plants is crucial for achieving a high-throughput phenotypic analysis of seedlings and smart field management in agriculture. Current crop monitoring techniques employing remote sensing predominantly focus on population analysis, thereby lacking precise estimations for individual plants. This study concentrates on maize, a critical staple crop, and leverages multispectral remote sensing data sourced from unmanned aerial vehicles (UAVs). A large-scale SAM image segmentation model is employed to efficiently annotate maize plant instances, thereby constructing a dataset for maize seedling instance segmentation. The study evaluates the experimental accuracy of six instance segmentation algorithms: Mask R-CNN, Cascade Mask R-CNN, PointRend, YOLOv5, Mask Scoring R-CNN, and YOLOv8, employing various combinations of multispectral bands for a comparative analysis. The experimental findings indicate that the YOLOv8 model exhibits exceptional segmentation accuracy, notably in the NRG band, with bbox_mAP50 and segm_mAP50 accuracies reaching 95.2% and 94%, respectively, surpassing other models. Furthermore, YOLOv8 demonstrates robust performance in generalization experiments, indicating its adaptability across diverse environments and conditions. Additionally, this study simulates and analyzes the impact of different resolutions on the model's segmentation accuracy. The findings reveal that the YOLOv8 model sustains high segmentation accuracy even at reduced resolutions (1.333 cm/px), meeting the phenotypic analysis and field management criteria.
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Affiliation(s)
- Tingting Geng
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
| | - Haiyang Yu
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
- Key Laboratory of Mine Spatio-Temporal Information and Ecological Restoration, Ministry of Natural Resources, Henan Polytechnic University, Jiaozuo 454000, China
| | - Xinru Yuan
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
| | - Ruopu Ma
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
| | - Pengao Li
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
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3
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Ahmad A, Liew AXW, Venturini F, Kalogeras A, Candiani A, Di Benedetto G, Ajibola S, Cartujo P, Romero P, Lykoudi A, De Grandis MM, Xouris C, Lo Bianco R, Doddy I, Elegbede I, D'Urso Labate GF, García del Moral LF, Martos V. AI can empower agriculture for global food security: challenges and prospects in developing nations. Front Artif Intell 2024; 7:1328530. [PMID: 38726306 PMCID: PMC11081032 DOI: 10.3389/frai.2024.1328530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/11/2024] [Indexed: 05/12/2024] Open
Abstract
Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030-Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.
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Affiliation(s)
- Ali Ahmad
- Research Institute for Integrated Coastal Zone Management, Polytechnic University of Valencia, Grau de Gandia, Valencia, Spain
| | | | - Francesca Venturini
- Institute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Winterthur, Switzerland
- TOELT LLC, Dübendorf, Switzerland
| | | | | | | | - Segun Ajibola
- Afridat UG, Bonn, Germany
- NOVA IMS, Universidade Nova de Lisboa, Campus de Campolide, Lisbon, Portugal
| | - Pedro Cartujo
- Department of Electronic and Computer Technology, University of Granada, Granada, Spain
| | - Pablo Romero
- GRANIOT Satellite Technologies S.L, Granada, Spain
| | | | | | - Christos Xouris
- Gaia Robotics Idiotiki Kefalaiouxiki Etaireia, Patras, Greece
| | - Riccardo Lo Bianco
- Department of Agricultural, Food and Forest Sciences, University of Palermo, Viale delle Scienze, Palermo, Italy
| | - Irawan Doddy
- Department of Mechanical Engineering, Universitas Muhammadiyah Pontianak – Universitas, Kalimantan Barat, Indonesia
| | | | | | - Luis F. García del Moral
- Department of Plant Physiology, Institute of Biotechnology, University of Granada, Granada, Spain
| | - Vanessa Martos
- Department of Plant Physiology, Institute of Biotechnology, University of Granada, Granada, Spain
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Zou M, Liu Y, Fu M, Li C, Zhou Z, Meng H, Xing E, Ren Y. Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage. FRONTIERS IN PLANT SCIENCE 2024; 14:1272049. [PMID: 38235191 PMCID: PMC10791996 DOI: 10.3389/fpls.2023.1272049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/08/2023] [Indexed: 01/19/2024]
Abstract
Introduction Leaf area index (LAI) is a critical physiological and biochemical parameter that profoundly affects vegetation growth. Accurately estimating the LAI for winter wheat during jointing stage is particularly important for monitoring wheat growth status and optimizing variable fertilization decisions. Recently, unmanned aerial vehicle (UAV) data and machine/depth learning methods are widely used in crop growth parameter estimation. In traditional methods, vegetation indices (VI) and texture are usually to estimate LAI. Plant Height (PH) unlike them, contains information about the vertical structure of plants, which should be consider. Methods Taking Xixingdian Township, Cangzhou City, Hebei Province, China as the research area in this paper, and four machine learning algorithms, namely, support vector machine(SVM), back propagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGBoost), and two deep learning algorithms, namely, convolutional neural network (CNN) and long short-term memory neural network (LSTM), were applied to estimate LAI of winter wheat at jointing stage by integrating the spectral and texture features as well as the plant height information from UAV multispectral images. Initially, Digital Surface Model (DSM) and Digital Orthophoto Map (DOM) were generated. Subsequently, the PH, VI and texture features were extracted, and the texture indices (TI) was further constructed. The measured LAI on the ground were collected for the same period and calculated its Pearson correlation coefficient with PH, VI and TI to pick the feature variables with high correlation. The VI, TI, PH and fusion were considered as the independent features, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the calibration set and validation set of samples. Results The ability of different inputs and algorithms to estimate winter wheat LAI were evaluated. The results showed that (1) The addition of PH as a feature variable significantly improved the accuracy of the LAI estimation, indicating that wheat plant height played a vital role as a supplementary parameter for LAI inversion modeling based on traditional indices; (2) The combination of texture features, including normalized difference texture indices (NDTI), difference texture indices (DTI), and ratio texture indices (RTI), substantially improved the correlation between texture features and LAI; Furthermore, multi-feature combinations of VI, TI, and PH exhibited superior capability in estimating LAI for winter wheat; (3) Six regression algorithms have achieved high accuracy in estimating LAI, among which the XGBoost algorithm estimated winter wheat LAI with the highest overall accuracy and best results, achieving the highest R2 (R2 = 0.88), the lowest RMSE (RMSE=0.69), and an RPD greater than 2 (RPD=2.54). Discussion This study provided compelling evidence that utilizing XGBoost and integrating spectral, texture, and plant height information extracted from UAV data can accurately monitor LAI during the jointing stage of winter wheat. The research results will provide a new perspective for accurate monitoring of crop parameters through remote sensing.
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Affiliation(s)
- Mengxi Zou
- College of Geomatics, Xi’an University of Science and Technology, Xi’an, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Yu Liu
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Maodong Fu
- Hebei Maodong Xingteng Agricultural Technology Service Co., Ltd, Cangzhou, China
| | - Cunjun Li
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
- Qingyuan Smart Agriculture and Rural Research Institute, Qingyuan, China
| | - Zixiang Zhou
- College of Geomatics, Xi’an University of Science and Technology, Xi’an, China
| | - Haoran Meng
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Enguang Xing
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Yanmin Ren
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
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Koji T, Iwata H, Ishimori M, Takanashi H, Yamasaki Y, Tsujimoto H. Genetic Dissection of Seasonal Changes in a Greening Plant Based on Time-Series Multispectral Imaging. PLANTS (BASEL, SWITZERLAND) 2023; 12:3597. [PMID: 37896060 PMCID: PMC10610531 DOI: 10.3390/plants12203597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/06/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023]
Abstract
Good appearance throughout the year is important for perennial ornamental plants used for rooftop greenery. However, the methods for evaluating appearance throughout the year, such as plant color and growth activity, are not well understood. In this study, evergreen and winter-dormant parents of Phedimus takesimensis and 94 F1 plants were used for multispectral imaging. We took 16 multispectral image measurements from March 2019 to April 2020 and used them to calculate 15 vegetation indices and the area of plant cover. QTL analysis was also performed. Traits such as the area of plant cover and vegetation indices related to biomass were high during spring and summer (growth period), whereas vegetation indices related to anthocyanins were high in winter (dormancy period). According to the PCA, changes in the intensity of light reflected from the plants at different wavelengths over the course of a year were consistent with the changes in plant color and growth activity. Seven QTLs were found to be associated with major seasonal growth changes. This approach, which monitors not only at a single point in time but also over time, can reveal morphological changes during growth, senescence, and dormancy throughout the year.
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Affiliation(s)
- Taeko Koji
- The United Graduate School of Agricultural Sciences, Tottori University, 4-101 Koyamacho Minami, Tottori 680-8553, Japan;
| | - Hiroyoshi Iwata
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan; (H.I.); (M.I.); (H.T.)
| | - Motoyuki Ishimori
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan; (H.I.); (M.I.); (H.T.)
| | - Hideki Takanashi
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan; (H.I.); (M.I.); (H.T.)
| | - Yuji Yamasaki
- Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori 680-0001, Japan;
| | - Hisashi Tsujimoto
- Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori 680-0001, Japan;
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6
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Koji T, Iwata H, Ishimori M, Takanashi H, Yamasaki Y, Tsujimoto H. Multispectral Phenotyping and Genetic Analyses of Spring Appearance in Greening Plant, Phedimus spp. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0063. [PMID: 37383728 PMCID: PMC10292581 DOI: 10.34133/plantphenomics.0063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 06/09/2023] [Indexed: 06/30/2023]
Abstract
The change in appearance during the seasonal transitions in ornamental greening plants is an important characteristic. In particular, the early onset of green leaf color is a desirable trait for a cultivar. In this study, we established a method for phenotyping leaf color change by multispectral imaging and performed genetic analysis based on the phenotypes to clarify the potential of the approach in breeding greening plants. We performed multispectral phenotyping and quantitative trait locus (QTL) analysis of an F1 population derived from 2 parental lines of Phedimus takesimensis, known to be a drought and heat-tolerant rooftop plant species. The imaging was conducted in April of 2019 and 2020 when dormancy breakage occurs and growth extension begins. Principal component analysis of 9 different wavelength values showed a high contribution from the first principal component (PC1), which captured variation in the visible light range. The high interannual correlation in PC1 and in the intensity of visible light indicated that the multispectral phenotyping captured genetic variation in the color of leaves. We also performed restriction site-associated DNA sequencing and obtained the first genetic linkage map of Phedimus spp. QTL analysis revealed 2 QTLs related to early dormancy breakage. Based on the genotypes of the markers underlying these 2 QTLs, the F1 phenotypes with early (late) dormancy break, green (red or brown) leaves, and a high (low) degree of vegetative growth were classified. The results suggest the potential of multispectral phenotyping in the genetic dissection of seasonal leaf color changes in greening plants.
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Affiliation(s)
- Taeko Koji
- The United Graduate School of Agricultural Sciences,
Tottori University, 4-101 Koyamacho minami, Tottori-shi, Tottori 680-8553, Japan
| | - Hiroyoshi Iwata
- Graduate School of Agricultural and Life Sciences,
The University of Tokyo, 1-1-1 Yayoi-chou, Bunkyo, Tokyo 113-8657, Japan
| | - Motoyuki Ishimori
- Graduate School of Agricultural and Life Sciences,
The University of Tokyo, 1-1-1 Yayoi-chou, Bunkyo, Tokyo 113-8657, Japan
| | - Hideki Takanashi
- Graduate School of Agricultural and Life Sciences,
The University of Tokyo, 1-1-1 Yayoi-chou, Bunkyo, Tokyo 113-8657, Japan
| | - Yuji Yamasaki
- Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori-shi, Tottori 680-0001, Japan
| | - Hisashi Tsujimoto
- Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori-shi, Tottori 680-0001, Japan
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7
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Chen Q, Zheng B, Chenu K, Chapman SC. A Generic Model to Estimate Wheat LAI over Growing Season Regardless of the Soil-Type Background. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0055. [PMID: 37234427 PMCID: PMC10205590 DOI: 10.34133/plantphenomics.0055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 04/29/2023] [Indexed: 05/28/2023]
Abstract
It is valuable to develop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data for diverse soil backgrounds without any ground calibration. To achieve this objective, 2 strategies were investigated to improve our existing random forest regression (RFR) model, which was trained with simulations from a radiative transfer model (PROSAIL). The 2 strategies consisted of (a) broadening the reflectance domain of soil background to generate training data and (b) finding an appropriate set of indicators (band reflectance and/or vegetation indices) as inputs of the RFR model. The RFR models were tested in diverse soils representing varying soil types in Australia. Simulation analysis indicated that adopting both strategies resulted in a generic model that can provide accurate estimation for wheat LAI and is resistant to changes in soil background. From validation on 2 years of field trials, this model achieved high prediction accuracy for LAI over the entire crop cycle (LAI up to 7 m2 m-2) (root mean square error (RMSE): 0.23 to 0.89 m2 m-2), including for sparse canopy (LAI less than 0.3 m2 m-2) grown on different soil types (RMSE: 0.02 to 0.25 m2 m-2). The model reliably captured the seasonal pattern of LAI dynamics for different treatments in terms of genotypes, plant densities, and water-nitrogen managements (correlation coefficient: 0.82 to 0.98). With appropriate adaptations, this framework can be adjusted to any type of sensors to estimate various traits for various species (including but not limited to LAI of wheat) in associated disciplines, e.g., crop breeding, precision agriculture, etc.
