Objective  To solve the insufficient identification of fine-grained tomato diseases in greenhouse.

Method  Taking tomato leaves with five early or late diseases as research objects, we proposed a new convolutional neural network model ARNet based on the combination of attention and residual thought. A multi-layered attention module was introduced to solve the problem of early disease location dispersion and the difficulty of feature extraction by extracting hierarchically disease classification information. In order to avoid the degradation of network training, we constructed a residual module to effectively integrate high- and low-order features. Meantime, we introduced the data expansion technology to prevent model over-fitting.

Result  Model training and testing results of early and late disease leaf datasets with 44 295 pictures showed that ARNet has better classification performance with an average recognition accuracy of 88.2%, which was significantly higher than those of other existing models. In addition, the identification accuracy of ARNet for early disease was significantly better than that for late disease, which verified the effectiveness of attention mechanism in extracting fine region features, and there was no excessive jitter during training process.

Conclusion  This model proposed in this paper has strong robustness and high stability, and can provide a reference for intelligent diagnosis of fine-grained tomato diseases in practical application.