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Morton M, Fiene G, Ahmed HI, Rey E, Abrouk M, Angel Y, Johansen K, Saber NO, Malbeteau Y, Al-Mashharawi S, Ziliani MG, Aragon B, Oakey H, Berger B, Brien C, Krattinger SG, Mousa MAA, McCabe MF, Negrão S, Tester M, Julkowska MM. Deciphering salt stress responses in Solanum pimpinellifolium through high-throughput phenotyping. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024. [PMID: 38970620 DOI: 10.1111/tpj.16894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 06/03/2024] [Indexed: 07/08/2024]
Abstract
Soil salinity is a major environmental stressor affecting agricultural productivity worldwide. Understanding plant responses to salt stress is crucial for developing resilient crop varieties. Wild relatives of cultivated crops, such as wild tomato, Solanum pimpinellifolium, can serve as a useful resource to further expand the resilience potential of the cultivated germplasm, S. lycopersicum. In this study, we employed high-throughput phenotyping in the greenhouse and field conditions to explore salt stress responses of a S. pimpinellifolium diversity panel. Our study revealed extensive phenotypic variations in response to salt stress, with traits such as transpiration rate, shoot mass, and ion accumulation showing significant correlations with plant performance. We found that while transpiration was a key determinant of plant performance in the greenhouse, shoot mass strongly correlated with yield under field conditions. Conversely, ion accumulation was the least influential factor under greenhouse conditions. Through a Genome Wide Association Study, we identified candidate genes not previously associated with salt stress, highlighting the power of high-throughput phenotyping in uncovering novel aspects of plant stress responses. This study contributes to our understanding of salt stress tolerance in S. pimpinellifolium and lays the groundwork for further investigations into the genetic basis of these traits, ultimately informing breeding efforts for salinity tolerance in tomato and other crops.
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Affiliation(s)
- Mitchell Morton
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Gabriele Fiene
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Hanin Ibrahim Ahmed
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Elodie Rey
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Michael Abrouk
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Yoseline Angel
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
| | - Kasper Johansen
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Noha O Saber
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Yoann Malbeteau
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Samir Al-Mashharawi
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Matteo G Ziliani
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Hydrosat S.à r.l., 9 Rue du Laboratoire, Luxembourg City, 1911, Luxembourg
| | - Bruno Aragon
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Helena Oakey
- Robinson Institute, University of Adelaide, Adelaide, Australia
| | - Bettina Berger
- Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, Australia
| | - Chris Brien
- Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, Australia
| | - Simon G Krattinger
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Magdi A A Mousa
- Department of Agriculture, Faculty of Environmental Sciences, King Abdulaziz University, Jeddah, 80208, Saudi Arabia
- Department of Vegetable Crops, Faculty of Agriculture, Assiut University, Assiut, 71526, Egypt
| | - Matthew F McCabe
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Sónia Negrão
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- University College, Dublin, Republic of Ireland
| | - Mark Tester
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Magdalena M Julkowska
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Boyce Thompson Institute, Ithaca, New York, USA
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Li H, Wan L, Li C, Wang L, Zhu S, Chen X, Wang P. Hyperspectal imaging technology for phenotyping iron and boron deficiency in Brassica napus under greenhouse conditions. FRONTIERS IN PLANT SCIENCE 2024; 15:1351301. [PMID: 38855462 PMCID: PMC11157068 DOI: 10.3389/fpls.2024.1351301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 05/10/2024] [Indexed: 06/11/2024]
Abstract
Introduction The micronutrient deficiency of iron and boron is a common issue affecting the growth of rapeseed (Brassica napus). In this study, a non-destructive diagnosis method for iron and boron deficiency in Brassica napus (genotype: Zhongshuang 11) using hyperspectral imaging technology was established. Methods The recognition accuracy was compared using the Fisher Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) recognition models. Recognition results showed that Multiple Scattering Correction (MSC) could be applied for the full band hyperspectral data processing, while the LDA models presented better performance on establishing the leaf iron and boron deficiency symptom recognition than the SVM models. Results The recognition accuracy of the training set reached 96.67%, and the recognition rate of the prediction set could be 91.67%. To improve the model accuracy, the Competitive Adaptive Reweighted Sampling algorithm (CARS) was added to construct the MSC-CARS-LDA model. 33 featured wavelengths were selected via CARS. The recognition accuracy of the MSC-CARS-LDA training set was 100%, while the recognition accuracy of the MSC-CARS-LDA prediction set was 95.00%. Discussion This study indicates that, it is capable to identify the iron and boron deficiency in rapeseed using hyperspectral imaging technology.
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Affiliation(s)
- Hui Li
- College of Engineering and Technology, Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, Southwest University, Chongqing, China
- Key Laboratory of Modern Agricultural Equipment and Technology (Jiangsu University), Ministry of Education, School of Agricultural Engineering, Jiangsu University, Zhenjiang, China
| | - Long Wan
- College of Engineering and Technology, Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, Southwest University, Chongqing, China
| | - Chengsong Li
- College of Engineering and Technology, Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, Southwest University, Chongqing, China
- National Citrus Engineering Research Center, Chinese Academy of Agricultural Sciences & Southwest University, Chongqing, China
| | - Lihong Wang
- College of Engineering and Technology, Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, Southwest University, Chongqing, China
| | - Shiping Zhu
- College of Engineering and Technology, Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, Southwest University, Chongqing, China
| | - Xinping Chen
- Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, College of Resources and Environment, Southwest University, Chongqing, China
| | - Pei Wang
- College of Engineering and Technology, Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, Southwest University, Chongqing, China
- Key Laboratory of Modern Agricultural Equipment and Technology (Jiangsu University), Ministry of Education, School of Agricultural Engineering, Jiangsu University, Zhenjiang, China
- Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China
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Mathew J, Delavarpour N, Miranda C, Stenger J, Zhang Z, Aduteye J, Flores P. A Novel Approach to Pod Count Estimation Using a Depth Camera in Support of Soybean Breeding Applications. SENSORS (BASEL, SWITZERLAND) 2023; 23:6506. [PMID: 37514799 PMCID: PMC10384073 DOI: 10.3390/s23146506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/28/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023]
Abstract
Improving soybean (Glycine max L. (Merr.)) yield is crucial for strengthening national food security. Predicting soybean yield is essential to maximize the potential of crop varieties. Non-destructive methods are needed to estimate yield before crop maturity. Various approaches, including the pod-count method, have been used to predict soybean yield, but they often face issues with the crop background color. To address this challenge, we explored the application of a depth camera to real-time filtering of RGB images, aiming to enhance the performance of the pod-counting classification model. Additionally, this study aimed to compare object detection models (YOLOV7 and YOLOv7-E6E) and select the most suitable deep learning (DL) model for counting soybean pods. After identifying the best architecture, we conducted a comparative analysis of the model's performance by training the DL model with and without background removal from images. Results demonstrated that removing the background using a depth camera improved YOLOv7's pod detection performance by 10.2% precision, 16.4% recall, 13.8% mAP@50, and 17.7% mAP@0.5:0.95 score compared to when the background was present. Using a depth camera and the YOLOv7 algorithm for pod detection and counting yielded a mAP@0.5 of 93.4% and mAP@0.5:0.95 of 83.9%. These results indicated a significant improvement in the DL model's performance when the background was segmented, and a reasonably larger dataset was used to train YOLOv7.
