Tomato Leaf Disease Identification Method Based on Improved YOLOX

Abstract

In tomato leaf disease identification tasks, the high cost and consumption of deep learning-based recognition methods affect their deployment and application on embedded devices. In this study, an improved YOLOX-based tomato leaf disease identification method is designed. To address the issue of positive and negative sample imbalance, the sample adaptive cross-entropy loss function (LBCE−β) is proposed as a confidence loss, and MobileNetV3 is employed instead of the YOLOX backbone for lightweight model feature extraction. By introducing CBAM (Convolutional Block Attention Module) between the YOLOX backbone and neck network, the model’s feature extraction performance is increased. CycleGAN is used to enhance the data of tomato disease leaf samples in the PlantVillage dataset, solving the issue of an imbalanced sample number. After data enhancement, simulation experiments and field tests revealed that the YOLOX’s accuracy improved by 1.27%, providing better detection of tomato leaf disease samples in complex environments. Compared with the original model, the improved YOLOX model occupies 35.34% less memory, model detection speed increases by 50.20%, and detection accuracy improves by 1.46%. The enhanced network model is quantized by TensorRT and works at 11.1 FPS on the Jetson Nano embedded device. This method can provide an efficient solution for the tomato leaf disease identification system.