关键词: 番茄病害检测, 反馈连接, 特征金字塔网络, 空洞卷积, 多尺度

Abstract: Aiming at the problems of low accuracy of tomato disease identification due to overlapping leaves and small targets in complex environments, a multiscale cascade model (IMS-Cascade) is proposed. The model is based on cascade neural network (Cascade R-CNN), the switchable Atrous convolution of fused context information is introduced into the backbone network, and complex multiscale convolution kernels are used to extract target features to solve the problem that the shape of the same disease is greatly different due to leaf occlusion, and the feedback connection module is added to the feature pyramid networks, so that the model can extract features for many times and improve the utilization of shallow information. Finally, the gradient of accurate samples is increased in the loss function to reduce the influence of abnormal samples on the model. When the model is applied to a portion of the tomato leaf disease dataset published by Plant Village, the mean average precision (mAP) reaches 89.1% and the average precision reaches 99.36%. These results represent improvements of 2.5% and 1.84%, respectively, over the original Cascade R-CNN model. This indicates higher detection accuracy, which is beneficial for tomato disease detection in complex environments.

Key words: tomato disease detection, feedback connection, feature pyramid networks, dilated convolution, multiscale