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Affiliation(s)
- Jithin Mathew
- Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA
| | - Nadia Delavarpour
- Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA
| | - Carrie Miranda
- Department of Plant Sciences, North Dakota State University, Fargo, ND 58105, USA
| | - John Stenger
- North Dakota Agricultural Weather Network, School of Natural Resource Sciences, North Dakota State University, Fargo, ND 58105, USA
| | - Zhao Zhang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Justice Aduteye
- Department of Agronomy, Earth University, San Jose 4442-1000, Costa Rica
| | - Paulo Flores
- Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA
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Roychowdhury R, Das SP, Gupta A, Parihar P, Chandrasekhar K, Sarker U, Kumar A, Ramrao DP, Sudhakar C. Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant's Abiotic Stress Tolerance Responses. Genes (Basel) 2023; 14:1281. [PMID: 37372461 DOI: 10.3390/genes14061281] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/03/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
The present day's ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant's innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the 'omics' toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant's genes, transcripts, proteins, epigenome, cellular metabolic circuits and resultant phenotype. Instead of mono-omics, two or more (hence 'multi-omics') integrated-omics approaches can decipher the plant's abiotic stress tolerance response very well. Multi-omics-characterized plants can be used as potent genetic resources to incorporate into the future breeding program. For the practical utility of crop improvement, multi-omics approaches for particular abiotic stress tolerance can be combined with genome-assisted breeding (GAB) by being pyramided with improved crop yield, food quality and associated agronomic traits and can open a new era of omics-assisted breeding. Thus, multi-omics pipelines together are able to decipher molecular processes, biomarkers, targets for genetic engineering, regulatory networks and precision agriculture solutions for a crop's variable abiotic stress tolerance to ensure food security under changing environmental circumstances.
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Affiliation(s)
- Rajib Roychowdhury
- Department of Plant Pathology and Weed Research, Institute of Plant Protection, Agricultural Research Organization (ARO)-The Volcani Institute, Rishon Lezion 7505101, Israel
| | - Soumya Prakash Das
- School of Bioscience, Seacom Skills University, Bolpur 731236, West Bengal, India
| | - Amber Gupta
- Dr. Vikram Sarabhai Institute of Cell and Molecular Biology, Faculty of Science, Maharaja Sayajirao University of Baroda, Vadodara 390002, Gujarat, India
| | - Parul Parihar
- Department of Biotechnology and Bioscience, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India
| | - Kottakota Chandrasekhar
- Department of Plant Biochemistry and Biotechnology, Sri Krishnadevaraya College of Agricultural Sciences (SKCAS), Affiliated to Acharya N.G. Ranga Agricultural University (ANGRAU), Guntur 522034, Andhra Pradesh, India
| | - Umakanta Sarker
- Department of Genetics and Plant Breeding, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
| | - Ajay Kumar
- Department of Botany, Maharshi Vishwamitra (M.V.) College, Buxar 802102, Bihar, India
| | - Devade Pandurang Ramrao
- Department of Biotechnology, Mizoram University, Pachhunga University College Campus, Aizawl 796001, Mizoram, India
| | - Chinta Sudhakar
- Plant Molecular Biology Laboratory, Department of Botany, Sri Krishnadevaraya University, Anantapur 515003, Andhra Pradesh, India
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Abebe AM, Kim Y, Kim J, Kim SL, Baek J. Image-Based High-Throughput Phenotyping in Horticultural Crops. PLANTS (BASEL, SWITZERLAND) 2023; 12:2061. [PMID: 37653978 PMCID: PMC10222289 DOI: 10.3390/plants12102061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/12/2023] [Accepted: 05/18/2023] [Indexed: 09/02/2023]
Abstract
Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.
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Affiliation(s)
| | | | | | | | - Jeongho Baek
- Department of Agricultural Biotechnology, National Institute of Agricultural Science, Rural Development Administration, Jeonju 54874, Republic of Korea
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Li JL, Su WH, Zhang HY, Peng Y. A real-time smart sensing system for automatic localization and recognition of vegetable plants for weed control. FRONTIERS IN PLANT SCIENCE 2023; 14:1133969. [PMID: 37051077 PMCID: PMC10083263 DOI: 10.3389/fpls.2023.1133969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Tomato is a globally grown vegetable crop with high economic and nutritional values. Tomato production is being threatened by weeds. This effect is more pronounced in the early stages of tomato plant growth. Thus weed management in the early stages of tomato plant growth is very critical. The increasing labor cost of manual weeding and the negative impact on human health and the environment caused by the overuse of herbicides are driving the development of smart weeders. The core task that needs to be addressed in developing a smart weeder is to accurately distinguish vegetable crops from weeds in real time. In this study, a new approach is proposed to locate tomato and pakchoi plants in real time based on an integrated sensing system consisting of camera and color mark sensors. The selection scheme of reference, color, area, and category of plant labels for sensor identification was examined. The impact of the number of sensors and the size of the signal tolerance region on the system recognition accuracy was also evaluated. The experimental results demonstrated that the color mark sensor using the main stem of tomato as the reference exhibited higher performance than that of pakchoi in identifying the plant labels. The scheme of applying white topical markers on the lower main stem of the tomato plant is optimal. The effectiveness of the six sensors used by the system to detect plant labels was demonstrated. The computer vision algorithm proposed in this study was specially developed for the sensing system, yielding the highest overall accuracy of 95.19% for tomato and pakchoi localization. The proposed sensor-based system is highly accurate and reliable for automatic localization of vegetable plants for weed control in real time.