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Affiliation(s)
- Qiaomin Chen
- School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia
- Agriculture and Food, CSIRO, Queensland Bioscience Precinct, St Lucia, QLD, Australia
| | - Bangyou Zheng
- Agriculture and Food, CSIRO, Queensland Bioscience Precinct, St Lucia, QLD, Australia
| | - Karine Chenu
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Toowoomba, QLD, Australia
| | - Scott C. Chapman
- School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia
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Giovannetti M, Salvioli di Fossalunga A, Stringlis IA, Proietti S, Fiorilli V. Unearthing soil-plant-microbiota crosstalk: Looking back to move forward. FRONTIERS IN PLANT SCIENCE 2023; 13:1082752. [PMID: 36762185 PMCID: PMC9902496 DOI: 10.3389/fpls.2022.1082752] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 12/29/2022] [Indexed: 06/18/2023]
Abstract
The soil is vital for life on Earth and its biodiversity. However, being a non-renewable and threatened resource, preserving soil quality is crucial to maintain a range of ecosystem services critical to ecological balances, food production and human health. In an agricultural context, soil quality is often perceived as the ability to support field production, and thus soil quality and fertility are strictly interconnected. The concept of, as well as the ways to assess, soil fertility has undergone big changes over the years. Crop performance has been historically used as an indicator for soil quality and fertility. Then, analysis of a range of physico-chemical parameters has been used to routinely assess soil quality. Today it is becoming evident that soil quality must be evaluated by combining parameters that refer both to the physico-chemical and the biological levels. However, it can be challenging to find adequate indexes for evaluating soil quality that are both predictive and easy to measure in situ. An ideal soil quality assessment method should be flexible, sensitive enough to detect changes in soil functions, management and climate, and should allow comparability among sites. In this review, we discuss the current status of soil quality indicators and existing databases of harmonized, open-access topsoil data. We also explore the connections between soil biotic and abiotic features and crop performance in an agricultural context. Finally, based on current knowledge and technical advancements, we argue that the use of plant health traits represents a powerful way to assess soil physico-chemical and biological properties. These plant health parameters can serve as proxies for different soil features that characterize soil quality both at the physico-chemical and at the microbiological level, including soil quality, fertility and composition of soil microbial communities.
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Affiliation(s)
- Marco Giovannetti
- Department of Biology, University of Padova, Padova, Italy
- Department of Life Sciences and Systems Biology, University of Turin, Turin, Italy
| | | | - Ioannis A. Stringlis
- Plant - Microbe Interactions, Department of Biology, Science4Life, Utrecht University, Utrecht, Netherlands
| | - Silvia Proietti
- Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, Italy
| | - Valentina Fiorilli
- Department of Life Sciences and Systems Biology, University of Turin, Turin, Italy
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9
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Bai D, Li D, Zhao C, Wang Z, Shao M, Guo B, Liu Y, Wang Q, Li J, Guo S, Wang R, Li YH, Qiu LJ, Jin X. Estimation of soybean yield parameters under lodging conditions using RGB information from unmanned aerial vehicles. FRONTIERS IN PLANT SCIENCE 2022; 13:1012293. [PMID: 36589058 PMCID: PMC9795850 DOI: 10.3389/fpls.2022.1012293] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 11/28/2022] [Indexed: 06/15/2023]
Abstract
The estimation of yield parameters based on early data is helpful for agricultural policymakers and food security. Developments in unmanned aerial vehicle (UAV) platforms and sensor technology help to estimate yields efficiency. Previous studies have been based on less cultivars (<10) and ideal experimental environments, it is not available in practical production. Therefore, the objective of this study was to estimate the yield parameters of soybean (Glycine max (L.) Merr.) under lodging conditions using RGB information. In this study, 17 time point data throughout the soybean growing season in Nanchang, Jiangxi Province, China, were collected, and the vegetation index, texture information, canopy cover, and crop height were obtained by UAV-image processing. After that, partial least squares regression (PLSR), logistic regression (Logistic), random forest regression (RFR), support vector machine regression (SVM), and deep learning neural network (DNN) were used to estimate the yield parameters. The results can be summarized as follows: (1) The most suitable time point to estimate the yield was flowering stage (48 days), which was when most of the soybean cultivars flowered. (2) The multiple data fusion improved the accuracy of estimating the yield parameters, and the texture information has a high potential to contribute to the estimation of yields, and (3) The DNN model showed the best accuracy of training (R2=0.66 rRMSE=32.62%) and validation (R2=0.50, rRMSE=43.71%) datasets. In conclusion, these results provide insights into both best estimate period selection and early yield estimation under lodging condition when using remote sensing.
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Affiliation(s)
- Dong Bai
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Delin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chaosen Zhao
- Nanchang Branch of National Center of Oil Crops Improvement, Jiangxi Province Key Laboratory of Oil Crops Biology, Crops Research Institute of Jiangxi Academy of Agricultural Sciences, Nanchang, China
| | - Zixu Wang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mingchao Shao
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Bingfu Guo
- Nanchang Branch of National Center of Oil Crops Improvement, Jiangxi Province Key Laboratory of Oil Crops Biology, Crops Research Institute of Jiangxi Academy of Agricultural Sciences, Nanchang, China
| | - Yadong Liu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Qi Wang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
- College of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jindong Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, China
| | - Shiyu Guo
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
- College of Agriculture, Northeast Agricultural University, Harbin, China
| | - Ruizhen Wang
- Nanchang Branch of National Center of Oil Crops Improvement, Jiangxi Province Key Laboratory of Oil Crops Biology, Crops Research Institute of Jiangxi Academy of Agricultural Sciences, Nanchang, China
| | - Ying-hui Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Li-juan Qiu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiuliang Jin
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, China
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10
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Ma X, Zhu X, Xie Q, Jin J, Zhou Y, Luo Y, Liu Y, Tian J, Zhao Y. Monitoring nature's calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications. GLOBAL CHANGE BIOLOGY 2022; 28:7186-7204. [PMID: 36114727 PMCID: PMC9827868 DOI: 10.1111/gcb.16436] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/09/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Abstract
Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant-climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.
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Affiliation(s)
- Xuanlong Ma
- College of Earth and Environmental Sciences, Lanzhou UniversityLanzhouChina
| | - Xiaolin Zhu
- Department of Land Surveying and Geo‐InformaticsThe Hong Kong Polytechnic UniversityHong KongChina
| | - Qiaoyun Xie
- School of Life Sciences, Faculty of ScienceUniversity of Technology SydneySydneyNew South WalesAustralia
| | - Jiaxin Jin
- College of Hydrology and Water Resources, Hohai UniversityNanjingChina
| | - Yuke Zhou
- Key Laboratory of Ecosystem Network Observation and ModellingInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesBeijingChina
| | - Yunpeng Luo
- Swiss Federal Institute for Forest, Snow and Landscape Research WSLBirmensdorfSwitzerland
- Department of Environmental System ScienceETH ZurichZurichSwitzerland
| | - Yuxia Liu
- School of Life Sciences, Faculty of ScienceUniversity of Technology SydneySydneyNew South WalesAustralia
- Geospatial Sciences Center of Excellence (GSCE)South Dakota State UniversityBrookingsSouth DakotaUSA
| | - Jiaqi Tian
- Department of Land Surveying and Geo‐InformaticsThe Hong Kong Polytechnic UniversityHong KongChina
- Department of GeographyNational University of SingaporeSingaporeSingapore
| | - Yuhe Zhao
- College of Earth and Environmental Sciences, Lanzhou UniversityLanzhouChina
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11
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Chen Q, Zheng B, Chenu K, Hu P, Chapman SC. Unsupervised Plot-Scale LAI Phenotyping via UAV-Based Imaging, Modelling, and Machine Learning. PLANT PHENOMICS 2022; 2022:9768253. [PMID: 35935677 PMCID: PMC9317541 DOI: 10.34133/2022/9768253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/25/2022] [Indexed: 11/14/2022]
Abstract
High-throughput phenotyping has become the frontier to accelerate breeding through linking genetics to crop growth estimation, which requires accurate estimation of leaf area index (LAI). This study developed a hybrid method to train the random forest regression (RFR) models with synthetic datasets generated by a radiative transfer model to estimate LAI from UAV-based multispectral images. The RFR models were evaluated on both (i) subsets from the synthetic datasets and (ii) observed data from two field experiments (i.e., Exp16, Exp19). Given the parameter ranges and soil reflectance are well calibrated in synthetic training data, RFR models can accurately predict LAI from canopy reflectance captured in field conditions, with systematic overestimation for LAI<2 due to background effect, which can be addressed by applying background correction on original reflectance map based on vegetation-background classification. Overall, RFR models achieved accurate LAI prediction from background-corrected reflectance for Exp16 (correlation coefficient (r) of 0.95, determination coefficient (R2) of 0.90~0.91, root mean squared error (RMSE) of 0.36~0.40 m2 m−2, relative root mean squared error (RRMSE) of 25~28%) and less accurate for Exp19 (r =0.80~0.83, R2 = 0.63~0.69, RMSE of 0.84~0.86 m2 m−2, RRMSE of 30~31%). Additionally, RFR models correctly captured spatiotemporal variation of observed LAI as well as identified variations for different growing stages and treatments in terms of genotypes and management practices (i.e., planting density, irrigation, and fertilization) for two experiments. The developed hybrid method allows rapid, accurate, nondestructive phenotyping of the dynamics of LAI during vegetative growth to facilitate assessments of growth rate including in breeding program assessments.
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Affiliation(s)
- Qiaomin Chen
- School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia
- Agriculture and Food, CSIRO, Queensland Bioscience Precinct, St Lucia, QLD, Australia
| | - Bangyou Zheng
- Agriculture and Food, CSIRO, Queensland Bioscience Precinct, St Lucia, QLD, Australia
| | - Karine Chenu
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Toowoomba, QLD, Australia
| | - Pengcheng Hu
- School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia
- Agriculture and Food, CSIRO, Queensland Bioscience Precinct, St Lucia, QLD, Australia
| | - Scott C. Chapman
- School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia
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12
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UAV Remote Sensing for High-Throughput Phenotyping and for Yield Prediction of Miscanthus by Machine Learning Techniques. REMOTE SENSING 2022. [DOI: 10.3390/rs14122927] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. The random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass, and standing biomass) using 15 VIs time series, and predicted yield using peak descriptors derived from these VIs time series with root mean square error of 2.3 Mg DM ha−1. The study demonstrates the potential of UAVs’ multispectral images in HTP applications and in yield prediction, providing important information needed to increase sustainable biomass production.
