1
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Mostafa S, Mondal D, Panjvani K, Kochian L, Stavness I. Explainable deep learning in plant phenotyping. Front Artif Intell 2023; 6:1203546. [PMID: 37795496 PMCID: PMC10546035 DOI: 10.3389/frai.2023.1203546] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 08/25/2023] [Indexed: 10/06/2023] Open
Abstract
The increasing human population and variable weather conditions, due to climate change, pose a threat to the world's food security. To improve global food security, we need to provide breeders with tools to develop crop cultivars that are more resilient to extreme weather conditions and provide growers with tools to more effectively manage biotic and abiotic stresses in their crops. Plant phenotyping, the measurement of a plant's structural and functional characteristics, has the potential to inform, improve and accelerate both breeders' selections and growers' management decisions. To improve the speed, reliability and scale of plant phenotyping procedures, many researchers have adopted deep learning methods to estimate phenotypic information from images of plants and crops. Despite the successful results of these image-based phenotyping studies, the representations learned by deep learning models remain difficult to interpret, understand, and explain. For this reason, deep learning models are still considered to be black boxes. Explainable AI (XAI) is a promising approach for opening the deep learning model's black box and providing plant scientists with image-based phenotypic information that is interpretable and trustworthy. Although various fields of study have adopted XAI to advance their understanding of deep learning models, it has yet to be well-studied in the context of plant phenotyping research. In this review article, we reviewed existing XAI studies in plant shoot phenotyping, as well as related domains, to help plant researchers understand the benefits of XAI and make it easier for them to integrate XAI into their future studies. An elucidation of the representations within a deep learning model can help researchers explain the model's decisions, relate the features detected by the model to the underlying plant physiology, and enhance the trustworthiness of image-based phenotypic information used in food production systems.
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Affiliation(s)
- Sakib Mostafa
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Debajyoti Mondal
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Karim Panjvani
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Leon Kochian
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ian Stavness
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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2
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Mora-Poblete F, Heidari P, Fuentes S. Editorial: Integrating advanced high-throughput technologies to improve plant resilience to environmental challenges. FRONTIERS IN PLANT SCIENCE 2023; 14:1218691. [PMID: 37324664 PMCID: PMC10264778 DOI: 10.3389/fpls.2023.1218691] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 05/23/2023] [Indexed: 06/17/2023]
Affiliation(s)
- Freddy Mora-Poblete
- Laboratory of Forest Genetics and Biotechnology, Institute of Biological Sciences, University of Talca, Talca, Chile
| | - Parviz Heidari
- Faculty of Agriculture, Shahrood University of Technology, Shahrood, Iran
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Sciences Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, The University of Melbourne, Melbourne, VIC, Australia
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Mexico
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3
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Zhai Q, Ye C, Li S, Liu J, Guo Z, Chang R, Hua J. Rice nitrogen nutrition monitoring classification method based on the convolution neural network model: Direct detection of rice nitrogen nutritional status. PLoS One 2022; 17:e0273360. [PMID: 36413518 PMCID: PMC9681082 DOI: 10.1371/journal.pone.0273360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the farmland to observe leaf color and growth and apply an appropriate amount of nitrogen fertilizer according to the results. However, this method is labor- and time-consuming. To realize automatic rice nitrogen nutrition monitoring, we constructed the Jiangxi rice nitrogen nutrition monitoring model based on a convolution neural network (CNN) using the same region rice canopy image in different generation periods. Our CNN model was evaluated using multiple evaluation criteria (Accuracy, Recall, Precision, and F1 score). The results show that the same CNN model could distinguish the rice nitrogen nutrition status in different periods, which can completely realize the automatic discrimination of nitrogen nutrition status so as to guide the scientific nitrogen application of rice in this area. This will greatly improve the discrimination efficiency of the nitrogen nutrition status and reduce the time and labor cost. The application of the proposed method also proved that the CNN model can be applied in the discrimination of the nitrogen nutrition status. Among CNN models, GoogleNet model proposed a CNN architecture named Inception which can improve the depth of the network and extract higher-level features without changing the amount of calculation of the model. The GoogleNet model achieved the highest accuracy, 95.7%.