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Zhang M, Chen T, Gu X, Chen D, Wang C, Wu W, Zhu Q, Zhao C. Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods. FRONTIERS IN PLANT SCIENCE 2023; 14:1073346. [PMID: 36968402 PMCID: PMC10030857 DOI: 10.3389/fpls.2023.1073346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.
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Affiliation(s)
- Mingzheng Zhang
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
| | - Tian’en Chen
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Xiaohe Gu
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Dong Chen
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Cong Wang
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Wenbiao Wu
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Qingzhen Zhu
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Chunjiang Zhao
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
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Guo M, Wang XS, Guo HD, Bai SY, Khan A, Wang XM, Gao YM, Li JS. Tomato salt tolerance mechanisms and their potential applications for fighting salinity: A review. FRONTIERS IN PLANT SCIENCE 2022; 13:949541. [PMID: 36186008 PMCID: PMC9515470 DOI: 10.3389/fpls.2022.949541] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 08/17/2022] [Indexed: 06/01/2023]
Abstract
One of the most significant environmental factors affecting plant growth, development and productivity is salt stress. The damage caused by salt to plants mainly includes ionic, osmotic and secondary stresses, while the plants adapt to salt stress through multiple biochemical and molecular pathways. Tomato (Solanum lycopersicum L.) is one of the most widely cultivated vegetable crops and a model dicot plant. It is moderately sensitive to salinity throughout the period of growth and development. Biotechnological efforts to improve tomato salt tolerance hinge on a synthesized understanding of the mechanisms underlying salinity tolerance. This review provides a comprehensive review of major advances on the mechanisms controlling salt tolerance of tomato in terms of sensing and signaling, adaptive responses, and epigenetic regulation. Additionally, we discussed the potential application of these mechanisms in improving salt tolerance of tomato, including genetic engineering, marker-assisted selection, and eco-sustainable approaches.
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Affiliation(s)
- Meng Guo
- School of Agriculture, Ningxia University, Yinchuan, China
- Key Laboratory of Modern Molecular Breeding for Dominant and Special Crops in Ningxia, Yinchuan, China
- Ningxia Modern Facility Horticulture Engineering Technology Research Center, Yinchuan, China
- Ningxia Facility Horticulture Technology Innovation Center, Ningxia University, Yinchuan, China
| | - Xin-Sheng Wang
- School of Agriculture, Ningxia University, Yinchuan, China
| | - Hui-Dan Guo
- College of Horticulture and Landscape, Henan Institute of Science and Technology, Xinxiang, China
| | - Sheng-Yi Bai
- School of Agriculture, Ningxia University, Yinchuan, China
| | - Abid Khan
- Department of Horticulture, The University of Haripur, Haripur, Pakistan
| | - Xiao-Min Wang
- School of Agriculture, Ningxia University, Yinchuan, China
- Key Laboratory of Modern Molecular Breeding for Dominant and Special Crops in Ningxia, Yinchuan, China
- Ningxia Modern Facility Horticulture Engineering Technology Research Center, Yinchuan, China
- Ningxia Facility Horticulture Technology Innovation Center, Ningxia University, Yinchuan, China
| | - Yan-Ming Gao
- School of Agriculture, Ningxia University, Yinchuan, China
- Key Laboratory of Modern Molecular Breeding for Dominant and Special Crops in Ningxia, Yinchuan, China
- Ningxia Modern Facility Horticulture Engineering Technology Research Center, Yinchuan, China
- Ningxia Facility Horticulture Technology Innovation Center, Ningxia University, Yinchuan, China
| | - Jian-She Li
- School of Agriculture, Ningxia University, Yinchuan, China
- Key Laboratory of Modern Molecular Breeding for Dominant and Special Crops in Ningxia, Yinchuan, China
- Ningxia Modern Facility Horticulture Engineering Technology Research Center, Yinchuan, China
- Ningxia Facility Horticulture Technology Innovation Center, Ningxia University, Yinchuan, China
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Ninomiya S. High-throughput field crop phenotyping: current status and challenges. BREEDING SCIENCE 2022; 72:3-18. [PMID: 36045897 PMCID: PMC8987842 DOI: 10.1270/jsbbs.21069] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/16/2021] [Indexed: 05/03/2023]
Abstract
In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is much more difficult to conduct due to unstable environmental conditions compared to HTP in controlled environments, recent advances in HTP technology have allowed these difficulties to be overcome, allowing for rapid, efficient, non-destructive, non-invasive, quantitative, repeatable, and objective phenotyping. Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image sensors, laser sensors, environmental sensors, and drones, along with high-speed computational resources. This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance, in addition to crop organ detection and counting in the fields. Current topics in field HTP are also presented, followed by a discussion on the low rates of adoption of HTP in practical breeding programs.
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Affiliation(s)
- Seishi Ninomiya
- Graduate School of Agriculture and Life Sciences, The University of Tokyo, Nishitokyo, Tokyo 188-0002, Japan
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
- Corresponding author (e-mail: )
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11
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Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements. REMOTE SENSING 2021. [DOI: 10.3390/rs13224560] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Prompt monitoring of maize dwarf mosaic virus (MDMV) is critical for the prevention and control of disease and to ensure high crop yield and quality. Here, we first analyzed the spectral differences between MDMV-infected red leaves and healthy leaves and constructed a sensitive index (SI) for measurements. Next, based on the characteristic bands (Rλ) associated with leaf anthocyanins (Anth), we determined vegetation indices (VIs) commonly used in plant physiological and biochemical parameter inversion and established a vegetation index (VIc) by utilizing the combination of two arbitrary bands following the construction principles of NDVI, DVI, RVI, and SAVI. Furthermore, we developed classification models based on linear discriminant analysis (LDA) and support vector machine (SVM) in order to distinguish the red leaves from healthy leaves. Finally, we performed UR, MLR, PLSR, PCR, and SVM simulations on Anth based on Rλ, VIs, VIc, and Rλ + VIs + VIc and indirectly estimated the severity of MDMV infection based on the relationship between the reflection spectra and Anth. Distinct from those of the normal leaves, the spectra of red leaves showed strong reflectance characteristics at 640 nm, and SI increased with increasing Anth. Moreover, the accuracy of the two VIc-based classification models was 100%, which is significantly higher than that of the VIs and Rλ-based models. Among the Anth regression models, the accuracy of the MLR model based on Rλ + VIs + VIc was the highest (R2c = 0.85; R2v = 0.74). The developed models could accurately identify MDMV and estimate the severity of its infection, laying the theoretical foundation for large-scale remote sensing-based monitoring of this virus in the future.