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13
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Szabó A, Mousavi SMN, Bojtor C, Ragán P, Nagy J, Vad A, Illés Á. Analysis of Nutrient-Specific Response of Maize Hybrids in Relation to Leaf Area Index (LAI) and Remote Sensing. PLANTS (BASEL, SWITZERLAND) 2022; 11:1197. [PMID: 35567198 PMCID: PMC9102345 DOI: 10.3390/plants11091197] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 04/23/2022] [Accepted: 04/26/2022] [Indexed: 06/15/2023]
Abstract
Leaf area index (LAI) indicates the leaf area per ground surface area occupied by a crop. Various methods are used to measure LAI, which is unitless and varies according to species and environmental conditions. This experiment was carried out in three different nitrogen ranges (control, 120 kg N ha-1, and 300 kg N ha-1) + PK nutrient levels, with five replications used for leaf area measurement on seven different maize hybrids. Hybrids had different moisture, protein, oil, and starch contents. N (1, 2) + PK treatments had a desirable effect on protein, starch, and yield. P0217 LAI had a minimal response at these fertiliser levels. LAI for Sushi peaked at different dates between control and fertiliser treatments. This result showed that Sushi has an excellent capacity for LAI. LAI values on 15 June 2020 showed minimum average values for all hybrids, and it had a maximum average values on 23 July 2020. LAI had maximum performance between the average values treatments in Sushi, Armagnac, Loupiac, and DKC4792 on 15 June 2020. This study also provides insights for examining variably applied N doses using crop sensors and UAV remote-sensing platforms.
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Affiliation(s)
- Atala Szabó
- Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Land Use, Engineering and Precision Farming Technology, University of Debrecen, 138 Böszörményi St., H-4032 Debrecen, Hungary; (A.S.); (S.M.N.M.); (P.R.); (J.N.); (Á.I.)
| | - Seyed Mohammad Nasir Mousavi
- Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Land Use, Engineering and Precision Farming Technology, University of Debrecen, 138 Böszörményi St., H-4032 Debrecen, Hungary; (A.S.); (S.M.N.M.); (P.R.); (J.N.); (Á.I.)
| | - Csaba Bojtor
- Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Land Use, Engineering and Precision Farming Technology, University of Debrecen, 138 Böszörményi St., H-4032 Debrecen, Hungary; (A.S.); (S.M.N.M.); (P.R.); (J.N.); (Á.I.)
| | - Péter Ragán
- Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Land Use, Engineering and Precision Farming Technology, University of Debrecen, 138 Böszörményi St., H-4032 Debrecen, Hungary; (A.S.); (S.M.N.M.); (P.R.); (J.N.); (Á.I.)
| | - János Nagy
- Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Land Use, Engineering and Precision Farming Technology, University of Debrecen, 138 Böszörményi St., H-4032 Debrecen, Hungary; (A.S.); (S.M.N.M.); (P.R.); (J.N.); (Á.I.)
| | - Attila Vad
- Institutes for Agricultural Research and Educational Farm (IAREF), Farm and Regional Research Institutes of Debrecen (RID), Experimental Station of Látókép, University of Debrecen, H-4032 Debrecen, Hungary;
| | - Árpád Illés
- Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Land Use, Engineering and Precision Farming Technology, University of Debrecen, 138 Böszörményi St., H-4032 Debrecen, Hungary; (A.S.); (S.M.N.M.); (P.R.); (J.N.); (Á.I.)
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14
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The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing. REMOTE SENSING 2022. [DOI: 10.3390/rs14071559] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Unmanned aerial vehicle (UAV)-based multispectral remote sensing effectively monitors agro-ecosystem functioning and predicts crop yield. However, the timing of the remote sensing field campaigns can profoundly impact the accuracy of yield predictions. Little is known on the effects of phenological phases on skills of high-frequency sensing observations used to predict maize yield. It is also unclear how much improvement can be gained using multi-temporal compared to mono-temporal data. We used a systematic scheme to address those gaps employing UAV multispectral observations at nine development stages of maize (from second-leaf to maturity). Next, the spectral and texture indices calculated from the mono-temporal and multi-temporal UAV images were fed into the Random Forest model for yield prediction. Our results indicated that multi-temporal UAV data could remarkably enhance the yield prediction accuracy compared with mono-temporal UAV data (R2 increased by 8.1% and RMSE decreased by 27.4%). For single temporal UAV observation, the fourteenth-leaf stage was the earliest suitable time and the milking stage was the optimal observing time to estimate grain yield. For multi-temporal UAV data, the combination of tasseling, silking, milking, and dough stages exhibited the highest yield prediction accuracy (R2 = 0.93, RMSE = 0.77 t·ha−1). Furthermore, we found that the Normalized Difference Red Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), and dissimilarity of the near-infrared image at milking stage were the most promising feature variables for maize yield prediction.
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15
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Ostmeyer TJ, Bahuguna RN, Kirkham MB, Bean S, Jagadish SVK. Enhancing Sorghum Yield Through Efficient Use of Nitrogen - Challenges and Opportunities. FRONTIERS IN PLANT SCIENCE 2022; 13:845443. [PMID: 35295626 PMCID: PMC8919068 DOI: 10.3389/fpls.2022.845443] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
Sorghum is an important crop, which is widely used as food, forage, fodder and biofuel. Despite its natural adaption to resource-poor and stressful environments, increasing yield potential of sorghum under more favorable conditions holds promise. Nitrogen is the most important nutrient for crops, having a dynamic impact on all growth, yield, and grain-quality-determining processes. Thus, increasing nitrogen use efficiency (NUE) in sorghum would provide opportunities to achieve higher yield and better-quality grain. NUE is a complex trait, which is regulated by several genes. Hence, exploring genetic diversity for NUE can help to develop molecular markers associated with NUE, which can be utilized to develop high NUE sorghum genotypes with greater yield potential. Research on improving NUE in sorghum suggests that, under water-deficit conditions, traits such as stay-green and altered canopy architecture, and under favorable conditions, traits such as an optimized stay-green and senescence ratio and efficient N translocation to grain, are potential breeding targets to develop high NUE sorghum genotypes. Hence, under a wide range of environments, sorghum breeding programs will need to reconsider strategies and develop breeding programs based on environment-specific trait(s) for better adaptation and improvement in productivity and grain quality. Unprecedented progress in sensor-based technology and artificial intelligence in high-throughput phenotyping has provided new horizons to explore complex traits in situ, such as NUE. A better understanding of the genetics and molecular pathways involving NUE, accompanied by targeted high-throughput sensor-based indices, is critical for identifying lines or developing management practices to enhance NUE in sorghum.
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Affiliation(s)
- Troy J. Ostmeyer
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Rajeev Nayan Bahuguna
- Center for Advanced Studies on Climate Change, Dr. Rajendra Prasad Central Agricultural University, Samastipur, India
| | - M. B. Kirkham
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Scott Bean
- Grain Quality and Structure Research Unit, CGAHR, USDA-ARS, Manhattan, KS, United States
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16
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Zhi X, Massey-Reed SR, Wu A, Potgieter A, Borrell A, Hunt C, Jordan D, Zhao Y, Chapman S, Hammer G, George-Jaeggli B. Estimating Photosynthetic Attributes from High-Throughput Canopy Hyperspectral Sensing in Sorghum. PLANT PHENOMICS (WASHINGTON, D.C.) 2022; 2022:9768502. [PMID: 35498954 PMCID: PMC9013486 DOI: 10.34133/2022/9768502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 02/25/2022] [Indexed: 05/04/2023]
Abstract
Sorghum, a genetically diverse C4 cereal, is an ideal model to study natural variation in photosynthetic capacity. Specific leaf nitrogen (SLN) and leaf mass per leaf area (LMA), as well as, maximal rates of Rubisco carboxylation (V cmax), phosphoenolpyruvate (PEP) carboxylation (V pmax), and electron transport (J max), quantified using a C4 photosynthesis model, were evaluated in two field-grown training sets (n = 169 plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict V cmax (R 2 = 0.83), V pmax (R 2 = 0.93), J max (R 2 = 0.76), SLN (R 2 = 0.82), and LMA (R 2 = 0.68) from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for V cmax, V pmax, J max, SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 (n = 875 plots including 650 genotypes) and 2020 (n = 912 plots with 634 genotypes). The predicted traits showed medium to high heritability and genome-wide association studies using the predicted values identified four QTL for V cmax and two QTL for J max. Candidate genes within 200 kb of the V cmax QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se. J max QTL was enriched for candidate genes involved in electron transport. These outcomes suggest the methods here are of great promise to effectively screen large germplasm collections for enhanced photosynthetic capacity.
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Affiliation(s)
- Xiaoyu Zhi
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
| | - Sean Reynolds Massey-Reed
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
| | - Alex Wu
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St Lucia, QLD, Australia
| | - Andries Potgieter
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia
| | - Andrew Borrell
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
| | - Colleen Hunt
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
- Agri-Science Queensland, Department of Agriculture and Fisheries (DAF), Hermitage Research Facility, Warwick, QLD, Australia
| | - David Jordan
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
| | - Yan Zhao
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St Lucia, QLD, Australia
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia
| | - Scott Chapman
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St Lucia, QLD, Australia
- School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD, Australia
| | - Graeme Hammer
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St Lucia, QLD, Australia
| | - Barbara George-Jaeggli
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
- Agri-Science Queensland, Department of Agriculture and Fisheries (DAF), Hermitage Research Facility, Warwick, QLD, Australia
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17
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Morota G, Jarquin D, Campbell MT, Iwata H. Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data. Methods Mol Biol 2022; 2539:269-296. [PMID: 35895210 DOI: 10.1007/978-1-0716-2537-8_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.
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Affiliation(s)
- Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL, USA
| | - Malachy T Campbell
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
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18
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Xu Y, Shrestha V, Piasecki C, Wolfe B, Hamilton L, Millwood RJ, Mazarei M, Stewart CN. Sustainability Trait Modeling of Field-Grown Switchgrass ( Panicum virgatum) Using UAV-Based Imagery. PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10122726. [PMID: 34961199 PMCID: PMC8709265 DOI: 10.3390/plants10122726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 11/29/2021] [Accepted: 12/07/2021] [Indexed: 05/17/2023]
Abstract
Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.
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Affiliation(s)
- Yaping Xu
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Vivek Shrestha
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Cristiano Piasecki
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- ATSI Brasil Pesquisa e Consultoria, Passo Fundo 99054-328, RS, Brazil
| | - Benjamin Wolfe
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Lance Hamilton
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Reginald J. Millwood
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- Correspondence: (R.J.M.); (M.M.); (C.N.S.J.)
| | - Mitra Mazarei
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- Correspondence: (R.J.M.); (M.M.); (C.N.S.J.)
| | - Charles Neal Stewart
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- Correspondence: (R.J.M.); (M.M.); (C.N.S.J.)
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19
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Wang J, Li X, Guo T, Dzievit MJ, Yu X, Liu P, Price KP, Yu J. Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping. THE PLANT GENOME 2021; 14:e20155. [PMID: 34596348 DOI: 10.1002/tpg2.20155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 08/12/2021] [Indexed: 06/13/2023]
Abstract
Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at five time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into two groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.
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Affiliation(s)
- Jinyu Wang
- Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA
| | - Xianran Li
- Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA
| | - Tingting Guo
- Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA
| | | | - Xiaoqing Yu
- Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA
| | - Peng Liu
- Dep. of Statistics, Iowa State Univ., Ames, IA, 50011, USA
| | | | - Jianming Yu
- Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA
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20
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Zhao Y, Zheng B, Chapman SC, Laws K, George-Jaeggli B, Hammer GL, Jordan DR, Potgieter AB. Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9874650. [PMID: 34676373 PMCID: PMC8502246 DOI: 10.34133/2021/9874650] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 08/31/2021] [Indexed: 06/03/2023]
Abstract
In plant breeding, unmanned aerial vehicles (UAVs) carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping (HTP) to aid the interpretation of genotype and environment effects on morphological, biochemical, and physiological traits. A key constraint remains the reduced resolution and quality extracted from "stitched" mosaics generated from UAV missions across large areas. This can be addressed by generating high-quality reflectance data from a single nadir image per plot. In this study, a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications. Sequential steps involved (i) imagery calibration, (ii) spectral band alignment, (iii) backward calculation, (iv) plot segmentation, and (v) application. Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot. Using a derived nadir image of each plot, the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads, respectively. Furthermore, the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours (i.e., red and white). Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches.