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Affiliation(s)
- Qiang Zhai
- School of Mechatronic Engineering, Nanchang University, Nanchang City, Jiangxi Province, China
| | - Chun Ye
- School of Mechatronic Engineering, Nanchang University, Nanchang City, Jiangxi Province, China
- Institute of Agricultural Engineering/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Jiangxi Academy of Agricultural Sciences, Nanchang, China
| | - Shuang Li
- Weichai Power Co., Ltd., WeiFang City, ShanDong Province, China
| | - Jizhong Liu
- School of Mechatronic Engineering, Nanchang University, Nanchang City, Jiangxi Province, China
- * E-mail: (JL); (JH)
| | - Zhiming Guo
- Weichai Power Co., Ltd., WeiFang City, ShanDong Province, China
| | - Ruzhi Chang
- Weichai Power Co., Ltd., WeiFang City, ShanDong Province, China
| | - Jing Hua
- School of Software, Jiangxi Agricultural University, Nanchang City, Jiangxi Province, China
- * E-mail: (JL); (JH)
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4
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Qi M, Berry JC, Veley KW, O'Connor L, Finkel OM, Salas-González I, Kuhs M, Jupe J, Holcomb E, Glavina Del Rio T, Creech C, Liu P, Tringe SG, Dangl JL, Schachtman DP, Bart RS. Identification of beneficial and detrimental bacteria impacting sorghum responses to drought using multi-scale and multi-system microbiome comparisons. THE ISME JOURNAL 2022; 16:1957-1969. [PMID: 35523959 PMCID: PMC9296637 DOI: 10.1038/s41396-022-01245-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 12/12/2022]
Abstract
Drought is a major abiotic stress limiting agricultural productivity. Previous field-level experiments have demonstrated that drought decreases microbiome diversity in the root and rhizosphere. How these changes ultimately affect plant health remains elusive. Toward this end, we combined reductionist, transitional and ecological approaches, applied to the staple cereal crop sorghum to identify key root-associated microbes that robustly affect drought-stressed plant phenotypes. Fifty-three Arabidopsis-associated bacteria were applied to sorghum seeds and their effect on root growth was monitored. Two Arthrobacter strains caused root growth inhibition (RGI) in Arabidopsis and sorghum. In the context of synthetic communities, Variovorax strains were able to protect plants from Arthrobacter-caused RGI. As a transitional system, high-throughput phenotyping was used to test the synthetic communities. During drought stress, plants colonized by Arthrobacter had reduced growth and leaf water content. Plants colonized by both Arthrobacter and Variovorax performed as well or better than control plants. In parallel, we performed a field trial wherein sorghum was evaluated across drought conditions. By incorporating data on soil properties into the microbiome analysis, we accounted for experimental noise with a novel method and were able to observe the negative correlation between the abundance of Arthrobacter and plant growth. Having validated this approach, we cross-referenced datasets from the high-throughput phenotyping and field experiments and report a list of bacteria with high confidence that positively associated with plant growth under drought stress. In conclusion, a three-tiered experimental system successfully spanned the lab-to-field gap and identified beneficial and deleterious bacterial strains for sorghum under drought.
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Affiliation(s)
- Mingsheng Qi
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | | | - Kira W Veley
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Lily O'Connor
- Donald Danforth Plant Science Center, St. Louis, MO, USA.,Washington University, St. Louis, MO, USA
| | - Omri M Finkel
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Howard Hughes Medical Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Plant and Environmental Sciences, Institute of Life Science, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Isai Salas-González
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Howard Hughes Medical Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Molly Kuhs
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Julietta Jupe
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Emily Holcomb
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | | | - Cody Creech
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Peng Liu
- Department of Statistics, Iowa State University, Ames, IA, USA
| | - Susannah G Tringe
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jeffery L Dangl
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Howard Hughes Medical Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daniel P Schachtman
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.,Center for Plant Science Innovation, University of Nebraska - Lincoln, Lincoln, NE, USA
| | - Rebecca S Bart
- Donald Danforth Plant Science Center, St. Louis, MO, USA.