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Stutsel B, Johansen K, Malbéteau YM, McCabe MF. Detecting Plant Stress Using Thermal and Optical Imagery From an Unoccupied Aerial Vehicle. FRONTIERS IN PLANT SCIENCE 2021; 12:734944. [PMID: 34777418 PMCID: PMC8579776 DOI: 10.3389/fpls.2021.734944] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/14/2021] [Indexed: 06/13/2023]
Abstract
Soil and water salinization has global impact on the sustainability of agricultural production, affecting the health and condition of staple crops and reducing potential yields. Identifying or developing salt-tolerant varieties of commercial crops is a potential pathway to enhance food and water security and deliver on the global demand for an increase in food supplies. Our study focuses on a phenotyping experiment that was designed to establish the influence of salinity stress on a diversity panel of the wild tomato species, Solanum pimpinellifolium. Here, we explore how unoccupied aerial vehicles (UAVs) equipped with both an optical and thermal infrared camera can be used to map and monitor plant temperature (Tp) changes in response to applied salinity stress. An object-based image analysis approach was developed to delineate individual tomato plants, while a green-red vegetation index derived from calibrated red, green, and blue (RGB) optical data allowed the discrimination of vegetation from the soil background. Tp was retrieved simultaneously from the co-mounted thermal camera, with Tp deviation from the ambient temperature and its change across time used as a potential indication of stress. Results showed that Tp differences between salt-treated and control plants were detectable across the five separate UAV campaigns undertaken during the field experiment. Using a simple statistical approach, we show that crop water stress index values greater than 0.36 indicated conditions of plant stress. The optimum period to collect UAV-based Tp for identifying plant stress was found between fruit formation and ripening. Preliminary results also indicate that UAV-based Tp may be used to detect plant stress before it is visually apparent, although further research with more frequent image collections and field observations is required. Our findings provide a tool to accelerate field phenotyping to identify salt-resistant germplasm and may allow farmers to alleviate yield losses through early detection of plant stress via management interventions.
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Affiliation(s)
- Bonny Stutsel
- Hydrology, Agriculture and Land Observation, Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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Machwitz M, Pieruschka R, Berger K, Schlerf M, Aasen H, Fahrner S, Jiménez-Berni J, Baret F, Rascher U. Bridging the Gap Between Remote Sensing and Plant Phenotyping-Challenges and Opportunities for the Next Generation of Sustainable Agriculture. FRONTIERS IN PLANT SCIENCE 2021; 12:749374. [PMID: 34751225 PMCID: PMC8571019 DOI: 10.3389/fpls.2021.749374] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/27/2021] [Indexed: 05/27/2023]
Affiliation(s)
- Miriam Machwitz
- Department of Environmental Research and Innovation, Luxembourg Institute of Science and Technology, Belval, Luxembourg
| | - Roland Pieruschka
- Institute of Bio and Geosciences, Plant Sciences, Forschungszentrum Jülich, Helmholtz-Verband Deutscher Forschungszentren, Jülich, Germany
| | - Katja Berger
- Department of Geography, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Martin Schlerf
- Department of Environmental Research and Innovation, Luxembourg Institute of Science and Technology, Belval, Luxembourg
| | - Helge Aasen
- Department of Environmental Systems Science, Crop Science, Eidgenössische Technische Hochschule (ETH) Zurich, Zurich, Switzerland
| | - Sven Fahrner
- Institute of Bio and Geosciences, Plant Sciences, Forschungszentrum Jülich, Helmholtz-Verband Deutscher Forschungszentren, Jülich, Germany
| | - Jose Jiménez-Berni
- Instituto de Agricultura Sostenible, Consejo Superior de Investigaciones Científicas, Cordoba, Spain
| | | | - Uwe Rascher
- Forschungszentrum Jülich, Institute of Bio- and Geosciences Plant Sciences (IBG-2), Jülich, Germany
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Varshney RK, Bohra A, Roorkiwal M, Barmukh R, Cowling WA, Chitikineni A, Lam HM, Hickey LT, Croser JS, Bayer PE, Edwards D, Crossa J, Weckwerth W, Millar H, Kumar A, Bevan MW, Siddique KHM. Fast-forward breeding for a food-secure world. Trends Genet 2021; 37:1124-1136. [PMID: 34531040 DOI: 10.1016/j.tig.2021.08.002] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 10/20/2022]
Abstract
Crop production systems need to expand their outputs sustainably to feed a burgeoning human population. Advances in genome sequencing technologies combined with efficient trait mapping procedures accelerate the availability of beneficial alleles for breeding and research. Enhanced interoperability between different omics and phenotyping platforms, leveraged by evolving machine learning tools, will help provide mechanistic explanations for complex plant traits. Targeted and rapid assembly of beneficial alleles using optimized breeding strategies and precise genome editing techniques could deliver ideal crops for the future. Realizing desired productivity gains in the field is imperative for securing an adequate future food supply for 10 billion people.