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Affiliation(s)
- Yan Zhao
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
| | - Bangyou Zheng
- CSIRO Agriculture and Food, St. Lucia, Queensland 4072, Australia
| | - Scott C. Chapman
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
- The University of Queensland, School of Agriculture and Food Sciences, St. Lucia, Queensland 4072, Australia
| | - Kenneth Laws
- Department of Agriculture and Fisheries, Agri-Science Queensland, Warwick, Queensland 4370, Australia
| | - Barbara George-Jaeggli
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
- Department of Agriculture and Fisheries, Agri-Science Queensland, Warwick, Queensland 4370, Australia
| | - Graeme L. Hammer
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
| | - David R. Jordan
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
- Department of Agriculture and Fisheries, Agri-Science Queensland, Warwick, Queensland 4370, Australia
| | - Andries B. Potgieter
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
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21
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Smith DT, Potgieter AB, Chapman SC. Scaling up high-throughput phenotyping for abiotic stress selection in the field. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1845-1866. [PMID: 34076731 DOI: 10.1007/s00122-021-03864-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/13/2021] [Indexed: 05/18/2023]
Abstract
High-throughput phenotyping (HTP) is in its infancy for deployment in large-scale breeding programmes. With the ability to measure correlated traits associated with physiological ideotypes, in-field phenotyping methods are available for screening of abiotic stress responses. As cropping environments become more hostile and unpredictable due to the effects of climate change, the need to characterise variability across spatial and temporal scales will become increasingly important. The sensor technologies that have enabled HTP from macroscopic through to satellite sensors may also be utilised here to complement spatial characterisation using envirotyping, which can improve estimations of genotypic performance across environments by better accounting for variation at the plot, trial and inter-trial levels. Climate change is leading to increased variation at all physical and temporal scales in the cropping environment. Maintaining yield stability under circumstances with greater levels of abiotic stress while capitalising upon yield potential in good years, requires approaches to plant breeding that target the physiological limitations to crop performance in specific environments. This requires dynamic modelling of conditions within target populations of environments, GxExM predictions, clustering of environments so breeding trajectories can be defined, and the development of screens that enable selection for genetic gain to occur. High-throughput phenotyping (HTP), combined with related technologies used for envirotyping, can help to address these challenges. Non-destructive analysis of the morphological, biochemical and physiological qualities of plant canopies using HTP has great potential to complement whole-genome selection, which is becoming increasingly common in breeding programmes. A range of novel analytic techniques, such as machine learning and deep learning, combined with a widening range of sensors, allow rapid assessment of large breeding populations that are repeatable and objective. Secondary traits underlying radiation use efficiency and water use efficiency can be screened with HTP for selection at the early stages of a breeding programme. HTP and envirotyping technologies can also characterise spatial variability at trial and within-plot levels, which can be used to correct for spatial variations that confound measurements of genotypic values. This review explores HTP for abiotic stress selection through a physiological trait lens and additionally investigates the use of envirotyping and EC to characterise spatial variability at all physical scales in METs.
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Affiliation(s)
- Daniel T Smith
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Andries B Potgieter
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Scott C Chapman
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia.
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22
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Deery DM, Smith DJ, Davy R, Jimenez-Berni JA, Rebetzke GJ, James RA. Impact of Varying Light and Dew on Ground Cover Estimates from Active NDVI, RGB, and LiDAR. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9842178. [PMID: 34250506 PMCID: PMC8240513 DOI: 10.34133/2021/9842178] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 04/29/2021] [Indexed: 05/29/2023]
Abstract
Canopy ground cover (GC) is an important agronomic measure for evaluating crop establishment and early growth. This study evaluates the reliability of GC estimates, in the presence of varying light and dew on leaves, from three different ground-based sensors: (1) normalized difference vegetation index (NDVI) from the commercially available GreenSeeker®; (2) RGB images from a digital camera, where GC was determined as the portion of pixels from each image meeting a greenness criterion (i.e., (Green - Red)/(Green + Red) > 0); and (3) LiDAR using two separate approaches: (a) GC from LiDAR red reflectance (whereby red reflectance less than five was classified as vegetation) and (b) GC from LiDAR height (whereby height greater than 10 cm was classified as vegetation). Hourly measurements were made early in the season at two different growth stages (tillering and stem elongation), among wheat genotypes highly diverse for canopy characteristics. The active NDVI showed the least variation through time and was particularly stable, regardless of the available light or the presence of dew. In addition, between-sample-time Pearson correlations for NDVI were consistently high and significant (P < 0.0001), ranging from 0.89 to 0.98. In comparison, GC from LiDAR and RGB showed greater variation across sampling times, and LiDAR red reflectance was strongly influenced by the presence of dew. Excluding times when the light was exceedingly low, correlations between GC from RGB and NDVI were consistently high (ranging from 0.79 to 0.92). The high reliability of the active NDVI sensor potentially affords a high degree of flexibility for users by enabling sampling across a broad range of acceptable light conditions.
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Affiliation(s)
| | | | - Robert Davy
- CSIRO Information Management and Technology, Canberra, ACT, Australia
| | - Jose A. Jimenez-Berni
- CSIRO Agriculture and Food, Canberra, ACT, Australia
- Instituto Agricultura Sostenible, Consejo Superior de Investigaciones Cientificas, Cordoba, Spain
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23
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Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning. REMOTE SENSING 2021. [DOI: 10.3390/rs13091763] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.
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24
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Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping. REMOTE SENSING 2021. [DOI: 10.3390/rs13061144] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.
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25
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Shu M, Shen M, Zuo J, Yin P, Wang M, Xie Z, Tang J, Wang R, Li B, Yang X, Ma Y. The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9890745. [PMID: 33889850 PMCID: PMC8054988 DOI: 10.34133/2021/9890745] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 03/19/2021] [Indexed: 05/19/2023]
Abstract
Crop traits such as aboveground biomass (AGB), total leaf area (TLA), leaf chlorophyll content (LCC), and thousand kernel weight (TWK) are important indices in maize breeding. How to extract multiple crop traits at the same time is helpful to improve the efficiency of breeding. Compared with digital and multispectral images, the advantages of high spatial and spectral resolution of hyperspectral images derived from unmanned aerial vehicle (UAV) are expected to accurately estimate the similar traits among breeding materials. This study is aimed at exploring the feasibility of estimating AGB, TLA, SPAD value, and TWK using UAV hyperspectral images and at determining the optimal models for facilitating the process of selecting advanced varieties. The successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to screen sensitive bands for the maize traits. Partial least squares (PLS) and random forest (RF) algorithms were used to estimate the maize traits. The results can be summarized as follows: The sensitive bands for various traits were mainly concentrated in the near-red and red-edge regions. The sensitive bands screened by CARS were more abundant than those screened by SPA. For AGB, TLA, and SPAD value, the optimal combination was the CARS-PLS method. Regarding the TWK, the optimal combination was the CARS-RF method. Compared with the model built by RF, the model built by PLS was more stable. This study provides guiding significance and practical value for main trait estimation of maize inbred lines by UAV hyperspectral images at the plot level.
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Affiliation(s)
- Meiyan Shu
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Mengyuan Shen
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jinyu Zuo
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Pengfei Yin
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China
| | - Min Wang
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China
| | - Ziwen Xie
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jihua Tang
- College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Ruili Wang
- Agricultural Artificial Intelligence and Crop Phenotype Engineering Research Center, Inner Mongolia Institute of Biotechnology, Huhhot 010070, China
| | - Baoguo Li
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Xiaohong Yang
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
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26
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Li B, Chen L, Sun W, Wu D, Wang M, Yu Y, Chen G, Yang W, Lin Z, Zhang X, Duan L, Yang X. Phenomics-based GWAS analysis reveals the genetic architecture for drought resistance in cotton. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:2533-2544. [PMID: 32558152 PMCID: PMC7680548 DOI: 10.1111/pbi.13431] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 02/13/2020] [Accepted: 06/05/2020] [Indexed: 05/08/2023]
Abstract
Drought resistance (DR) is a complex trait that is regulated by a variety of genes. Without comprehensive profiling of DR-related traits, the knowledge of the genetic architecture for DR in cotton remains limited. Thus, there is a need to bridge the gap between genomics and phenomics. In this study, an automatic phenotyping platform (APP) was systematically applied to examine 119 image-based digital traits (i-traits) during drought stress at the seedling stage, across a natural population of 200 representative upland cotton accessions. Some novel i-traits, as well as some traditional i-traits, were used to evaluate the DR in cotton. The phenomics data allowed us to identify 390 genetic loci by genome-wide association study (GWAS) using 56 morphological and 63 texture i-traits. DR-related genes, including GhRD2, GhNAC4, GhHAT22 and GhDREB2, were identified as candidate genes by some digital traits. Further analysis of candidate genes showed that Gh_A04G0377 and Gh_A04G0378 functioned as negative regulators for cotton drought response. Based on the combined digital phenotyping, GWAS analysis and transcriptome data, we conclude that the phenomics dataset provides an excellent resource to characterize key genetic loci with an unprecedented resolution which can inform future genome-based breeding for improved DR in cotton.
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Affiliation(s)
- Baoqi Li
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanHubeiChina
| | - Lin Chen
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanHubeiChina
| | - Weinan Sun
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanHubeiChina
| | - Di Wu
- Hubei Key Laboratory of Agricultural BioinformaticsHuazhong Agricultural UniversityWuhanHubeiChina
- College of EngineeringHuazhong Agricultural UniversityWuhanHubeiChina
| | - Maojun Wang
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanHubeiChina
| | - Yu Yu
- Cotton InstituteXinjiang Academy of Agriculture and Reclamation ScienceShiheziXinjiangChina
| | - Guoxing Chen
- MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze RiverHuazhong Agricultural UniversityWuhanHubeiChina
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanHubeiChina
- Hubei Key Laboratory of Agricultural BioinformaticsHuazhong Agricultural UniversityWuhanHubeiChina
| | - Zhongxu Lin
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanHubeiChina
| | - Xianlong Zhang
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanHubeiChina
| | - Lingfeng Duan
- Hubei Key Laboratory of Agricultural BioinformaticsHuazhong Agricultural UniversityWuhanHubeiChina
- College of EngineeringHuazhong Agricultural UniversityWuhanHubeiChina
| | - Xiyan Yang
- National Key Laboratory of Crop Genetic ImprovementNational Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanHubeiChina
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27
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Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data. REMOTE SENSING 2020. [DOI: 10.3390/rs12213587] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.
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28
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Librán-Embid F, Klaus F, Tscharntke T, Grass I. Unmanned aerial vehicles for biodiversity-friendly agricultural landscapes - A systematic review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 732:139204. [PMID: 32438190 DOI: 10.1016/j.scitotenv.2020.139204] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 04/28/2020] [Accepted: 05/02/2020] [Indexed: 06/11/2023]
Abstract
The development of biodiversity-friendly agricultural landscapes is of major importance to meet the sustainable development challenges of our time. The emergence of unmanned aerial vehicles (UAVs), i.e. drones, has opened a new set of research and management opportunities to achieve this goal. On the one hand, this review summarizes UAV applications in agricultural landscapes, focusing on biodiversity conservation and agricultural land monitoring, based on a systematic review of the literature that resulted in 550 studies. Additionally, the review proposes how to integrate UAV research in these fields and point to new potential applications that may contribute to biodiversity-friendly agricultural landscapes. UAV-based imagery can be used to identify and monitor plants, floral resources and animals, facilitating the detection of quality habitats with high prediction power. Through vegetation indices derived from their sensors, UAVs can estimate biomass, monitor crop plant health and stress, detect pest or pathogen infestations, monitor soil fertility and target patches of high weed or invasive plant pressure, allowing precise management practices and reduced agrochemical input. Thereby, UAVs are helping to design biodiversity-friendly agricultural landscapes and to mitigate yield-biodiversity trade-offs. In conclusion, UAV applications have become a major means of biodiversity conservation and biodiversity-friendly management in agriculture, while latest developments, such as the miniaturization and decreasing costs of hyperspectral sensors, promise many new applications for the future.