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5
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Berry JC, Qi M, Sonawane BV, Sheflin A, Cousins A, Prenni J, Schachtman DP, Liu P, Bart RS. Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components. eLife 2022; 11:70056. [PMID: 35819140 PMCID: PMC9275819 DOI: 10.7554/elife.70056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 06/29/2022] [Indexed: 12/11/2022] Open
Abstract
Environmental variability poses a major challenge to any field study. Researchers attempt to mitigate this challenge through replication. Thus, the ability to detect experimental signals is determined by the degree of replication and the amount of environmental variation, noise, within the experimental system. A major source of noise in field studies comes from the natural heterogeneity of soil properties which create microtreatments throughout the field. In addition, the variation within different soil properties is often nonrandomly distributed across a field. We explore this challenge through a sorghum field trial dataset with accompanying plant, microbiome, and soil property data. Diverse sorghum genotypes and two watering regimes were applied in a split-plot design. We describe a process of identifying, estimating, and controlling for the effects of spatially distributed soil properties on plant traits and microbial communities using minimal degrees of freedom. Importantly, this process provides a method with which sources of environmental variation in field data can be identified and adjusted, improving our ability to resolve effects of interest and to quantify subtle phenotypes.
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Affiliation(s)
- Jeffrey C Berry
- Donald Danforth Plant Science Center, St. Louis, United States
| | - Mingsheng Qi
- Donald Danforth Plant Science Center, St. Louis, United States
| | - Balasaheb V Sonawane
- School of Biological Sciences, Washington State University, Pullman, United States
| | - Amy Sheflin
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, United States
| | - Asaph Cousins
- School of Biological Sciences, Washington State University, Pullman, United States
| | - Jessica Prenni
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, United States
| | - Daniel P Schachtman
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, United States
| | - Peng Liu
- Department of Statistics, Iowa State University, Ames, United States
| | - Rebecca S Bart
- Donald Danforth Plant Science Center, St. Louis, United States
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6
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Al-Tamimi N, Langan P, Bernád V, Walsh J, Mangina E, Negrão S. Capturing crop adaptation to abiotic stress using image-based technologies. Open Biol 2022; 12:210353. [PMID: 35728624 PMCID: PMC9213114 DOI: 10.1098/rsob.210353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.
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Affiliation(s)
- Nadia Al-Tamimi
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Patrick Langan
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Villő Bernád
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Jason Walsh
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland,School of Computer Science and UCD Energy Institute, University College Dublin, Dublin, Ireland
| | - Eleni Mangina
- School of Computer Science and UCD Energy Institute, University College Dublin, Dublin, Ireland
| | - Sónia Negrão
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
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7
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Wahinya FW, Yamazaki K, Jing Z, Takami T, Kamiya T, Kajiya-Kanegae H, Takanashi H, Iwata H, Tsutsumi N, Fujiwara T, Sakamoto W. Sorghum Ionomics Reveals the Functional SbHMA3a Allele that Limits Excess Cadmium Accumulation in Grains. PLANT & CELL PHYSIOLOGY 2022; 63:713-728. [PMID: 35312772 DOI: 10.1093/pcp/pcac035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/07/2022] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
Understanding uptake and redistribution of essential minerals or sequestering of toxic elements is important for optimized crop production. Although the mechanisms controlling mineral transport have been elucidated in rice and other species, little is understood in sorghum-an important C4 cereal crop. Here, we assessed the genetic factors that govern grain ionome profiles in sorghum using recombinant inbred lines (RILs) derived from a cross between BTx623 and NOG (Takakibi). Pairwise correlation and clustering analysis of 22 elements, measured in sorghum grains harvested under greenhouse conditions, indicated that the parental lines, as well as the RILs, show different ionomes. In particular, BTx623 accumulated significantly higher levels of cadmium (Cd) than NOG, because of differential root-to-shoot translocation factors between the two lines. Quantitative trait locus (QTL) analysis revealed a prominent QTL for grain Cd concentration on chromosome 2. Detailed analysis identified SbHMA3a, encoding a P1B-type ATPase heavy metal transporter, as responsible for low Cd accumulation in grains; the NOG allele encoded a functional HMA3 transporter (SbHMA3a-NOG) whose Cd-transporting activity was confirmed by heterologous expression in yeast. BTx623 possessed a truncated, loss-of-function SbHMA3a allele. The functionality of SbHMA3a in NOG was confirmed by Cd concentrations of F2 grains derived from the reciprocal cross, in which the NOG allele behaved in a dominant manner. We concluded that SbHMA3a-NOG is a Cd transporter that sequesters excess Cd in root tissues, as shown in other HMA3s. Our findings will facilitate the isolation of breeding cultivars with low Cd in grains or in exploiting high-Cd cultivars for phytoremediation.