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Affiliation(s)
- Rajeev K Varshney
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India; State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch WA 6150, Western Australia, Australia; The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia.
| | - Abhishek Bohra
- ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, India
| | - Manish Roorkiwal
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India; The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Rutwik Barmukh
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | - Wallace A Cowling
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Annapurna Chitikineni
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | - Hon-Ming Lam
- School of Life Sciences and Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, QLD, Australia
| | - Janine S Croser
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Philipp E Bayer
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia; School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia
| | - David Edwards
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia; School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, Vienna Metabolomics Center (VIME), University of Vienna, Vienna, Austria
| | - Harvey Millar
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, WA, Australia
| | - Arvind Kumar
- Deputy Director General's Office, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | | | - Kadambot H M Siddique
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
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Tatsumi K, Igarashi N, Mengxue X. Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery. PLANT METHODS 2021; 17:77. [PMID: 34266447 PMCID: PMC8281694 DOI: 10.1186/s13007-021-00761-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/04/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND The objective of this study is twofold. First, ascertain the important variables that predict tomato yields from plant height (PH) and vegetation index (VI) maps. The maps were derived from images taken by unmanned aerial vehicles (UAVs). Second, examine the accuracy of predictions of tomato fresh shoot masses (SM), fruit weights (FW), and the number of fruits (FN) from multiple machine learning algorithms using selected variable sets. To realize our objective, ultra-high-resolution RGB and multispectral images were collected by a UAV on ten days in 2020's tomato growing season. From these images, 756 total variables, including first- (e.g., average, standard deviation, skewness, range, and maximum) and second-order (e.g., gray-level co-occurrence matrix features and growth rates of PH and VIs) statistics for each plant, were extracted. Several selection algorithms (i.e., Boruta, DALEX, genetic algorithm, least absolute shrinkage and selection operator, and recursive feature elimination) were used to select the variable sets useful for predicting SM, FW, and FN. Random forests, ridge regressions, and support vector machines were used to predict the yield using the top five selected variable sets. RESULTS First-order statistics of PH and VIs collected during the early to mid-fruit formation periods, about one month prior to harvest, were important variables for predicting SM. Similar to the case for SM, variables collected approximately one month prior to harvest were important for predicting FW and FN. Furthermore, variables related to PH were unimportant for prediction. Compared with predictions obtained using only first-order statistics, those obtained using the second-order statistics of VIs were more accurate for FW and FN. The prediction accuracy of SM, FW, and FN by models constructed from all variables (rRMSE = 8.8-28.1%) was better than that from first-order statistics (rRMSE = 10.0-50.1%). CONCLUSIONS In addition to basic statistics (e.g., average and standard deviation), we derived second-order statistics of PH and VIs at the plant level using the ultra-high resolution UAV images. Our findings indicated that our variable selection method reduced the number variables needed for tomato yield prediction, improving the efficiency of phenotypic data collection and assisting with the selection of high-yield lines within breeding programs.
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Affiliation(s)
- Kenichi Tatsumi
- Department of Environmental and Agricultural Engineering, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan.
| | - Noa Igarashi
- Faculty of Agriculture Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan
| | - Xiao Mengxue
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan
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Tatsumi K, Igarashi N, Mengxue X. Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery. PLANT METHODS 2021; 17:77. [PMID: 34266447 DOI: 10.21203/rs.3.rs-344860/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/04/2021] [Indexed: 05/21/2023]
Abstract
BACKGROUND The objective of this study is twofold. First, ascertain the important variables that predict tomato yields from plant height (PH) and vegetation index (VI) maps. The maps were derived from images taken by unmanned aerial vehicles (UAVs). Second, examine the accuracy of predictions of tomato fresh shoot masses (SM), fruit weights (FW), and the number of fruits (FN) from multiple machine learning algorithms using selected variable sets. To realize our objective, ultra-high-resolution RGB and multispectral images were collected by a UAV on ten days in 2020's tomato growing season. From these images, 756 total variables, including first- (e.g., average, standard deviation, skewness, range, and maximum) and second-order (e.g., gray-level co-occurrence matrix features and growth rates of PH and VIs) statistics for each plant, were extracted. Several selection algorithms (i.e., Boruta, DALEX, genetic algorithm, least absolute shrinkage and selection operator, and recursive feature elimination) were used to select the variable sets useful for predicting SM, FW, and FN. Random forests, ridge regressions, and support vector machines were used to predict the yield using the top five selected variable sets. RESULTS First-order statistics of PH and VIs collected during the early to mid-fruit formation periods, about one month prior to harvest, were important variables for predicting SM. Similar to the case for SM, variables collected approximately one month prior to harvest were important for predicting FW and FN. Furthermore, variables related to PH were unimportant for prediction. Compared with predictions obtained using only first-order statistics, those obtained using the second-order statistics of VIs were more accurate for FW and FN. The prediction accuracy of SM, FW, and FN by models constructed from all variables (rRMSE = 8.8-28.1%) was better than that from first-order statistics (rRMSE = 10.0-50.1%). CONCLUSIONS In addition to basic statistics (e.g., average and standard deviation), we derived second-order statistics of PH and VIs at the plant level using the ultra-high resolution UAV images. Our findings indicated that our variable selection method reduced the number variables needed for tomato yield prediction, improving the efficiency of phenotypic data collection and assisting with the selection of high-yield lines within breeding programs.
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Affiliation(s)
- Kenichi Tatsumi
- Department of Environmental and Agricultural Engineering, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan.
| | - Noa Igarashi
- Faculty of Agriculture Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan
| | - Xiao Mengxue
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan
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Zinc Oxide Phytonanoparticles' Effects on Yield and Mineral Contents in Fruits of Tomato ( Solanum lycopersicum L. cv. Cherry) under Field Conditions. ScientificWorldJournal 2021; 2021:5561930. [PMID: 34220365 PMCID: PMC8213504 DOI: 10.1155/2021/5561930] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 05/14/2021] [Accepted: 05/27/2021] [Indexed: 11/24/2022] Open
Abstract
The use of phytonanoparticles in agriculture could decrease the use of fertilizers and therefore decrease soil contamination, due to their size being better assimilated in plants. It is important to mention that the nanofertilizer is slow-releasing and improves plant physiological properties and various nutritional parameters. The influence of soil and foliar applications of phytonanoparticles of ZnO with the Moringa oleifera extract under three concentrations (25, 50, and 100 ppm) was evaluated on the cherry tomato crop (Solanum lycopersicum L.). Synthesis of the phytonanoparticles was analyzed with ultraviolet-visible spectroscopy (UV-Vis) and infrared transmission spectroscopy with Fourier transform (FT-IR), as well as the analysis with the dynamic light scattering (DLS) technique. The morphometric parameters were evaluated before and after the application of the nanoparticles. The minerals' content of fruits was done 95 days after planting. Results showed that soil application was better at a concentration of 25 ppm of phytonanoparticles since it allowed the greatest number of flowers and fruits on the plant; however, it was demonstrated that when performing a foliar application, the fruit showed the highest concentrations for the elements Mg, Ca, and Na at concentrations of 511, 4589, and 223 mg kg−1, respectively.