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Affiliation(s)
| | - Felix Klaus
- Agroecology, University of Göttingen, D-37077 Göttingen, Germany
| | - Teja Tscharntke
- Agroecology, University of Göttingen, D-37077 Göttingen, Germany
| | - Ingo Grass
- Department of Ecology of Tropical Agricultural Systems, University of Hohenheim, D-70599 Stuttgart, Germany
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29
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Tucker R, Callaham JA, Zeidler C, Paul AL, Ferl RJ. NDVI imaging within space exploration plant growth modules - A case study from EDEN ISS Antarctica. LIFE SCIENCES IN SPACE RESEARCH 2020; 26:1-9. [PMID: 32718674 DOI: 10.1016/j.lssr.2020.03.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 03/07/2020] [Indexed: 06/11/2023]
Abstract
The concept of using informative wavelength imagery to monitor plant health and ecosystem stability from space is derived from the deployment of Landsat and the development of the Normalized Difference Vegetative Index, or NDVI. NDVI presents the relative reflectance of the Near IR from plant leaves as a measure of relative plant health in terrestrial habitats and landscapes. However, the use of NDVI and NDVI-like imagery is rapidly evolving toward higher spatial resolution and more localized assessments of plant health, such as the use of drone imagery to monitor outdoor farms, and the use of mounted cameras within indoor growing facilities. With the advancement of plant growth systems in support of human space exploration, especially to the moon and Mars, remote assessment of plant health within exploration habitats becomes a critical element for development. This project examines the deployment of NDVI-like capabilities within a planetary analog greenhouse on the Antarctic ice shelf. The EDEN ISS Antarctica project provides a case study on the practical use of specific wavelength imagery to monitor plant health within space exploration environments. GoPro cameras, modified to dual bandpass capabilities, provided Single Image NDVI analyses for a year within the EDEN ISS Future Exploration Greenhouse at the Neumayer Station III in Antarctica. Images were acquired on site, analyzed remotely, and archived for the entire duration of the deployment through a combination of back-room science activities and operational communications with the Neumayer Station III. The results provide insights into the potential use of specific imaging wavelengths to enhance crop production in space exploration.
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Affiliation(s)
- Rachel Tucker
- Horticultural Sciences, University of Florida, Gainesville, FL, USA
| | | | - Conrad Zeidler
- EDEN Research Group, Institute of Space Systems, Department of System Analysis Space Segment, German Aerospace Center (DLR), Bremen, Germany
| | - Anna-Lisa Paul
- Horticultural Sciences, University of Florida, Gainesville, FL, USA; Program in Plant Molecular and Cellular Biology, University of Florida, Gainesville, FL, USA; Interdisiplinary Center for Biotechnology and Research, University of Florida, Gainesville, FL, USA
| | - Robert J Ferl
- Horticultural Sciences, University of Florida, Gainesville, FL, USA; Program in Plant Molecular and Cellular Biology, University of Florida, Gainesville, FL, USA; Office of Research, University of Florida, Gainesville, FL, USA.
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30
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Combining Genetic Analysis and Multivariate Modeling to Evaluate Spectral Reflectance Indices as Indirect Selection Tools in Wheat Breeding under Water Deficit Stress Conditions. REMOTE SENSING 2020. [DOI: 10.3390/rs12091480] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Progress in high-throughput tools has enabled plant breeders to increase the rate of genetic gain through multidimensional assessment of previously intractable traits in a fast and nondestructive manner. This study investigates the potential use of spectral reflectance indices (SRIs; 15 vegetation-SRIs; 15 water-SRIs) as alternative selection tools for destructively measured traits in wheat breeding programs. The genetic variability, heritability (h2), genetic gain (GG), and expected genetic advances (GA) of these indices were compared with those of destructively measured traits in 43 F7-8 recombinant inbred lines (RILs) grown under limited water conditions. The performance of SRIs to estimate the destructively measured traits directly was also evaluated using the partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) models. Most vegetation-SRIs exhibited high genotypic variation, similar to the measured traits, and phenotypic correlations with these traits, compared with the water-SRIs. Most vegetation-SRIs presented comparable values for h2 (>60%) and GG (>20%) as intermediate traits, while about half of water-SRIs exhibited a high h2 (>60%), but low GG (<20%). Principle component analysis revealed that most vegetation-SRIs and seven of 15 water-SRIs were grouped together in a positive direction, had a moderate to strong relationship with measured traits, and could identify the drought-tolerant parent Sakha 93 and several RILs. The PLSR model based on all SRIs as a single index showed moderate to high R2 in calibration (0.53–0.75) and validation (0.46–0.72) datasets, with strong relationships between observed and predicted values of measured traits. The SMLR models identified four and three SRIs from vegetation-SRIs and water-SRIs, respectively, to explain 63–86% of the total variability in measured traits among genotypes. These results demonstrated that vegetation-SRIs can be used individually or combined with water-SRIs as alternative breeding tools to increase genetic gains and selection accuracy in spring wheat breeding.
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Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling. REMOTE SENSING 2020. [DOI: 10.3390/rs12061024] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) satellite data with a return cycle of five days and a high resolution at 13 spectral bands allows close observation of crop phenology and crop physiological attributes at field scale during crop growth. Here, we test the potential for indices derived from S2 data to estimate dryland wheat yields at the field scale and the potential for enhanced predictability by incorporating a modelled crop water stress index (SI). Observations from 103 study fields over the 2016 and 2017 cropping seasons across Northeastern Australia were used. Vegetation indices derived from S2 showed moderately high accuracy in yield prediction and explained over 70% of the yield variability. Specifically, the red edge chlorophyll index (CI; chlorophyll) (R2 = 0.76, RMSE = 0.88 t/ha) and the optimized soil-adjusted vegetation index (OSAVI; structural) (R2 = 0.74, RMSE = 0.91 t/ha) showed the best correlation with field yields. Furthermore, combining the crop model-derived SI with both structural and chlorophyll indices significantly enhanced predictability. The best model with combined OSAVI, CI and SI generated a much higher correlation, with R2 = 0.91 and RMSE = 0.54 t/ha. When validating the models on an independent set of fields, this model also showed high correlation (R2 = 0.93, RMSE = 0.64 t/ha). This study demonstrates the potential of combining S2-derived indices and crop model-derived indices to construct an enhanced yield prediction model suitable for fields in diversified climate conditions.
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The Impacts of Flowering Time and Tillering on Grain Yield of Sorghum Hybrids across Diverse Environments. AGRONOMY-BASEL 2020. [DOI: 10.3390/agronomy10010135] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Sorghum in Australia is grown in water-limited environments of varying extent, generating substantial genotype × environment interactions (GEIs) for grain yield. Much of the yield variation and GEI results from variations in flowering time and tillering through their effects on canopy development. The confounding effects of flowering and tillering complicate the interpretation of breeding trials. In this study, we evaluated the impacts of both flowering time (DTF) and tillering capacity (FTN) on the yield of 1741 unique test hybrids derived from three common female testers in 21 yield testing trials (48 tester/trial combinations) across the major sorghum production regions in Australia in three seasons. Contributions of DTF and FTN to genetic variation in grain yield were significant in 14 and 12 tester/trial combinations, respectively. The proportion of genetic variance in grain yield explained by DTF and FTN ranged from 0.2% to 61.0% and from 1.4% to 56.9%, respectively, depending on trials and genetic background of female testers. The relationship of DTF or FTN with grain yield of hybrids was frequently positive but varied across the genetic background of testers. Accounting for the effects of DTF and FTN using linear models did not substantially increase the between-trial genetic correlations for grain yield. The results suggested that other factors affecting canopy development dynamics and grain yield might contribute GEI and/or the linear approach to account for DTF and FTN on grain yield did not capture the complex non-linear interactions.
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Yang M, Hassan MA, Xu K, Zheng C, Rasheed A, Zhang Y, Jin X, Xia X, Xiao Y, He Z. Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat. FRONTIERS IN PLANT SCIENCE 2020; 11:927. [PMID: 32676089 PMCID: PMC7333459 DOI: 10.3389/fpls.2020.00927] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 06/05/2020] [Indexed: 05/21/2023]
Abstract
Unmanned aerial vehicle (UAV) based remote sensing is a promising approach for non-destructive and high-throughput assessment of crop water and nitrogen (N) efficiencies. In this study, UAV was used to evaluate two field trials using four water (T0 = 0 mm, T1 = 80 mm, T2 = 120 mm, and T3 = 160 mm), and four N (T0 = 0, T1 = 120 kg ha-1, T2 = 180 kg ha-1, and T3 = 240 kg ha-1) treatments, respectively, conducted on three wheat genotypes at two locations. Ground-based destructive data of water and N indictors such as biomass and N contents were also measured to validate the aerial surveillance results. Multispectral traits including red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), red-edge chlorophyll index (RECI) and normalized green red difference index (NGRDI) were recorded using UAV as reliable replacement of destructive measurements by showing high r values up to 0.90. NGRDI was identified as the most efficient non-destructive indicator through strong prediction values ranged from R 2 = 0.69 to 0.89 for water use efficiencies (WUE) calculated from biomass (WUE.BM), and R 2 = 0.80 to 0.86 from grain yield (WUE.GY). RNDVI was better in predicting the phenotypic variations for N use efficiency calculated from nitrogen contents of plant samples (NUE.NC) with high R 2 values ranging from 0.72 to 0.94, while NDRE was consistent in predicting both NUE.NC and NUE.GY by 0.73 to 0.84 with low root mean square errors. UAV-based remote sensing demonstrates that treatment T2 in both water 120 mm and N 180 kg ha-1 supply trials was most appropriate dosages for optimum uptake of water and N with high GY. Among three cultivars, Zhongmai 895 was highly efficient in WUE and NUE across the water and N treatments. Conclusively, UAV can be used to predict time-series WUE and NUE across the season for selection of elite genotypes, and to monitor crop efficiency under varying N and water dosages.
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Affiliation(s)
- Mengjiao Yang
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Muhammad Adeel Hassan
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Kaijie Xu
- Institute of Cotton Research, CAAS, Anyang, China
| | - Chengyan Zheng
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Awais Rasheed
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
- Department of Plant Science, Quaid-i-Azam University, Islamabad, Pakistan
- International Maize and Wheat Improvement Centre (CIMMYT) China Office, c/o CAAS, Beijing, China
| | - Yong Zhang
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Xiuliang Jin
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing, China
| | - Xianchun Xia
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Yonggui Xiao
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
- *Correspondence: Yonggui Xiao,
| | - Zhonghu He
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
- International Maize and Wheat Improvement Centre (CIMMYT) China Office, c/o CAAS, Beijing, China
- Zhonghu He,
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Liedtke JD, Hunt CH, George-Jaeggli B, Laws K, Watson J, Potgieter AB, Cruickshank A, Jordan DR. High-Throughput Phenotyping of Dynamic Canopy Traits Associated with Stay-Green in Grain Sorghum. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:4635153. [PMID: 33313557 PMCID: PMC7706314 DOI: 10.34133/2020/4635153] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 07/10/2020] [Indexed: 05/19/2023]
Abstract
Drought is a recurring phenomenon that puts crop yields at risk and threatens the livelihoods of many people around the globe. Stay-green is a drought adaption phenotype found in sorghum and other cereals. Plants expressing this phenotype show less drought-induced senescence and maintain functional green leaves for longer when water limitation occurs during grain fill, conferring benefits in both yield per se and harvestability. The physiological causes of the phenotype are postulated to be water saving through mechanisms such as reduced canopy size or access to extra water through mechanisms such as deeper roots. In sorghum breeding programs, stay-green has traditionally been assessed by comparing visual scores of leaf senescence either by identifying final leaf senescence or by estimating rate of leaf senescence. In this study, we compared measurements of canopy dynamics obtained from remote sensing on two sorghum breeding trials to stay-green values (breeding values) obtained from visual leaf senescence ratings in multienvironment breeding trials to determine which components of canopy development were most closely linked to the stay-green phenotype. Surprisingly, canopy size as estimated using preflowering canopy parameters was weakly correlated with stay-green values for leaf senescence while postflowering canopy parameters showed a much stronger association with leaf senescence. Our study suggests that factors other than canopy size have an important role in the expression of a stay-green phenotype in grain sorghum and further that the use of UAVs with multispectral sensors provides an excellent way of measuring canopy traits of hundreds of plots grown in large field trials.
