Xiaodong Bai;Susong Gu;Pichao Liu;An‐Gang Yang;Zucong Cai;Jianjun Wang;Jianguo Ya

Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, Jiangsu, Chin;School of Computer Science and Technology, Hainan University, Haikou 570228, Hainan, China;Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, Jiangsu, China;Agricultural Meteorological Center, Jiangxi Meteorological Bureau, Nanchang 330045, Jiangxi, China

Attention mechanism;Deep learning;Plant counting;Precision agriculture;Ric

Rice is a major food crop and is planted worldwide. Climatic deterioration, population growth, farmland shrinkage, and other factors have necessitated the application of cutting-edge technology to achieve accurate and efficient rice production. In this study, we mainly focus on the precise counting of rice plants in paddy field and design a novel deep learning network, RPNet, consisting of four modules: feature enco-der, attention block, initial density map generator, and attention map generator. Additionally, we propose a novel loss function called RPloss. This loss function considers the magnitude relationship between dif-ferent sub-loss functions and ensures the validity of the designed network. To verify the proposed method, we conducted experiments on our recently presented URC dataset, which is an unmanned aerial vehicle dataset that is quite challenged at counting rice plants. For experimental comparison, we chose some popular or recently proposed counting methods, namely MCNN, CSRNet, SANet, TasselNetV2, and FIDTM. In the experiment, the mean absolute error (MAE), root mean squared error (RMSE), relative MAE (rMAE) and relative RMSE (rRMSE) of the proposed RPNet were 8.3, 11.2, 1.2% and 1.6%, respectively, for the URC dataset. RPNet surpasses state-of-the-art methods in plant counting. To verify the universal-ity of the proposed method, we conducted experiments on the well-know MTC and WED datasets. The final results on these datasets showed that our network achieved the best results compared with excel-lent previous approaches. The experiments showed that the proposed RPNet can be utilized to count rice plants in paddy fields and replace traditional methods.(c) 2023 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).