Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images
Rice (Oryza sativa L.) is one of the most important cereals, which provides 20% of the world’s food energy. However, its productivity is poorly assessed especially in the global South. Here, we provide a first study to perform a deep-learning-based approach for instantaneously estimating rice yield...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/132700 |
| _version_ | 1855528256510361600 |
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| author | Tanaka, Y. Watanabe, T. Katsura, K. Tsujimoto, Y. Takai, T. Tanaka, T.S.T. Kawamura, K. Saito, H. Homma, K. Ahouanton, K. Ibrahim, A. Senthilkumar, K. Semwal, V.K. Matute, E.J.G. Corredor, E. El-Namaky, R. Manigbas, N. Quilang, E.J.P. Iwahashi, Y. Nakajima, K. Takeuchi, E. Saito, K. Mairoua, S.G. |
| author_browse | Ahouanton, K. Corredor, E. El-Namaky, R. Homma, K. Ibrahim, A. Iwahashi, Y. Katsura, K. Kawamura, K. Mairoua, S.G. Manigbas, N. Matute, E.J.G. Nakajima, K. Quilang, E.J.P. Saito, H. Saito, K. Semwal, V.K. Senthilkumar, K. Takai, T. Takeuchi, E. Tanaka, T.S.T. Tanaka, Y. Tsujimoto, Y. Watanabe, T. |
| author_facet | Tanaka, Y. Watanabe, T. Katsura, K. Tsujimoto, Y. Takai, T. Tanaka, T.S.T. Kawamura, K. Saito, H. Homma, K. Ahouanton, K. Ibrahim, A. Senthilkumar, K. Semwal, V.K. Matute, E.J.G. Corredor, E. El-Namaky, R. Manigbas, N. Quilang, E.J.P. Iwahashi, Y. Nakajima, K. Takeuchi, E. Saito, K. Mairoua, S.G. |
| author_sort | Tanaka, Y. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Rice (Oryza sativa L.) is one of the most important cereals, which provides 20% of the world’s food energy. However, its productivity is poorly assessed especially in the global South. Here, we provide a first study to perform a deep-learning-based approach for instantaneously estimating rice yield using red-green-blue images. During ripening stage and at harvest, over 22,000 digital images were captured vertically downward over the rice canopy from a distance of 0.8 to 0.9 m at 4,820 harvesting plots having the yield of 0.1 to 16.1 t·ha−1 across 6 countries in Africa and Japan. A convolutional neural network applied to these data at harvest predicted 68% variation in yield with a relative root mean square error of 0.22. The developed model successfully detected genotypic difference and impact of agronomic interventions on yield in the independent dataset. The model also demonstrated robustness against the images acquired at different shooting angles up to 30° from right angle, diverse light environments, and shooting date during late ripening stage. Even when the resolution of images was reduced (from 0.2 to 3.2 cm·pixel−1 of ground sampling distance), the model could predict 57% variation in yield, implying that this approach can be scaled by the use of unmanned aerial vehicles. Our work offers low-cost, hands-on, and rapid approach for high-throughput phenotyping and can lead to impact assessment of productivity-enhancing interventions, detection of fields where these are needed to sustainably increase crop production, and yield forecast at several weeks before harvesting. |
| format | Journal Article |
| id | CGSpace132700 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1327002025-11-12T04:57:59Z Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images Tanaka, Y. Watanabe, T. Katsura, K. Tsujimoto, Y. Takai, T. Tanaka, T.S.T. Kawamura, K. Saito, H. Homma, K. Ahouanton, K. Ibrahim, A. Senthilkumar, K. Semwal, V.K. Matute, E.J.G. Corredor, E. El-Namaky, R. Manigbas, N. Quilang, E.J.P. Iwahashi, Y. Nakajima, K. Takeuchi, E. Saito, K. Mairoua, S.G. energy data rice food systems Rice (Oryza sativa L.) is one of the most important cereals, which provides 20% of the world’s food energy. However, its productivity is poorly assessed especially in the global South. Here, we provide a first study to perform a deep-learning-based approach for instantaneously estimating rice yield using red-green-blue images. During ripening stage and at harvest, over 22,000 digital images were captured vertically downward over the rice canopy from a distance of 0.8 to 0.9 m at 4,820 harvesting plots having the yield of 0.1 to 16.1 t·ha−1 across 6 countries in Africa and Japan. A convolutional neural network applied to these data at harvest predicted 68% variation in yield with a relative root mean square error of 0.22. The developed model successfully detected genotypic difference and impact of agronomic interventions on yield in the independent dataset. The model also demonstrated robustness against the images acquired at different shooting angles up to 30° from right angle, diverse light environments, and shooting date during late ripening stage. Even when the resolution of images was reduced (from 0.2 to 3.2 cm·pixel−1 of ground sampling distance), the model could predict 57% variation in yield, implying that this approach can be scaled by the use of unmanned aerial vehicles. Our work offers low-cost, hands-on, and rapid approach for high-throughput phenotyping and can lead to impact assessment of productivity-enhancing interventions, detection of fields where these are needed to sustainably increase crop production, and yield forecast at several weeks before harvesting. 2023 2023-11-03T11:09:42Z 2023-11-03T11:09:42Z Journal Article https://hdl.handle.net/10568/132700 en Open Access application/pdf Elsevier Tanaka, Y., Watanabe, T., Katsura, K., Tsujimoto, Y., Takai, T., Tanaka, T.S.T., Kawamura, K., Saito, H., Homma, K., Mairoua,S.G., Ahouanton, K., Ibrahim, A., Senthilkumar, K., Semwal, V.K., Matute, E.J.G., Corredor, E., El-Namaky, R., Manigbas, N., Quilang, E.J.P,. Iwahashi, Y., Nakajima, K., Takeuchi, E. and Saito, K. 2023. Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images. Plant Phenomics 5:0073. |
| spellingShingle | energy data rice food systems Tanaka, Y. Watanabe, T. Katsura, K. Tsujimoto, Y. Takai, T. Tanaka, T.S.T. Kawamura, K. Saito, H. Homma, K. Ahouanton, K. Ibrahim, A. Senthilkumar, K. Semwal, V.K. Matute, E.J.G. Corredor, E. El-Namaky, R. Manigbas, N. Quilang, E.J.P. Iwahashi, Y. Nakajima, K. Takeuchi, E. Saito, K. Mairoua, S.G. Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images |
| title | Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images |
| title_full | Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images |
| title_fullStr | Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images |
| title_full_unstemmed | Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images |
| title_short | Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images |
| title_sort | deep learning enables instant and versatile estimation of rice yield using ground based rgb images |
| topic | energy data rice food systems |
| url | https://hdl.handle.net/10568/132700 |
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