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Affiliation(s)
| | - C. H. Hunt
- Agri-Science Queensland, Department of Agriculture and Fisheries, Warwick, QLD 4370, Australia
| | - B. George-Jaeggli
- Agri-Science Queensland, Department of Agriculture and Fisheries, Warwick, QLD 4370, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Warwick, QLD 4370, Australia
| | - K. Laws
- Agri-Science Queensland, Department of Agriculture and Fisheries, Warwick, QLD 4370, Australia
| | - J. Watson
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Gatton Campus, Toowoomba QLD 4343, Australia
| | - A. B. Potgieter
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Gatton Campus, Toowoomba QLD 4343, Australia
| | - A. Cruickshank
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Warwick, QLD 4370, Australia
| | - D. R. Jordan
- Agri-Science Queensland, Department of Agriculture and Fisheries, Warwick, QLD 4370, Australia
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35
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A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting. REMOTE SENSING 2019. [DOI: 10.3390/rs11242939] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Small unmanned aerial systems (UAS) have emerged as high-throughput platforms for the collection of high-resolution image data over large crop fields to support precision agriculture and plant breeding research. At the same time, the improved efficiency in image capture is leading to massive datasets, which pose analysis challenges in providing needed phenotypic data. To complement these high-throughput platforms, there is an increasing need in crop improvement to develop robust image analysis methods to analyze large amount of image data. Analysis approaches based on deep learning models are currently the most promising and show unparalleled performance in analyzing large image datasets. This study developed and applied an image analysis approach based on a SegNet deep learning semantic segmentation model to estimate sorghum panicles counts, which are critical phenotypic data in sorghum crop improvement, from UAS images over selected sorghum experimental plots. The SegNet model was trained to semantically segment UAS images into sorghum panicles, foliage and the exposed ground using 462, 250 × 250 labeled images, which was then applied to field orthomosaic to generate a field-level semantic segmentation. Individual panicle locations were obtained after post-processing the segmentation output to remove small objects and split merged panicles. A comparison between model panicle count estimates and manually digitized panicle locations in 60 randomly selected plots showed an overall detection accuracy of 94%. A per-plot panicle count comparison also showed high agreement between estimated and reference panicle counts (Spearman correlation ρ = 0.88, mean bias = 0.65). Misclassifications of panicles during the semantic segmentation step and mosaicking errors in the field orthomosaic contributed mainly to panicle detection errors. Overall, the approach based on deep learning semantic segmentation showed good promise and with a larger labeled dataset and extensive hyper-parameter tuning, should provide even more robust and effective characterization of sorghum panicle counts.
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36
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Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs. REMOTE SENSING 2019. [DOI: 10.3390/rs11202456] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Leaf area index (LAI) is a key biophysical parameter for monitoring crop growth status, predicting crop yield, and quantifying crop variability in agronomic applications. Mapping the LAI at the field scale using multispectral cameras onboard unmanned aerial vehicles (UAVs) is a promising precision-agriculture application with specific requirements: The LAI retrieval method should be (1) robust so that crop LAI can be estimated with similar accuracy and (2) easy to use so that it can be applied to the adjustment of field management practices. In this study, three UAV remote-sensing missions (UAVs with Micasense RedEdge-M and Cubert S185 cameras) were carried out over six experimental plots from 2018 to 2019 to investigate the performance of reflectance-based lookup tables (LUTs) and vegetation index (VI)-based LUTs generated from the PROSAIL model for wheat LAI retrieval. The effects of the central wavelengths and bandwidths for the VI calculations on the LAI retrieval were further examined. We found that the VI-LUT strategy was more robust and accurate than the reflectance-LUT strategy. The differences in the LAI retrieval accuracy among the four VI-LUTs were small, although the improved modified chlorophyll absorption ratio index-lookup table (MCARI2-LUT) and normalized difference vegetation index-lookup table (NDVI-LUT) performed slightly better. We also found that both of the central wavelengths and bandwidths of the VIs had effects on the LAI retrieval. The VI-LUTs with optimized central wavelengths (red = 612 nm, near-infrared (NIR) = 756 nm) and narrow bandwidths (~4 nm) improved the wheat LAI retrieval accuracy (R2 ≥ 0.75). The results of this study provide an alternative method for retrieving crop LAI, which is robust and easy use for precision-agriculture applications and may be helpful for designing UAV multispectral cameras for agricultural monitoring.
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Valente J, Kooistra L, Mucher S. Fast Classification of Large Germinated Fields Via High-Resolution UAV Imagery. IEEE Robot Autom Lett 2019. [DOI: 10.1109/lra.2019.2926957] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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38
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Zhu Y, Zhao C, Yang H, Yang G, Han L, Li Z, Feng H, Xu B, Wu J, Lei L. Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data. PeerJ 2019; 7:e7593. [PMID: 31576235 PMCID: PMC6753932 DOI: 10.7717/peerj.7593] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 07/31/2019] [Indexed: 12/17/2022] Open
Abstract
Above-ground biomass (AGB) is an important indicator for effectively assessing crop growth and yield and, in addition, is an important ecological indicator for assessing the efficiency with which crops use light and store carbon in ecosystems. However, most existing methods using optical remote sensing to estimate AGB cannot observe structures below the maize canopy, which may lead to poor estimation accuracy. This paper proposes to use the stem-leaf separation strategy integrated with unmanned aerial vehicle LiDAR and multispectral image data to estimate the AGB in maize. First, the correlation matrix was used to screen optimal the LiDAR structural parameters (LSPs) and the spectral vegetation indices (SVIs). According to the screened indicators, the SVIs and the LSPs were subjected to multivariable linear regression (MLR) with the above-ground leaf biomass (AGLB) and above-ground stem biomass (AGSB), respectively. At the same time, all SVIs derived from multispectral data and all LSPs derived from LiDAR data were subjected to partial least squares regression (PLSR) with the AGLB and AGSB, respectively. Finally, the AGB was computed by adding the AGLB and the AGSB, and each was estimated by using the MLR and the PLSR methods, respectively. The results indicate a strong correlation between the estimated and field-observed AGB using the MLR method (R2 = 0.82, RMSE = 79.80 g/m2, NRMSE = 11.12%) and the PLSR method (R2 = 0.86, RMSE = 72.28 g/m2, NRMSE = 10.07%). The results indicate that PLSR more accurately estimates AGB than MLR, with R2 increasing by 0.04, root mean square error (RMSE) decreasing by 7.52 g/m2, and normalized root mean square error (NRMSE) decreasing by 1.05%. In addition, the AGB is more accurately estimated by combining LiDAR with multispectral data than LiDAR and multispectral data alone, with R2 increasing by 0.13 and 0.30, respectively, RMSE decreasing by 22.89 and 54.92 g/m2, respectively, and NRMSE decreasing by 4.46% and 7.65%, respectively. This study improves the prediction accuracy of AGB and provides a new guideline for monitoring based on the fusion of multispectral and LiDAR data.
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Affiliation(s)
- Yaohui Zhu
- School of Information Science and Technology, Beijing Forestry University, Beijing, China.,Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, China.,National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Chunjiang Zhao
- School of Information Science and Technology, Beijing Forestry University, Beijing, China.,Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, China.,National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Hao Yang
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, China.,National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Guijun Yang
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, China.,National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Liang Han
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, China.,College of Architecture and Geomatics Engineering, Shanxi Datong University, Datong, China
| | - Zhenhai Li
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, China.,National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Haikuan Feng
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, China.,National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Bo Xu
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, China.,National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Jintao Wu
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, China.,National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Lei Lei
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, China.,National Engineering Research Center for Information Technology in Agriculture, Beijing, China
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39
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Furbank RT, Jimenez-Berni JA, George-Jaeggli B, Potgieter AB, Deery DM. Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops. THE NEW PHYTOLOGIST 2019; 223:1714-1727. [PMID: 30937909 DOI: 10.1111/nph.15817] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Accepted: 03/02/2019] [Indexed: 05/21/2023]
Abstract
Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high-throughput techniques based on machine vision, robotics, and computing (plant phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning, and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focuses on how field-based plant phenomics can enable next-generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis, and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from 'Green Revolution' traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, Chl fluorescence, and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high-throughput phenotyping could enable the next generation of crop research and breeding is presented.
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Affiliation(s)
- Robert T Furbank
- ARC Centre of Excellence for Translational Photosynthesis, Division of Plant Science, Australian National University, Canberra, 2601, ACT, Australia
- CSIRO Agriculture and Food, Canberra, 2601, ACT, Australia
| | - Jose A Jimenez-Berni
- CSIRO Agriculture and Food, Canberra, 2601, ACT, Australia
- Institute for Sustainable Agriculture (IAS), CSIC, Cordoba, 14004, Spain
| | - Barbara George-Jaeggli
- Queensland Alliance for Agriculture & Food Innovation, Centre for Crop Science, The University of Queensland, Hermitage Research Station, Warwick, 4370, QLD, Australia
- Agri-Science Queensland, Queensland Department of Agriculture & Fisheries, Hermitage Research Facility, Warwick, 4370, QLD, Australia
| | - Andries B Potgieter
- Queensland Alliance for Agriculture & Food Innovation, Centre for Crop Science, The University of Queensland, Tor Street, Toowoomba, 4350, QLD, Australia
| | - David M Deery
- CSIRO Agriculture and Food, Canberra, 2601, ACT, Australia
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Cockerton HM, Li B, Vickerstaff RJ, Eyre CA, Sargent DJ, Armitage AD, Marina-Montes C, Garcia-Cruz A, Passey AJ, Simpson DW, Harrison RJ. Identifying Verticillium dahliae Resistance in Strawberry Through Disease Screening of Multiple Populations and Image Based Phenotyping. FRONTIERS IN PLANT SCIENCE 2019; 10:924. [PMID: 31379904 PMCID: PMC6657532 DOI: 10.3389/fpls.2019.00924] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Accepted: 07/01/2019] [Indexed: 05/06/2023]
Abstract
Verticillium dahliae is a highly detrimental pathogen of soil cultivated strawberry (Fragaria x ananassa). Breeding of Verticillium wilt resistance into commercially viable strawberry cultivars can help mitigate the impact of the disease. In this study we describe novel sources of resistance identified in multiple strawberry populations, creating a wealth of data for breeders to exploit. Pathogen-informed experiments have allowed the differentiation of subclade-specific resistance responses, through studying V. dahliae subclade II-1 specific resistance in the cultivar "Redgauntlet" and subclade II-2 specific resistance in "Fenella" and "Chandler." A large-scale low-cost phenotyping platform was developed utilizing automated unmanned vehicles and near infrared imaging cameras to assess field-based disease trials. The images were used to calculate disease susceptibility for infected plants through the normalized difference vegetation index score. The automated disease scores showed a strong correlation with the manual scores. A co-dominant resistant QTL; FaRVd3D, present in both "Redgauntlet" and "Hapil" cultivars exhibited a major effect of 18.3% when the two resistance alleles were combined. Another allele, FaRVd5D, identified in the "Emily" cultivar was associated with an increase in Verticillium wilt susceptibility of 17.2%, though whether this allele truly represents a susceptibility factor requires further research, due to the nature of the F1 mapping population. Markers identified in populations were validated across a set of 92 accessions to determine whether they remained closely linked to resistance genes in the wider germplasm. The resistant markers FaRVd2B from "Redgauntlet" and FaRVd6D from "Chandler" were associated with resistance across the wider germplasm. Furthermore, comparison of imaging versus manual phenotyping revealed the automated platform could identify three out of four disease resistance markers. As such, this automated wilt disease phenotyping platform is considered to be a good, time saving, substitute for manual assessment.
