Lightweight Tomato Crop Disease Detection Model Based on Residual Connections

Abstract:

Tomato crops are prone to be attacked by various diseases during their growth process. The computational load of disease detection strategies based on deep learning models was usually substantial. To address this issue, a lightweight deep learning model called ResDepSepNet was proposed. This model was constructed based on residual modules to alleviate the gradient vanishing problem that may occur during model training, thereby improving the overall performance of the model. To reduce the model’s computational load, depthwise separable convolutions were introduced, and downsampling was achieved by increasing the stride of the convolutional operations. Additionally, an squeeze-and-excitation (SE) attention module was introduced to enable the model to focus more on feature information crucial for disease identification, thereby enhancing its disease recognition capability. The ResDepSepNet model was tested by using the PlantVillage tomato disease dataset, and the test results were compared with the MobileNetV2 model and the TrioConvTomatoNet model. The test results showed that the overall accuracy of the ResDepSepNet model was 4.8 and 1.1 percentage points higher than that of the MobileNetV2 and TrioConvTomatoNet models, respectively. Moreover, its floating-point operations count was merely 3.5×107, approximately 1/18 and 1/7 of those of the MobilenetV2 and TrioConvTomatoNet models, respectively. The research result can provide a technical reference for disease detection in tomato crops.