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Affiliation(s)
- Fiona Wacera Wahinya
- Institute of Plant Science and Resources, Okayama University, 2-20-1 Chuo, Kurashiki, Okayama, 710-0046 Japan
| | - Kiyoshi Yamazaki
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
| | - Zihuan Jing
- Institute of Plant Science and Resources, Okayama University, 2-20-1 Chuo, Kurashiki, Okayama, 710-0046 Japan
| | - Tsuneaki Takami
- Institute of Plant Science and Resources, Okayama University, 2-20-1 Chuo, Kurashiki, Okayama, 710-0046 Japan
| | - Takehiro Kamiya
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
| | - Hiromi Kajiya-Kanegae
- Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization, 2-14-1 Nishi-shimbashi, Minato-ku, Tokyo, 105-0003 Japan
| | - Hideki Takanashi
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
| | - Hiroyoshi Iwata
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
| | - Nobuhiro Tsutsumi
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
| | - Toru Fujiwara
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
| | - Wataru Sakamoto
- Institute of Plant Science and Resources, Okayama University, 2-20-1 Chuo, Kurashiki, Okayama, 710-0046 Japan
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8
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Ebersbach J, Khan NA, McQuillan I, Higgins EE, Horner K, Bandi V, Gutwin C, Vail SL, Robinson SJ, Parkin IAP. Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population. FRONTIERS IN PLANT SCIENCE 2022; 12:780250. [PMID: 35069637 PMCID: PMC8767643 DOI: 10.3389/fpls.2021.780250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.
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Affiliation(s)
| | - Nazifa Azam Khan
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ian McQuillan
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | | | - Kyla Horner
- Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
| | - Venkat Bandi
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Carl Gutwin
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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9
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Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution. AGRIENGINEERING 2021. [DOI: 10.3390/agriengineering3040047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Conventional farming necessitates a large number of resources and infrastructure such as land, irrigation, manpower to manage farms, etc. Modern initiatives are required to automate conventional farms. Smart indoor farms offer the potential to remedy the shortfalls of conventional farms by providing a controlled, intelligent, and smart environment. This paper presents a three-dimensional perspective consisting of soilless farming, energy harvesting, and smart technologies, which could be considered as the three important characteristics of smart indoor farms. A six-layer smart indoor farms architecture has also been proposed, which explains how data are collected using various sensors and devices and then transmitted onto the cloud infrastructure for further analysis and control through various layers. Artificial lighting, smart nutrition management, and artificial climate control, to name a few, are some of the important requirements for smart indoor farms while considering control and service management factors. The major bottleneck in installing such systems is both the economical and the technical constraints. However, with the evolution of technology (and when they become widely available in the near future), a more favourable farming scenario may emerge. Furthermore, smart indoor farms could be viewed as a potential answer for meeting the demands of a sustainable agricultural revolution as we move closer to Agriculture 4.0. Finally, in order to adapt smart indoor farms and their study scope, our work has presented various research areas to potential researchers.
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10
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Danilevicz MF, Bayer PE, Nestor BJ, Bennamoun M, Edwards D. Resources for image-based high-throughput phenotyping in crops and data sharing challenges. PLANT PHYSIOLOGY 2021; 187:699-715. [PMID: 34608963 PMCID: PMC8561249 DOI: 10.1093/plphys/kiab301] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 05/26/2021] [Indexed: 05/06/2023]
Abstract
High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.