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Affiliation(s)
| | - Bo Li
- NIAB EMR, East Malling, United Kingdom
| | | | - Catherine A. Eyre
- Driscoll’s Genetics Ltd., East Malling Enterprise Centre, East Malling, United Kingdom
| | - Daniel J. Sargent
- Driscoll’s Genetics Ltd., East Malling Enterprise Centre, East Malling, United Kingdom
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Deery DM, Rebetzke GJ, Jimenez-Berni JA, Bovill WD, James RA, Condon AG, Furbank RT, Chapman SC, Fischer RA. Evaluation of the Phenotypic Repeatability of Canopy Temperature in Wheat Using Continuous-Terrestrial and Airborne Measurements. FRONTIERS IN PLANT SCIENCE 2019; 10:875. [PMID: 31338102 PMCID: PMC6629910 DOI: 10.3389/fpls.2019.00875] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 06/19/2019] [Indexed: 05/19/2023]
Abstract
Infrared canopy temperature (CT) is a well-established surrogate measure of stomatal conductance. There is ample evidence showing that genotypic variation in stomatal conductance is associated with grain yield in wheat. Our goal was to determine when CT repeatability is greatest (throughout the season and within the day) to guide CT deployment for research and wheat breeding. CT was measured continuously with ArduCrop wireless infrared thermometers from post-tillering to physiological maturity, and with airborne thermography on cloudless days from manned helicopter at multiple times before and after flowering. Our experiments in wheat, across two years contrasting for water availability, showed that repeatability for CT was greatest later in the season, during grain-filling, and usually in the afternoon. This was supported by the observation that repeatability for ArduCrop, and more so for airborne CT, was significantly associated (P < 0.0001) with calculated clear-sky solar radiation and to a lesser degree, vapor pressure deficit. Adding vapor pressure deficit to a model comprising either clear-sky solar radiation or its determinants, day-of-year and hour-of-day, made little to no improvement to the coefficient of determination. Phenotypic correlations for airborne CT afternoon sampling events were consistently high between events in the same year, more so for the year when soil water was plentiful (r = 0.7 to 0.9) than the year where soil water was limiting (r = 0.4 to 0.9). Phenotypic correlations for afternoon airborne CT were moderate between years contrasting in soil water availability (r = 0.1 to 0.5) and notably greater on two separate days following irrigation or rain in the drier year, ranging from r = 0.39 to 0.53 (P < 0.0001) for the midday events. For ArduCrop CT the pattern of phenotypic correlations, within a given year, was similar for both years: phenotypic correlations were higher during the grain-filling months of October and November and for hours-of-day from 11 onwards. The lowest correlations comprised events from hours-of-day 8 and 9 across all months. The capacity for the airborne method to instantaneously sample CT on hundreds of plots is more suited to large field experiments than the static ArduCrop sensors which measure CT continuously on a single experimental plot at any given time. Our findings provide promising support for the reliable deployment of CT phenotyping for research and wheat breeding, whereby the high repeatability and high phenotypic correlations between afternoon sampling events during grain-filling could enable reliable screening of germplasm from only one or two sampling events.
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Affiliation(s)
| | | | | | | | | | | | - Robert T. Furbank
- CSIRO Agriculture and Food, Canberra, ACT, Australia
- ARC Centre of Excellence for Translational Photosynthesis, Australian National University, Canberra, ACT, Australia
| | - Scott C. Chapman
- CSIRO Agriculture and Food, Brisbane, QLD, Australia
- School of Food and Agricultural Sciences, The University of Queensland, St. Lucia, QLD, Australia
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Blancon J, Dutartre D, Tixier MH, Weiss M, Comar A, Praud S, Baret F. A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery. FRONTIERS IN PLANT SCIENCE 2019; 10:685. [PMID: 31231403 PMCID: PMC6568052 DOI: 10.3389/fpls.2019.00685] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 05/07/2019] [Indexed: 05/19/2023]
Abstract
The dynamics of the Green Leaf Area Index (GLAI) is of great interest for numerous applications such as yield prediction and plant breeding. We present a high-throughput model-assisted method for characterizing GLAI dynamics in maize (Zea mays subsp. mays) using multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV). Two trials were conducted with a high diversity panel of 400 lines under well-watered and water-deficient treatments in 2016 and 2017. For each UAV flight, we first derived GLAI estimates from empirical relationships between the multispectral reflectance and ground level measurements of GLAI achieved over a small sample of microplots. We then fitted a simple but physiologically sound GLAI dynamics model over the GLAI values estimated previously. Results show that GLAI dynamics was estimated accurately throughout the cycle (R2 > 0.9). Two parameters of the model, biggest leaf area and leaf longevity, were also estimated successfully. We showed that GLAI dynamics and the parameters of the fitted model are highly heritable (0.65 ≤ H2 ≤ 0.98), responsive to environmental conditions, and linked to yield and drought tolerance. This method, combining growth modeling, UAV imagery and simple non-destructive field measurements, provides new high-throughput tools for understanding the adaptation of GLAI dynamics and its interaction with the environment. GLAI dynamics is also a promising trait for crop breeding, and paves the way for future genetic studies.
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Affiliation(s)
- Justin Blancon
- Biogemma, Centre de Recherche de Chappes, Chappes, France
| | | | | | - Marie Weiss
- INRA UMR 114 EMMAH, UMT CAPTE, Domaine Saint-Paul, Avignon, France
| | | | | | - Frédéric Baret
- INRA UMR 114 EMMAH, UMT CAPTE, Domaine Saint-Paul, Avignon, France
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Han L, Yang G, Dai H, Yang H, Xu B, Li H, Long H, Li Z, Yang X, Zhao C. Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform. PLANT METHODS 2019; 15:57. [PMID: 31149023 PMCID: PMC6537385 DOI: 10.1186/s13007-019-0444-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 05/22/2019] [Indexed: 05/31/2023]
Abstract
BACKGROUND With environmental deterioration, natural resource scarcity, and rapid population growth, mankind is facing severe global food security problems. To meet future needs, it is necessary to accelerate progress in breeding for new varieties with high yield and strong resistance. However, the traditional phenotypic screening methods have some disadvantages, such as destructive, inefficient, low-dimensional, labor-intensive and cumbersome, which seriously hinder the development of field breeding. Breeders urgently need a high-throughput technique for acquiring and evaluating phenotypic data that can efficiently screen out excellent phenotypic traits from large-scale genotype populations. RESULTS In the present study, we used an unmanned aerial vehicle (UAV) high-throughput phenotyping (HTP) platform to collect RGB and multispectral images for a breeding program and acquired multiple phenotypic components (or traits), such as plant height, normalized difference vegetation index, biomass accumulation, plant-height growth rate, lodging, and leaf color. By implementing self-organizing maps and principal components analysis biplots to establish phenotypic map and similarity, we proposed an UAV-assisted HTP framework for preselecting maize (Zee mays L.) phenotypic components (or traits). CONCLUSIONS This framework gives breeders additional information to allow them to quickly identify and preselect plants that have genotypes conferring desirable phenotypic components out of thousands of field plots. The present study also demonstrates that remote sensing is a powerful tool with which to acquire abundant phenotypic components. By using these rich phenotypic components, breeders should be able to more effectively identify and select superior genotypes.
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Affiliation(s)
- Liang Han
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 2College of Architecture and Geomatics Engineering, Shanxi Datong University, Datong, 037003 China
- 4College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing, 100083 China
| | - Guijun Yang
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Huayang Dai
- 4College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing, 100083 China
| | - Hao Yang
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Bo Xu
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Heli Li
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Huiling Long
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Zhenhai Li
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Xiaodong Yang
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Chunjiang Zhao
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
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Hassan MA, Yang M, Rasheed A, Yang G, Reynolds M, Xia X, Xiao Y, He Z. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:95-103. [PMID: 31003615 DOI: 10.1016/j.plantsci.2018.10.022] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 10/23/2018] [Accepted: 10/24/2018] [Indexed: 05/18/2023]
Abstract
Wheat improvement programs require rapid assessment of large numbers of individual plots across multiple environments. Vegetation indices (VIs) that are mainly associated with yield and yield-related physiological traits, and rapid evaluation of canopy normalized difference vegetation index (NDVI) can assist in-season selection. Multi-spectral imagery using unmanned aerial vehicles (UAV) can readily assess the VIs traits at various crop growth stages. Thirty-two wheat cultivars and breeding lines grown in limited irrigation and full irrigation treatments were investigated to monitor NDVI across the growth cycle using a Sequoia sensor mounted on a UAV. Significant correlations ranging from R2 = 0.38 to 0.90 were observed between NDVI detected from UAV and Greenseeker (GS) during stem elongation (SE) to late grain gilling (LGF) across the treatments. UAV-NDVI also had high heritabilities at SE (h2 = 0.91), flowering (F)(h2 = 0.95), EGF (h2 = 0.79) and mid grain filling (MGF) (h2 = 0.71) under the full irrigation treatment, and at booting (B) (h2 = 0.89), EGF (h2 = 0.75) in the limited irrigation treatment. UAV-NDVI explained significant variation in grain yield (GY) at EGF (R2 = 0.86), MGF (R2 = 0.83) and LGF (R2 = 0.89) stages, and results were consistent with GS-NDVI. Higher correlations between UAV-NDVI and GY were observed under full irrigation at three different grain-filling stages (R2 = 0.40, 0.49 and 0.45) than the limited irrigation treatment (R2 = 0.08, 0.12 and 0.14) and GY was calculated to be 24.4% lower under limited irrigation conditions. Pearson correlations between UAV-NDVI and GY were also low ranging from r = 0.29 to 0.37 during grain-filling under limited irrigation but higher than GS-NDVI data. A similar pattern was observed for normalized difference red-edge (NDRE) and normalized green red difference index (NGRDI) when correlated with GY. Fresh biomass estimated at late flowering stage had significant correlations of r = 0.30 to 0.51 with UAV-NDVI at EGF. Some genotypes Nongda 211, Nongda 5181, Zhongmai 175 and Zhongmai 12 were identified as high yielding genotypes using NDVI during grain-filling. In conclusion, a multispectral sensor mounted on a UAV is a reliable high-throughput platform for NDVI measurement to predict biomass and GY and grain-filling stage seems the best period for selection.
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Affiliation(s)
- Muhammad Adeel Hassan
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China
| | - Mengjiao Yang
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China; College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China
| | - Awais Rasheed
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China; International Maize and Wheat Improvement Centre (CIMMYT) China Office, c/o CAAS, Beijing 100081, China
| | - Guijun Yang
- Beijing Research Centre for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, China
| | - Matthew Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Apdo. Postal 6-641, 06600 Mexico DF, Mexico
| | - Xianchun Xia
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China
| | - Yonggui Xiao
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China.
| | - Zhonghu He
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China; International Maize and Wheat Improvement Centre (CIMMYT) China Office, c/o CAAS, Beijing 100081, China.
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van Eeuwijk FA, Bustos-Korts D, Millet EJ, Boer MP, Kruijer W, Thompson A, Malosetti M, Iwata H, Quiroz R, Kuppe C, Muller O, Blazakis KN, Yu K, Tardieu F, Chapman SC. Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:23-39. [PMID: 31003609 DOI: 10.1016/j.plantsci.2018.06.018] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 06/05/2018] [Accepted: 06/19/2018] [Indexed: 05/18/2023]
Abstract
New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.