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Guo W, Carroll ME, Singh A, Swetnam TL, Merchant N, Sarkar S, Singh AK, Ganapathysubramanian B. UAS-Based Plant Phenotyping for Research and Breeding Applications. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9840192. [PMID: 34195621 PMCID: PMC8214361 DOI: 10.34133/2021/9840192] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 04/29/2021] [Indexed: 05/19/2023]
Abstract
Unmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network. These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications. This paper reviews the state of the art in the deployment, collection, curation, storage, and analysis of data from UAS-based phenotyping platforms. We discuss pressing technical challenges, identify future trends in UAS-based phenotyping that the plant research community should be aware of, and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines. This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas.
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Affiliation(s)
- Wei Guo
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Japan
| | | | - Arti Singh
- Department of Agronomy, Iowa State University, Ames, Iowa, USA
| | | | - Nirav Merchant
- Data Science Institute, University of Arizona, Tucson, USA
| | - Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, Iowa, USA
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Francesconi S, Harfouche A, Maesano M, Balestra GM. UAV-Based Thermal, RGB Imaging and Gene Expression Analysis Allowed Detection of Fusarium Head Blight and Gave New Insights Into the Physiological Responses to the Disease in Durum Wheat. FRONTIERS IN PLANT SCIENCE 2021; 12:628575. [PMID: 33868331 PMCID: PMC8047627 DOI: 10.3389/fpls.2021.628575] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 03/12/2021] [Indexed: 05/24/2023]
Abstract
Wheat is one of the world's most economically important cereal crop, grown on 220 million hectares. Fusarium head blight (FHB) disease is considered a major threat to durum (Triticum turgidum subsp. durum (Desfontaines) Husnache) and bread wheat (T. aestivum L.) cultivars and is mainly managed by the application of fungicides at anthesis. However, fungicides are applied when FHB symptoms are clearly visible and the spikes are almost entirely bleached (% of diseased spikelets > 80%), by when it is too late to control FHB disease. For this reason, farmers often react by performing repeated fungicide treatments that, however, due to the advanced state of the infection, cause a waste of money and pose significant risks to the environment and non-target organisms. In the present study, we used unmanned aerial vehicle (UAV)-based thermal infrared (TIR) and red-green-blue (RGB) imaging for FHB detection in T. turgidum (cv. Marco Aurelio) under natural field conditions. TIR and RGB data coupled with ground-based measurements such as spike's temperature, photosynthetic efficiency and molecular identification of FHB pathogens, detected FHB at anthesis half-way (Zadoks stage 65, ZS 65), when the percentage (%) of diseased spikelets ranged between 20% and 60%. Moreover, in greenhouse experiments the transcripts of the key genes involved in stomatal closure were mostly up-regulated in F. graminearum-inoculated plants, demonstrating that the physiological mechanism behind the spike's temperature increase and photosynthetic efficiency decrease could be attributed to the closure of the guard cells in response to F. graminearum. In addition, preliminary analysis revealed that there is differential regulation of genes between drought-stressed and F. graminearum-inoculated plants, suggesting that there might be a possibility to discriminate between water stress and FHB infection. This study shows the potential of UAV-based TIR and RGB imaging for field phenotyping of wheat and other cereal crop species in response to environmental stresses. This is anticipated to have enormous promise for the detection of FHB disease and tremendous implications for optimizing the application of fungicides, since global food crop demand is to be met with minimal environmental impacts.
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Affiliation(s)
- Sara Francesconi
- Department of Agriculture and Forest Sciences (DAFNE), University of Tuscia, Viterbo, Italy
| | - Antoine Harfouche
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
| | - Mauro Maesano
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
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Takei H, Shirasawa K, Kuwabara K, Toyoda A, Matsuzawa Y, Iioka S, Ariizumi T. De novo genome assembly of two tomato ancestors, Solanum pimpinellifolium and Solanum lycopersicum var. cerasiforme, by long-read sequencing. DNA Res 2021; 28:6104860. [PMID: 33475141 PMCID: PMC7934570 DOI: 10.1093/dnares/dsaa029] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 12/24/2020] [Indexed: 12/13/2022] Open
Abstract
The ancestral tomato species are known to possess genes that are valuable for improving traits in breeding. Here, we aimed to construct high-quality de novo genome assemblies of Solanum pimpinellifolium ‘LA1670’ and S. lycopersicum var. cerasiforme ‘LA1673’, originating from Peru. The Pacific Biosciences (PacBio) long-read sequences with 110× and 104× coverages were assembled and polished to generate 244 and 202 contigs spanning 808.8 Mbp for ‘LA1670’ and 804.5 Mbp for ‘LA1673’, respectively. After chromosome-level scaffolding with reference guiding, 14 scaffold sequences corresponding to 12 tomato chromosomes and 2 unassigned sequences were constructed. High-quality genome assemblies were confirmed using the Benchmarking Universal Single-Copy Orthologs and long terminal repeat assembly index. The protein-coding sequences were then predicted, and their transcriptomes were confirmed. The de novo assembled genomes of S. pimpinellifolium and S. lycopersicum var. cerasiforme were predicted to have 71,945 and 75,230 protein-coding genes, including 29,629 and 29,185 non-redundant genes, respectively, as supported by the transcriptome analysis results. The chromosome-level genome assemblies coupled with transcriptome data sets of the two accessions would be valuable for gaining insights into tomato domestication and understanding genome-scale breeding.
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Affiliation(s)
- Hitomi Takei
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Japan.,Research Fellow of Japan Society for Promotion of Science (JSPS), Kojimachi, Tokyo 102-0083, Japan
| | - Kenta Shirasawa
- Department of Frontier Research and Development, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
| | - Kosuke Kuwabara
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Japan
| | - Atsushi Toyoda
- Comparative Genomics Laboratory, Advanced Genomics Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan.,Comparative Genomics Laboratory, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | | | - Shinji Iioka
- TOKITA Seed Co. LTD, Otone, Saitama 349-1144, Japan
| | - Tohru Ariizumi
- Tsukuba Plant Innovation Research Center, University of Tsukuba, Tsukuba, Ibaraki, 305-8572, Japan.,Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan
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Singh A, Jones S, Ganapathysubramanian B, Sarkar S, Mueller D, Sandhu K, Nagasubramanian K. Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping. TRENDS IN PLANT SCIENCE 2021; 26:53-69. [PMID: 32830044 DOI: 10.1016/j.tplants.2020.07.010] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 07/15/2020] [Accepted: 07/23/2020] [Indexed: 05/06/2023]
Abstract
Plant stress phenotyping is essential to select stress-resistant varieties and develop better stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of stress assessment in comparison with unaided visual measurement. The growing capabilities of machine learning (ML) methods in conjunction with image-based phenotyping can extract new insights from curated, annotated, and high-dimensional datasets across varied crops and stresses. We propose an overarching strategy for utilizing ML techniques that methodically enables the application of plant stress phenotyping at multiple scales across different types of stresses, program goals, and environments.