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Affiliation(s)
- Monica F. Danilevicz
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
| | - Philipp E. Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
| | - Benjamin J. Nestor
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, University of Western Australia, Perth, Western Australia 6009, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
- Author for communication:
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11
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Zhang L, MacQueen A, Bonnette J, Fritschi FB, Lowry DB, Juenger TE. QTL x environment interactions underlie ionome divergence in switchgrass. G3-GENES GENOMES GENETICS 2021; 11:6259145. [PMID: 33914881 PMCID: PMC8495926 DOI: 10.1093/g3journal/jkab144] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 04/23/2021] [Indexed: 01/02/2023]
Abstract
Ionomics measures elemental concentrations in biological organisms and provides a snapshot of physiology under different conditions. In this study, we evaluate genetic variation of the ionome in outbred, perennial switchgrass in three environments across the species' native range, and explore patterns of genotype-by-environment interactions. We grew 725 clonally replicated genotypes of a large full sib family from a four-way linkage mapping population, created from deeply diverged upland and lowland switchgrass ecotypes, at three common gardens. Concentrations of 18 mineral elements were determined in whole post-anthesis tillers using ion coupled plasma mass spectrometry (ICP-MS). These measurements were used to identify quantitative trait loci (QTL) with and without QTL-by-environment interactions (QTLxE) using a multi-environment QTL mapping approach. We found that element concentrations varied significantly both within and between switchgrass ecotypes, and GxE was present at both the trait and QTL level. Concentrations of 14 of the 18 elements were under some genetic control, and 77 QTL were detected for these elements. 74% of QTL colocalized multiple elements, half of QTL exhibited significant QTLxE, and roughly equal numbers of QTL had significant differences in magnitude and sign of their effects across environments. The switchgrass ionome is under moderate genetic control and by loci with highly variable effects across environments.
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Affiliation(s)
- Li Zhang
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - Alice MacQueen
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - Jason Bonnette
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - Felix B Fritschi
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211
| | - David B Lowry
- Department of Plant Biology and DOE Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI 48824
| | - Thomas E Juenger
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
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12
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Cardim Ferreira Lima M, Krus A, Valero C, Barrientos A, del Cerro J, Roldán-Gómez JJ. Monitoring Plant Status and Fertilization Strategy through Multispectral Images. SENSORS 2020; 20:s20020435. [PMID: 31941027 PMCID: PMC7014396 DOI: 10.3390/s20020435] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/09/2020] [Accepted: 01/09/2020] [Indexed: 11/25/2022]
Abstract
A crop monitoring system was developed for the supervision of organic fertilization status on tomato plants at early stages. An automatic and nondestructive approach was used to analyze tomato plants with different levels of water-soluble organic fertilizer (3 + 5 NK) and vermicompost. The evaluation system was composed by a multispectral camera with five lenses: green (550 nm), red (660 nm), red edge (735 nm), near infrared (790 nm), RGB, and a computational image processing system. The water-soluble fertilizer was applied weekly in four different treatments: (T0: 0 mL, T1: 6.25 mL, T2: 12.5 mL and T3: 25 mL) and the vermicomposting was added in Weeks 1 and 5. The trial was conducted in a greenhouse and 192 images were taken with each lens. A plant segmentation algorithm was developed and several vegetation indices were calculated. On top of calculating indices, multiple morphological features were obtained through image processing techniques. The morphological features were revealed to be more feasible to distinguish between the control and the organic fertilized plants than the vegetation indices. The system was developed in order to be assembled in a precision organic fertilization robotic platform.
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Affiliation(s)
- Matheus Cardim Ferreira Lima
- Department of Agroforest Ecosystems, ETSI Agrónomos, Universidad Politécnica de Valencia, 46022 Valencia, Spain
- Research and Extension Unit (AGDR), Food and Agriculture Organization of the United Nations (FAO), 00153 Rome, Italy
- Correspondence:
| | - Anne Krus
- Department of Agroforest Engineering, ETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain; (A.K.); (C.V.)
| | - Constantino Valero
- Department of Agroforest Engineering, ETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain; (A.K.); (C.V.)
| | - Antonio Barrientos
- Centre for Automation and Robotics (CSIC-UPM), 28006 Madrid, Spain; (A.B.); (J.d.C.); or (J.J.R.-G.)
| | - Jaime del Cerro
- Centre for Automation and Robotics (CSIC-UPM), 28006 Madrid, Spain; (A.B.); (J.d.C.); or (J.J.R.-G.)
| | - Juan Jesús Roldán-Gómez
- Centre for Automation and Robotics (CSIC-UPM), 28006 Madrid, Spain; (A.B.); (J.d.C.); or (J.J.R.-G.)
