Biomass estimation of World rice (Oryza sativa L.) core collection based on the convolutional neural network and digital images of canopy
ABSTRACTAbove-ground biomass (AGB) is an important indicator of crop productivity. Destructive measurements of AGB incur huge costs, and most non-destructive estimations cannot be applied to diverse cultivars having different canopy architectures. This insufficient access to AGB data has potentially...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-04-01
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| Series: | Plant Production Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/1343943X.2023.2210767 |
| _version_ | 1797805366376923136 |
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| author | Kota Nakajima Yu Tanaka Keisuke Katsura Tomoaki Yamaguchi Tomoya Watanabe Tatsuhiko Shiraiwa |
| author_facet | Kota Nakajima Yu Tanaka Keisuke Katsura Tomoaki Yamaguchi Tomoya Watanabe Tatsuhiko Shiraiwa |
| author_sort | Kota Nakajima |
| collection | DOAJ |
| description | ABSTRACTAbove-ground biomass (AGB) is an important indicator of crop productivity. Destructive measurements of AGB incur huge costs, and most non-destructive estimations cannot be applied to diverse cultivars having different canopy architectures. This insufficient access to AGB data has potentially limited improvements in crop productivity. Recently, a deep learning technique called convolutional neural network (CNN) has been applied to estimate crop AGB due to its high capacity for digital image recognition. However, the versatility of the CNN-based AGB estimation for diverse cultivars is still unclear. We established and evaluated a CNN-based estimation method for rice AGB using digital images with 59 diverse cultivars which were mostly in World Rice Core Collection. Across two years at two locations, we took 12,183 images of 59 cultivars with commercial digital cameras and manually obtained their corresponding AGB. The CNN model was established by using 28 cultivars and showed high accuracy (R2 = 0.95) to the test dataset. We further evaluated the performance of the CNN model by using 31 cultivars, which were not in the model establishment. The CNN model successfully estimated AGB when the observed AGB was lesser than 924 g m−2 (R2 = 0.87), whereas it underestimated AGB when the observed AGB was greater than 924 g m−2 (R2 = 0.02). This underestimation might be improved by adding training data with a greater AGB in further study. The present study indicates that this CNN-based estimation method is highly versatile and could be a practical tool for monitoring crop AGB in diverse cultivars. |
| first_indexed | 2024-03-13T05:51:00Z |
| format | Article |
| id | doaj.art-8fbfb42666a541059f48805e73e21a1c |
| institution | Directory Open Access Journal |
| issn | 1343-943X 1349-1008 |
| language | English |
| last_indexed | 2024-03-13T05:51:00Z |
| publishDate | 2023-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Plant Production Science |
| spelling | doaj.art-8fbfb42666a541059f48805e73e21a1c2023-06-13T13:51:14ZengTaylor & Francis GroupPlant Production Science1343-943X1349-10082023-04-0126218719610.1080/1343943X.2023.2210767Biomass estimation of World rice (Oryza sativa L.) core collection based on the convolutional neural network and digital images of canopyKota Nakajima0Yu Tanaka1Keisuke Katsura2Tomoaki Yamaguchi3Tomoya Watanabe4Tatsuhiko Shiraiwa5Graduate School of Agriculture, Kyoto University, Kyoto, JapanGraduate School of Environmental, Life, Natural Science and Technology, Okayama University, Okayama, JapanUnited Graduate School of Agriculture Science, Tokyo University of Agriculture and Technology, Fuchu, Tokyo, JapanUnited Graduate School of Agriculture Science, Tokyo University of Agriculture and Technology, Fuchu, Tokyo, JapanIndependent researcher, Tokyo, JapanGraduate School of Agriculture, Kyoto University, Kyoto, JapanABSTRACTAbove-ground biomass (AGB) is an important indicator of crop productivity. Destructive measurements of AGB incur huge costs, and most non-destructive estimations cannot be applied to diverse cultivars having different canopy architectures. This insufficient access to AGB data has potentially limited improvements in crop productivity. Recently, a deep learning technique called convolutional neural network (CNN) has been applied to estimate crop AGB due to its high capacity for digital image recognition. However, the versatility of the CNN-based AGB estimation for diverse cultivars is still unclear. We established and evaluated a CNN-based estimation method for rice AGB using digital images with 59 diverse cultivars which were mostly in World Rice Core Collection. Across two years at two locations, we took 12,183 images of 59 cultivars with commercial digital cameras and manually obtained their corresponding AGB. The CNN model was established by using 28 cultivars and showed high accuracy (R2 = 0.95) to the test dataset. We further evaluated the performance of the CNN model by using 31 cultivars, which were not in the model establishment. The CNN model successfully estimated AGB when the observed AGB was lesser than 924 g m−2 (R2 = 0.87), whereas it underestimated AGB when the observed AGB was greater than 924 g m−2 (R2 = 0.02). This underestimation might be improved by adding training data with a greater AGB in further study. The present study indicates that this CNN-based estimation method is highly versatile and could be a practical tool for monitoring crop AGB in diverse cultivars.https://www.tandfonline.com/doi/10.1080/1343943X.2023.2210767Above-ground biomassBiomass estimationConvolutional neural networkDigital imageRiceWorld rice core collection |
| spellingShingle | Kota Nakajima Yu Tanaka Keisuke Katsura Tomoaki Yamaguchi Tomoya Watanabe Tatsuhiko Shiraiwa Biomass estimation of World rice (Oryza sativa L.) core collection based on the convolutional neural network and digital images of canopy Plant Production Science Above-ground biomass Biomass estimation Convolutional neural network Digital image Rice World rice core collection |
| title | Biomass estimation of World rice (Oryza sativa L.) core collection based on the convolutional neural network and digital images of canopy |
| title_full | Biomass estimation of World rice (Oryza sativa L.) core collection based on the convolutional neural network and digital images of canopy |
| title_fullStr | Biomass estimation of World rice (Oryza sativa L.) core collection based on the convolutional neural network and digital images of canopy |
| title_full_unstemmed | Biomass estimation of World rice (Oryza sativa L.) core collection based on the convolutional neural network and digital images of canopy |
| title_short | Biomass estimation of World rice (Oryza sativa L.) core collection based on the convolutional neural network and digital images of canopy |
| title_sort | biomass estimation of world rice oryza sativa l core collection based on the convolutional neural network and digital images of canopy |
| topic | Above-ground biomass Biomass estimation Convolutional neural network Digital image Rice World rice core collection |
| url | https://www.tandfonline.com/doi/10.1080/1343943X.2023.2210767 |
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