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Affiliation(s)
- Arti Singh
- Department of Agronomy, Iowa State University, Ames, IA, USA.
| | - Sarah Jones
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | | | - Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA
| | - Daren Mueller
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, USA
| | - Kulbir Sandhu
- Department of Agronomy, Iowa State University, Ames, IA, USA
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Aktar R, Kharismawati DE, Palaniappan K, Aliakbarpour H, Bunyak F, Stapleton AE, Kazic T. Robust mosaicking of maize fields from aerial imagery. APPLICATIONS IN PLANT SCIENCES 2020; 8:e11387. [PMID: 32995105 PMCID: PMC7507512 DOI: 10.1002/aps3.11387] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 07/08/2020] [Indexed: 06/11/2023]
Abstract
PREMISE Aerial imagery from small unmanned aerial vehicle systems is a promising approach for high-throughput phenotyping and precision agriculture. A key requirement for both applications is to create a field-scale mosaic of the aerial imagery sequence so that the same features are in registration, a very challenging problem for crop imagery. METHODS We have developed an improved mosaicking pipeline, Video Mosaicking and summariZation (VMZ), which uses a novel two-dimensional mosaicking algorithm that minimizes errors in estimating the transformations between successive frames during registration. The VMZ pipeline uses only the imagery, rather than relying on vehicle telemetry, ground control points, or global positioning system data, to estimate the frame-to-frame homographies. It exploits the spatiotemporal ordering of the image frames to reduce the computational complexity of finding corresponding features between frames using feature descriptors. We compared the performance of VMZ to a standard two-dimensional mosaicking algorithm (AutoStitch) by mosaicking imagery of two maize (Zea mays) research nurseries freely flown with a variety of trajectories. RESULTS The VMZ pipeline produces superior mosaics faster. Using the speeded up robust features (SURF) descriptor, VMZ produces the highest-quality mosaics. DISCUSSION Our results demonstrate the value of VMZ for the future automated extraction of plant phenotypes and dynamic scouting for crop management.
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Affiliation(s)
- Rumana Aktar
- Department of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Dewi Endah Kharismawati
- Department of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
- Interdisciplinary Plant Group and MU Plant Science FoundryUniversity of MissouriColumbiaMissouriUSA
- Missouri Maize CenterUniversity of MissouriColumbiaMissouriUSA
| | - Kannappan Palaniappan
- Department of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
- Interdisciplinary Plant Group and MU Plant Science FoundryUniversity of MissouriColumbiaMissouriUSA
| | - Hadi Aliakbarpour
- Department of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
- Interdisciplinary Plant Group and MU Plant Science FoundryUniversity of MissouriColumbiaMissouriUSA
| | - Filiz Bunyak
- Department of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
- Interdisciplinary Plant Group and MU Plant Science FoundryUniversity of MissouriColumbiaMissouriUSA
| | - Ann E. Stapleton
- Department of Biology and Marine BiologyUniversity of North CarolinaWilmingtonNorth CarolinaUSA
| | - Toni Kazic
- Department of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
- Interdisciplinary Plant Group and MU Plant Science FoundryUniversity of MissouriColumbiaMissouriUSA
- Missouri Maize CenterUniversity of MissouriColumbiaMissouriUSA
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Johansen K, Morton MJL, Malbeteau Y, Aragon B, Al-Mashharawi S, Ziliani MG, Angel Y, Fiene G, Negrão S, Mousa MAA, Tester MA, McCabe MF. Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest. Front Artif Intell 2020; 3:28. [PMID: 33733147 PMCID: PMC7861253 DOI: 10.3389/frai.2020.00028] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 03/31/2020] [Indexed: 11/13/2022] Open
Abstract
Biomass and yield are key variables for assessing the production and performance of agricultural systems. Modeling and predicting the biomass and yield of individual plants at the farm scale represents a major challenge in precision agriculture, particularly when salinity and other abiotic stresses may play a role. Here, we evaluate a diversity panel of the wild tomato species (Solanum pimpinellifolium) through both field and unmanned aerial vehicle (UAV)-based phenotyping of 600 control and 600 salt-treated plants. The study objective was to predict fresh shoot mass, tomato fruit numbers, and yield mass at harvest based on a range of variables derived from the UAV imagery. UAV-based red-green-blue (RGB) imageries collected 1, 2, 4, 6, 7, and 8 weeks before harvest were also used to determine if prediction accuracies varied between control and salt-treated plants. Multispectral UAV-based imagery was also collected 1 and 2 weeks prior to harvest to further explore predictive insights. In order to estimate the end of season biomass and yield, a random forest machine learning approach was implemented using UAV-imagery-derived predictors as input variables. Shape features derived from the UAV, such as plant area, border length, width, and length, were found to have the highest importance in the predictions, followed by vegetation indices and the entropy texture measure. The multispectral UAV imagery collected 2 weeks prior to harvest produced the highest explained variances for fresh shoot mass (87.95%), fruit numbers (63.88%), and yield mass per plant (66.51%). The RGB UAV imagery produced very similar results to those of the multispectral UAV dataset, with the explained variance reducing as a function of increasing time to harvest. The results showed that predicting the yield of salt-stressed plants produced higher accuracies when the models excluded control plants, whereas predicting the yield of control plants was not affected by the inclusion of salt-stressed plants within the models. This research demonstrates that it is possible to predict the average biomass and yield up to 8 weeks prior to harvest within 4.23% of field-based measurements and up to 4 weeks prior to harvest at the individual plant level. Results from this work may be useful in providing guidance for yield forecasting of healthy and salt-stressed tomato plants, which in turn may inform growing practices, logistical planning, and sales operations.
