关键词: 植物病害检测, 改进YOLOv8, 实时目标检测, 深度神经网络, 残差网络

Abstract: A high-performance real-time fine-grained plant disease detection framework is proposed to solve the problems of dense distribution, irregular shape, multi-scale target category and texture similarity encountered by existing identification methods in plant disease detection. Firstly, two new residual blocks are designed in YOLOv8 backbone network and neck to enhance feature extraction and reduce computing cost. Secondly, the DenseNet layer is introduced and the Hard-Swish function is used as the main activation function to improve the accuracy of the model. Finally, the PANet network is designed to retain finegrained local information and improve feature fusion. Four different diseases of tomato plants were detected in different complex environments, and the experimental results showed that the proposed model was superior to the most advanced detection models in both accuracy and speed. At the detection rate of 71.23 FPS, the model obtained the precision of 92.58%, the recall rate of 97.59%, and the F1score of 93.64%, which provided an effective technical means for precision agriculture automation.

Key words: plant disease detection, improved YOLOv8, realtime target detection, deep neural network, residual network