Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images
Yu Tanaka, Tomoya Watanabe, Keisuke Katsura, Yasuhiro Tsujimoto, Toshiyuki Takai, Takashi Tanaka, Kensuke Kawamura, Hiroki Saito, Koki Homma, Salifou Goube Mairoua, Kokou Ahouanton, Ali Ibrahim, Kalimuthu Senthilkumar, Vimal Kumar Semwal, Eduardo Matute, Edgar Corredor, Raafat El‐Namaky, Norvie L. Manigbas, Eduardo Jimmy Quilang, Yu Iwahashi, Kota Nakajima, Eisuke Takeuchi, Kazuki Saito
Abstract
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.