Zhang Yuhan, Liu Dongsheng
School of Information Science and Technology, Harbin Institute of Technology (Weihai), Weihai, 264200, China.
School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China.
Sci Rep. 2025 May 5;15(1):15624. doi: 10.1038/s41598-025-99027-3.
Complex pest and disease features appearing during the growth of wheat crops are difficult to capture and can seriously affect the normal growth of wheat crops. The existing methods ignore the full pre-interaction of deep and shallow features, which largely affects the accuracy of identification. To address the above problems and needs, we rethink the feature representation and attention mechanism in intelligent recognition of wheat leaf diseases and pests, and propose a representation and recognition network (RReNet) based on the feature attention mechanism. RReNet captures key information more efficiently by focusing on complex pest and disease characteristics and fusing multi-semantic feature information. In addition, RReNet further enhances the perception of complex disease and pest features by using four layers of detection units and fast IoU loss function, which significantly improves the accuracy and robustness of wheat leaf disease and pest recognition. Tests on a challenging wheat leaf pest and disease dataset with twelve pest and disease types show that RReNet achieves precision, recall and mAP as high as 94.1%, 95.7% and 98.3% respectively. Also, ablation experiments proved the effectiveness of all parts of the proposed method.
小麦作物生长过程中出现的复杂病虫害特征难以捕捉，严重影响小麦作物的正常生长。现有方法忽略了深层和浅层特征的充分预交互，这在很大程度上影响了识别的准确性。为了解决上述问题和需求，我们重新思考了小麦叶部病虫害智能识别中的特征表示和注意力机制，提出了一种基于特征注意力机制的表示与识别网络（RReNet）。RReNet通过聚焦复杂病虫害特征并融合多语义特征信息，更高效地捕捉关键信息。此外，RReNet利用四层检测单元和快速IoU损失函数进一步增强了对复杂病虫害特征的感知，显著提高了小麦叶部病虫害识别的准确性和鲁棒性。在一个具有十二种病虫害类型的具有挑战性的小麦叶部病虫害数据集上的测试表明，RReNet的精确率、召回率和平均精度均值分别高达94.1%、95.7%和98.3%。同时，消融实验证明了所提方法各部分的有效性。