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Affiliation(s)
- Fred A van Eeuwijk
- Biometris, Wageningen University & Research Centre, P.O. Box 16, 6700 AC Wageningen, The Netherlands.
| | - Daniela Bustos-Korts
- Biometris, Wageningen University & Research Centre, P.O. Box 16, 6700 AC Wageningen, The Netherlands
| | - Emilie J Millet
- Biometris, Wageningen University & Research Centre, P.O. Box 16, 6700 AC Wageningen, The Netherlands
| | - Martin P Boer
- Biometris, Wageningen University & Research Centre, P.O. Box 16, 6700 AC Wageningen, The Netherlands
| | - Willem Kruijer
- Biometris, Wageningen University & Research Centre, P.O. Box 16, 6700 AC Wageningen, The Netherlands
| | - Addie Thompson
- Institute for Plant Sciences, Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - Marcos Malosetti
- Biometris, Wageningen University & Research Centre, P.O. Box 16, 6700 AC Wageningen, The Netherlands
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Roberto Quiroz
- International Potato Center (CIP), P.O. Box 1558, Lima 12, Peru
| | - Christian Kuppe
- Institute for Bio-and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Onno Muller
- Institute for Bio-and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Konstantinos N Blazakis
- Department of Horticultural Genetics and Biotechnology, Mediterranean Agronomic Institute of Chania (MAICh), Alsylio Agrokipiou, P.O. Box 85, 73100 Chania-Crete, Greece
| | - Kang Yu
- Crop Science, Institute of Agricultural Sciences, ETH Zurich, Switzerland; Remote Sensing & Terrestrial Ecology, Department of Earth and Environmental Sciences, KU Leuven, Belgium
| | - Francois Tardieu
- Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, UMR759, INRA, 34060 Montpellier, France
| | - Scott C Chapman
- CSIRO Agriculture and Food, Queensland Bioscience Precinct, 306 Carmody Road, St Lucia, QLD 4067, Australia; School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD 4343, Australia
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Leakey ADB, Ferguson JN, Pignon CP, Wu A, Jin Z, Hammer GL, Lobell DB. Water Use Efficiency as a Constraint and Target for Improving the Resilience and Productivity of C 3 and C 4 Crops. ANNUAL REVIEW OF PLANT BIOLOGY 2019; 70:781-808. [PMID: 31035829 DOI: 10.1146/annurev-arplant-042817-040305] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
The ratio of plant carbon gain to water use, known as water use efficiency (WUE), has long been recognized as a key constraint on crop production and an important target for crop improvement. WUE is a physiologically and genetically complex trait that can be defined at a range of scales. Many component traits directly influence WUE, including photosynthesis, stomatal and mesophyll conductances, and canopy structure. Interactions of carbon and water relations with diverse aspects of the environment and crop development also modulate WUE. As a consequence, enhancing WUE by breeding or biotechnology has proven challenging but not impossible. This review aims to synthesize new knowledge of WUE arising from advances in phenotyping, modeling, physiology, genetics, and molecular biology in the context of classical theoretical principles. In addition, we discuss how rising atmospheric CO2 concentration has created and will continue to create opportunities for enhancing WUE by modifying the trade-off between photosynthesis and transpiration.
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Affiliation(s)
- Andrew D B Leakey
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA;
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - John N Ferguson
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Charles P Pignon
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA;
| | - Alex Wu
- Centre for Crop Science and Centre of Excellence for Translational Photosynthesis, University of Queensland, St. Lucia, Queensland 4069, Australia
| | - Zhenong Jin
- Department of Earth System Science and Center for Food Security and Environment, Stanford University, Stanford, California 94305, USA
| | - Graeme L Hammer
- Centre for Crop Science and Centre of Excellence for Translational Photosynthesis, University of Queensland, St. Lucia, Queensland 4069, Australia
| | - David B Lobell
- Department of Earth System Science and Center for Food Security and Environment, Stanford University, Stanford, California 94305, USA
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Cen H, Wan L, Zhu J, Li Y, Li X, Zhu Y, Weng H, Wu W, Yin W, Xu C, Bao Y, Feng L, Shou J, He Y. Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras. PLANT METHODS 2019; 15:32. [PMID: 30972143 PMCID: PMC6436235 DOI: 10.1186/s13007-019-0418-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 03/21/2019] [Indexed: 05/18/2023]
Abstract
BACKGROUND Unmanned aerial vehicle (UAV)-based remote sensing provides a flexible, low-cost, and efficient approach to monitor crop growth status at fine spatial and temporal resolutions, and has a high potential to accelerate breeding process and improve precision field management. METHOD In this study, we discussed the use of lightweight UAV with dual image-frame snapshot cameras to estimate aboveground biomass (AGB) and panicle biomass (PB) of rice at different growth stages with different nitrogen (N) treatments. The spatial-temporal variations in the typical vegetation indices (VIs) and AGB were first investigated, and the accuracy of crop surface model (CSM) extracted from the Red Green Blue (RGB) images at two different stages were also evaluated. Random forest (RF) model for AGB estimation as well as the PB was then developed. Furthermore, variable importance and sensitivity analysis of UAV variables were performed to study the potential of improving model robustness and prediction accuracies. RESULTS It was found that the canopy height extracted from the CSM (Hcsm) exhibited a high correlation with the ground-measured canopy height, while it was unsuitable to be independently used for biomass assessment of rice during the entire growth stages. We also observed that several VIs were highly correlated with AGB, and the modified normalized difference spectral index extracted from the multispectral image achieved the highest correlation. RF model with fusing RGB and multispectral image data substantially improved the prediction results of AGB and PB with the prediction of root mean square error (RMSEP) reduced by 8.33-16.00%. The best prediction results for AGB and PB were achieved with the coefficient of determination (r2), the RMSEP and relative RMSE (RRMSE) of 0.90, 0.21 kg/m2 and 14.05%, and 0.68, 0.10 kg/m2 and 12.11%, respectively. In addition, the result confirmed that the sensitivity analysis could simplify the prediction model without reducing the prediction accuracy. CONCLUSION These findings demonstrate the feasibility of applying lightweight UAV with dual image-frame snapshot cameras for rice biomass estimation, and its potential for high throughput analysis of plant growth-related traits in precision agriculture as well as the advanced breeding program.
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Affiliation(s)
- Haiyan Cen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 People’s Republic of China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 People’s Republic of China
| | - Liang Wan
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 People’s Republic of China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 People’s Republic of China
| | - Jiangpeng Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 People’s Republic of China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 People’s Republic of China
| | - Yijian Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 People’s Republic of China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 People’s Republic of China
| | - Xiaoran Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 People’s Republic of China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 People’s Republic of China
| | - Yueming Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 People’s Republic of China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 People’s Republic of China
| | - Haiyong Weng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 People’s Republic of China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 People’s Republic of China
| | - Weikang Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 People’s Republic of China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 People’s Republic of China
| | - Wenxin Yin
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 People’s Republic of China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 People’s Republic of China
| | - Chi Xu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 People’s Republic of China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 People’s Republic of China
| | - Yidan Bao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 People’s Republic of China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 People’s Republic of China
| | - Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 People’s Republic of China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 People’s Republic of China
| | - Jianyao Shou
- Zhuji Agricultural Technology Extension Center, Zhuji, 311800 People’s Republic of China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 People’s Republic of China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 People’s Republic of China
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Wu S, Wen W, Xiao B, Guo X, Du J, Wang C, Wang Y. An Accurate Skeleton Extraction Approach From 3D Point Clouds of Maize Plants. FRONTIERS IN PLANT SCIENCE 2019; 10:248. [PMID: 30899271 PMCID: PMC6416182 DOI: 10.3389/fpls.2019.00248] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 02/14/2019] [Indexed: 05/27/2023]
Abstract
Accurate and high-throughput determination of plant morphological traits is essential for phenotyping studies. Nowadays, there are many approaches to acquire high-quality three-dimensional (3D) point clouds of plants. However, it is difficult to estimate phenotyping parameters accurately of the whole growth stages of maize plants using these 3D point clouds. In this paper, an accurate skeleton extraction approach was proposed to bridge the gap between 3D point cloud and phenotyping traits estimation of maize plants. The algorithm first uses point cloud clustering and color difference denoising to reduce the noise of the input point clouds. Next, the Laplacian contraction algorithm is applied to shrink the points. Then the key points representing the skeleton of the plant are selected through adaptive sampling, and neighboring points are connected to form a plant skeleton composed of semantic organs. Finally, deviation skeleton points to the input point cloud are calibrated by building a step forward local coordinate along the tangent direction of the original points. The proposed approach successfully generates accurately extracted skeleton from 3D point cloud and helps to estimate phenotyping parameters with high precision of maize plants. Experimental verification of the skeleton extraction process, tested using three cultivars and different growth stages maize, demonstrates that the extracted matches the input point cloud well. Compared with 3D digitizing data-derived morphological parameters, the NRMSE of leaf length, leaf inclination angle, leaf top length, leaf azimuthal angle, leaf growth height, and plant height, estimated using the extracted plant skeleton, are 5.27, 8.37, 5.12, 4.42, 1.53, and 0.83%, respectively, which could meet the needs of phenotyping analysis. The time required to process a single maize plant is below 100 s. The proposed approach may play an important role in further maize research and applications, such as genotype-to-phenotype study, geometric reconstruction, functional structural maize modeling, and dynamic growth animation.
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Affiliation(s)
- Sheng Wu
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Weiliang Wen
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Boxiang Xiao
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xinyu Guo
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Jianjun Du
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Chuanyu Wang
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Yongjian Wang
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
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Boyles RE, Brenton ZW, Kresovich S. Genetic and genomic resources of sorghum to connect genotype with phenotype in contrasting environments. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:19-39. [PMID: 30260043 DOI: 10.1111/tpj.14113] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 08/30/2018] [Accepted: 09/03/2018] [Indexed: 05/10/2023]
Abstract
With the recent development of genomic resources and high-throughput phenotyping platforms, the 21st century is primed for major breakthroughs in the discovery, understanding and utilization of plant genetic variation. Significant advances in agriculture remain at the forefront to increase crop production and quality to satisfy the global food demand in a changing climate all while reducing the environmental impacts of the world's food production. Sorghum, a resilient C4 grain and grass important for food and energy production, is being extensively dissected genetically and phenomically to help connect the relationship between genetic and phenotypic variation. Unlike genetically modified crops such as corn or soybean, sorghum improvement has relied heavily on public research; thus, many of the genetic resources serve a dual purpose for both academic and commercial pursuits. Genetic and genomic resources not only provide the foundation to identify and understand the genes underlying variation, but also serve as novel sources of genetic and phenotypic diversity in plant breeding programs. To better disseminate the collective information of this community, we discuss: (i) the genomic resources of sorghum that are at the disposal of the research community; (ii) the suite of sorghum traits as potential targets for increasing productivity in contrasting environments; and (iii) the prospective approaches and technologies that will help to dissect the genotype-phenotype relationship as well as those that will apply foundational knowledge for sorghum improvement.
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Affiliation(s)
- Richard E Boyles
- Pee Dee Research and Education Center, Clemson University, 2200 Pocket Rd, Florence, SC, 29506, USA
- Advanced Plant Technology Program, Clemson University, 105 Collings St, Clemson, SC, 29634, USA
| | - Zachary W Brenton
- Advanced Plant Technology Program, Clemson University, 105 Collings St, Clemson, SC, 29634, USA
- Department of Plant and Environment Sciences, Clemson University, 171 Poole Agricultural Center, Clemson, SC, 29634, USA
| | - Stephen Kresovich
- Advanced Plant Technology Program, Clemson University, 105 Collings St, Clemson, SC, 29634, USA
- Department of Plant and Environment Sciences, Clemson University, 171 Poole Agricultural Center, Clemson, SC, 29634, USA
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Mochida K, Koda S, Inoue K, Hirayama T, Tanaka S, Nishii R, Melgani F. Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective. Gigascience 2019; 8:5232233. [PMID: 30520975 PMCID: PMC6312910 DOI: 10.1093/gigascience/giy153] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 09/06/2018] [Accepted: 11/24/2018] [Indexed: 11/29/2022] Open
Abstract
Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.
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Affiliation(s)
- Keiichi Mochida
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Microalgae Production Control Technology Laboratory, RIKEN Baton Zone Program, RIKEN Cluster for Science, Technology and Innovation Hub, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Institute of Plant Science and Resources, Okayama University, 2-20-1 Chuo, Kurashiki, Okayama 710-0046, Japan
- Kihara Institute for Biological Research, Yokohama City University, 641-12 Maioka-cho, Totsuka-ku, Yokohama, Kanagawa 244–0813, Japan
- Graduate School of Nanobioscience, Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama, Kanagawa 236-0027, Japan
| | - Satoru Koda
- Graduate School of Mathematics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Komaki Inoue
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Takashi Hirayama
- Institute of Plant Science and Resources, Okayama University, 2-20-1 Chuo, Kurashiki, Okayama 710-0046, Japan
| | - Shojiro Tanaka
- Hiroshima University of Economics, 5-37-1, Gion, Asaminami, Hiroshima-shi Hiroshima 731-0138, Japan
| | - Ryuei Nishii
- Institute of Mathematics for Industry, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Farid Melgani
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123 Trento, Italy
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