Comparison of Various Drought Resistance Traits in Soybean (<i>Glycine max</i> L.) Based on Image Analysis for Precision Agriculture
Drought is being annually exacerbated by recent global warming, leading to crucial damage of crop growth and final yields. Soybean, one of the most consumed crops worldwide, has also been affected in the process. The development of a resistant cultivar is required to solve this problem, which is con...
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| Format: | Article |
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MDPI AG
2023-06-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/12/12/2331 |
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| author | JaeYoung Kim Chaewon Lee JiEun Park Nyunhee Kim Song-Lim Kim JeongHo Baek Yong-Suk Chung Kyunghwan Kim |
| author_facet | JaeYoung Kim Chaewon Lee JiEun Park Nyunhee Kim Song-Lim Kim JeongHo Baek Yong-Suk Chung Kyunghwan Kim |
| author_sort | JaeYoung Kim |
| collection | DOAJ |
| description | Drought is being annually exacerbated by recent global warming, leading to crucial damage of crop growth and final yields. Soybean, one of the most consumed crops worldwide, has also been affected in the process. The development of a resistant cultivar is required to solve this problem, which is considered the most efficient method for crop producers. To accelerate breeding cycles, genetic engineering and high-throughput phenotyping technologies have replaced conventional breeding methods. However, the current novel phenotyping method still needs to be optimized by species and varieties. Therefore, we aimed to assess the most appropriate and effective phenotypes for evaluating drought stress by applying a high-throughput image-based method on the nested association mapping (NAM) population of soybeans. The acquired image-based traits from the phenotyping platform were divided into three large categories—area, boundary, and color—and demonstrated an aspect for each characteristic. Analysis on categorized traits interpreted stress responses in morphological and physiological changes. The evaluation of drought stress regardless of varieties was possible by combining various image-based traits. We might suggest that a combination of image-based traits obtained using computer vision can be more efficient than using only one trait for the precision agriculture. |
| first_indexed | 2024-03-11T02:00:54Z |
| format | Article |
| id | doaj.art-52677c39835a4b659d4728ba89a8f559 |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-03-11T02:00:54Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj.art-52677c39835a4b659d4728ba89a8f5592023-11-18T12:10:39ZengMDPI AGPlants2223-77472023-06-011212233110.3390/plants12122331Comparison of Various Drought Resistance Traits in Soybean (<i>Glycine max</i> L.) Based on Image Analysis for Precision AgricultureJaeYoung Kim0Chaewon Lee1JiEun Park2Nyunhee Kim3Song-Lim Kim4JeongHo Baek5Yong-Suk Chung6Kyunghwan Kim7Gene Engineering Division, Department of Agricultural Biotechnology, National Institute of Agricultural Science, Jeonju-si 55365, Republic of KoreaCrop Cultivation & Environment Research Division, National Institute of Crop Science, Suwon-si 16613, Republic of KoreaDepartment of Plant Resources and Environment, Jeju National University, Jeju-si 63243, Republic of KoreaGene Engineering Division, Department of Agricultural Biotechnology, National Institute of Agricultural Science, Jeonju-si 55365, Republic of KoreaGene Engineering Division, Department of Agricultural Biotechnology, National Institute of Agricultural Science, Jeonju-si 55365, Republic of KoreaGene Engineering Division, Department of Agricultural Biotechnology, National Institute of Agricultural Science, Jeonju-si 55365, Republic of KoreaDepartment of Plant Resources and Environment, Jeju National University, Jeju-si 63243, Republic of KoreaGene Engineering Division, Department of Agricultural Biotechnology, National Institute of Agricultural Science, Jeonju-si 55365, Republic of KoreaDrought is being annually exacerbated by recent global warming, leading to crucial damage of crop growth and final yields. Soybean, one of the most consumed crops worldwide, has also been affected in the process. The development of a resistant cultivar is required to solve this problem, which is considered the most efficient method for crop producers. To accelerate breeding cycles, genetic engineering and high-throughput phenotyping technologies have replaced conventional breeding methods. However, the current novel phenotyping method still needs to be optimized by species and varieties. Therefore, we aimed to assess the most appropriate and effective phenotypes for evaluating drought stress by applying a high-throughput image-based method on the nested association mapping (NAM) population of soybeans. The acquired image-based traits from the phenotyping platform were divided into three large categories—area, boundary, and color—and demonstrated an aspect for each characteristic. Analysis on categorized traits interpreted stress responses in morphological and physiological changes. The evaluation of drought stress regardless of varieties was possible by combining various image-based traits. We might suggest that a combination of image-based traits obtained using computer vision can be more efficient than using only one trait for the precision agriculture.https://www.mdpi.com/2223-7747/12/12/2331abiotic stress responsedigital image analysisimage processingphenotyping platform systemRGB phenotyping |
| spellingShingle | JaeYoung Kim Chaewon Lee JiEun Park Nyunhee Kim Song-Lim Kim JeongHo Baek Yong-Suk Chung Kyunghwan Kim Comparison of Various Drought Resistance Traits in Soybean (<i>Glycine max</i> L.) Based on Image Analysis for Precision Agriculture Plants abiotic stress response digital image analysis image processing phenotyping platform system RGB phenotyping |
| title | Comparison of Various Drought Resistance Traits in Soybean (<i>Glycine max</i> L.) Based on Image Analysis for Precision Agriculture |
| title_full | Comparison of Various Drought Resistance Traits in Soybean (<i>Glycine max</i> L.) Based on Image Analysis for Precision Agriculture |
| title_fullStr | Comparison of Various Drought Resistance Traits in Soybean (<i>Glycine max</i> L.) Based on Image Analysis for Precision Agriculture |
| title_full_unstemmed | Comparison of Various Drought Resistance Traits in Soybean (<i>Glycine max</i> L.) Based on Image Analysis for Precision Agriculture |
| title_short | Comparison of Various Drought Resistance Traits in Soybean (<i>Glycine max</i> L.) Based on Image Analysis for Precision Agriculture |
| title_sort | comparison of various drought resistance traits in soybean i glycine max i l based on image analysis for precision agriculture |
| topic | abiotic stress response digital image analysis image processing phenotyping platform system RGB phenotyping |
| url | https://www.mdpi.com/2223-7747/12/12/2331 |
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