- Department of Computer Engineering, Higher Polytechnic School, Autonomous University of Madrid (UAM), 28049 Madrid, Spain
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13
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Ubbens J, Cieslak M, Prusinkiewicz P, Parkin I, Ebersbach J, Stavness I. Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:5801869. [PMID: 33313558 PMCID: PMC7706325 DOI: 10.34133/2020/5801869] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 12/15/2019] [Indexed: 05/05/2023]
Abstract
Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C4 grass Setaria, a diversity panel of sorghum (S. bicolor), and the founder panel for a nested association mapping population of canola (Brassica napus L.). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines.
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Affiliation(s)
- Jordan Ubbens
- Department of Computer Science, University of Saskatchewan, Canada
| | - Mikolaj Cieslak
- Department of Computer Science, University of Calgary, Canada
| | | | - Isobel Parkin
- Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
| | | | - Ian Stavness
- Department of Computer Science, University of Saskatchewan, Canada
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14
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Zheng X, Fahlgren N, Abbasi A, Berry JC, Carrington JC. Antiviral ARGONAUTEs Against Turnip Crinkle Virus Revealed by Image-Based Trait Analysis. PLANT PHYSIOLOGY 2019; 180:1418-1435. [PMID: 31043494 PMCID: PMC6752898 DOI: 10.1104/pp.19.00121] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 04/21/2019] [Indexed: 05/18/2023]
Abstract
RNA-based silencing functions as an important antiviral immunity mechanism in plants. Plant viruses evolved to encode viral suppressors of RNA silencing (VSRs) that interfere with the function of key components in the silencing pathway. As effectors in the RNA silencing pathway, ARGONAUTE (AGO) proteins are targeted by some VSRs, such as that encoded by Turnip crinkle virus (TCV). A VSR-deficient TCV mutant was used to identify AGO proteins with antiviral activities during infection. A quantitative phenotyping protocol using an image-based color trait analysis pipeline on the PlantCV platform, with temporal red, green, and blue imaging and a computational segmentation algorithm, was used to measure plant disease after TCV inoculation. This process captured and analyzed growth and leaf color of Arabidopsis (Arabidopsis thaliana) plants in response to virus infection over time. By combining this quantitative phenotypic data with molecular assays to detect local and systemic virus accumulation, AGO2, AGO3, and AGO7 were shown to play antiviral roles during TCV infection. In leaves, AGO2 and AGO7 functioned as prominent nonadditive, anti-TCV effectors, whereas AGO3 played a minor role. Other AGOs were required to protect inflorescence tissues against TCV. Overall, these results indicate that distinct AGO proteins have specialized, modular roles in antiviral defense across different tissues, and demonstrate the effectiveness of image-based phenotyping to quantify disease progression.
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Affiliation(s)
- Xingguo Zheng
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Noah Fahlgren
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Arash Abbasi
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Jeffrey C Berry
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
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15
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Berry JC, Fahlgren N, Pokorny AA, Bart RS, Veley KM. An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping. PeerJ 2018; 6:e5727. [PMID: 30310752 PMCID: PMC6174877 DOI: 10.7717/peerj.5727] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 09/10/2018] [Indexed: 12/11/2022] Open
Abstract
High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.
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Affiliation(s)
- Jeffrey C. Berry
- Donald Danforth Plant Science Center, Saint Louis, MO, United States of America
| | - Noah Fahlgren
- Donald Danforth Plant Science Center, Saint Louis, MO, United States of America
| | | | - Rebecca S. Bart
- Donald Danforth Plant Science Center, Saint Louis, MO, United States of America
| | - Kira M. Veley
- Donald Danforth Plant Science Center, Saint Louis, MO, United States of America
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16
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Effects of Ambient Ozone on Soybean Biophysical Variables and Mineral Nutrient Accumulation. REMOTE SENSING 2018. [DOI: 10.3390/rs10040562] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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