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Affiliation(s)
- Kasper Johansen
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Mitchell J L Morton
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Yoann Malbeteau
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Bruno Aragon
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Samer Al-Mashharawi
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Matteo G Ziliani
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Yoseline Angel
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Gabriele Fiene
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Sónia Negrão
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.,School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Magdi A A Mousa
- Department of Arid Land Agriculture, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah, Saudi Arabia.,Department of Vegetables, Faculty of Agriculture, Assiut University, Assiut, Egypt
| | - Mark A Tester
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Matthew F McCabe
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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24
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GNSS/INS-Assisted Structure from Motion Strategies for UAV-Based Imagery over Mechanized Agricultural Fields. REMOTE SENSING 2020. [DOI: 10.3390/rs12030351] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Acquired imagery by unmanned aerial vehicles (UAVs) has been widely used for three-dimensional (3D) reconstruction/modeling in various digital agriculture applications, such as phenotyping, crop monitoring, and yield prediction. 3D reconstruction from well-textured UAV-based images has matured and the user community has access to several commercial and opensource tools that provide accurate products at a high level of automation. However, in some applications, such as digital agriculture, due to repetitive image patterns, these approaches are not always able to produce reliable/complete products. The main limitation of these techniques is their inability to establish a sufficient number of correctly matched features among overlapping images, causing incomplete and/or inaccurate 3D reconstruction. This paper provides two structure from motion (SfM) strategies, which use trajectory information provided by an onboard survey-grade global navigation satellite system/inertial navigation system (GNSS/INS) and system calibration parameters. The main difference between the proposed strategies is that the first one—denoted as partially GNSS/INS-assisted SfM—implements the four stages of an automated triangulation procedure, namely, imaging matching, relative orientation parameters (ROPs) estimation, exterior orientation parameters (EOPs) recovery, and bundle adjustment (BA). The second strategy— denoted as fully GNSS/INS-assisted SfM—removes the EOPs estimation step while introducing a random sample consensus (RANSAC)-based strategy for removing matching outliers before the BA stage. Both strategies modify the image matching by restricting the search space for conjugate points. They also implement a linear procedure for ROPs’ refinement. Finally, they use the GNSS/INS information in modified collinearity equations for a simpler BA procedure that could be used for refining system calibration parameters. Eight datasets over six agricultural fields are used to evaluate the performance of the developed strategies. In comparison with a traditional SfM framework and Pix4D Mapper Pro, the proposed strategies are able to generate denser and more accurate 3D point clouds as well as orthophotos without any gaps.
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25
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Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors. REMOTE SENSING 2019. [DOI: 10.3390/rs12010034] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Hyperspectral systems integrated on unmanned aerial vehicles (UAV) provide unique opportunities to conduct high-resolution multitemporal spectral analysis for diverse applications. However, additional time-consuming rectification efforts in postprocessing are routinely required, since geometric distortions can be introduced due to UAV movements during flight, even if navigation/motion sensors are used to track the position of each scan. Part of the challenge in obtaining high-quality imagery relates to the lack of a fast processing workflow that can retrieve geometrically accurate mosaics while optimizing the ground data collection efforts. To address this problem, we explored a computationally robust automated georectification and mosaicking methodology. It operates effectively in a parallel computing environment and evaluates results against a number of high-spatial-resolution datasets (mm to cm resolution) collected using a push-broom sensor and an associated RGB frame-based camera. The methodology estimates the luminance of the hyperspectral swaths and coregisters these against a luminance RGB-based orthophoto. The procedure includes an improved coregistration strategy by integrating the Speeded-Up Robust Features (SURF) algorithm, with the Maximum Likelihood Estimator Sample Consensus (MLESAC) approach. SURF identifies common features between each swath and the RGB-orthomosaic, while MLESAC fits the best geometric transformation model to the retrieved matches. Individual scanlines are then geometrically transformed and merged into a single spatially continuous mosaic reaching high positional accuracies only with a few number of ground control points (GCPs). The capacity of the workflow to achieve high spatial accuracy was demonstrated by examining statistical metrics such as RMSE, MAE, and the relative positional accuracy at 95% confidence level. Comparison against a user-generated georectification demonstrates that the automated approach speeds up the coregistration process by 85%.
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Barreto MAP, Johansen K, Angel Y, McCabe MF. Radiometric Assessment of a UAV-Based Push-Broom Hyperspectral Camera. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4699. [PMID: 31671804 PMCID: PMC6864972 DOI: 10.3390/s19214699] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 10/06/2019] [Accepted: 10/08/2019] [Indexed: 11/16/2022]
Abstract
The use of unmanned aerial vehicles (UAVs) for Earth and environmental sensing has increased significantly in recent years. This is particularly true for multi- and hyperspectral sensing, with a variety of both push-broom and snap-shot systems becoming available. However, information on their radiometric performance and stability over time is often lacking. The authors propose the use of a general protocol for sensor evaluation to characterize the data retrieval and radiometric performance of push-broom hyperspectral cameras, and illustrate the workflow with the Nano-Hyperspec (Headwall Photonics, Boston USA) sensor. The objectives of this analysis were to: (1) assess dark current and white reference consistency, both temporally and spatially; (2) evaluate spectral fidelity; and (3) determine the relationship between sensor-recorded radiance and spectroradiometer-derived reflectance. Both the laboratory-based dark current and white reference evaluations showed an insignificant increase over time (<2%) across spatial pixels and spectral bands for >99.5% of pixel-waveband combinations. Using a mercury/argon (Hg/Ar) lamp, the hyperspectral wavelength bands exhibited a slight shift of 1-3 nm against 29 Hg/Ar wavelength emission lines. The relationship between the Nano-Hyperspec radiance values and spectroradiometer-derived reflectance was found to be highly linear for all spectral bands. The developed protocol for assessing UAV-based radiometric performance of hyperspectral push-broom sensors showed that the Nano-Hyperspec data were both time-stable and spectrally sound.
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Affiliation(s)
- M Alejandra P Barreto
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Al Jazri Building West, Thuwal 23955-6900, Saudi Arabia.
| | - Kasper Johansen
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Al Jazri Building West, Thuwal 23955-6900, Saudi Arabia.
| | - Yoseline Angel
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Al Jazri Building West, Thuwal 23955-6900, Saudi Arabia.
| | - Matthew F McCabe
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Al Jazri Building West, Thuwal 23955-6900, Saudi Arabia.
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