EPDNet: A method for identifying tomato leaf diseases with uneven samples

Abstract

Tomato industry is one of the important parts of agriculture, and the timely diagnosis of leaf diseases plays a key role in ensuring its production safety. At present, most of the mainstream recognition technologies are based on laboratory standard images to construct recognition models. Although they can achieve accurate analysis of isolated leaves, it is difficult to cope with the practical challenges such as complex climate and environmental factors of plant growth in open environments. The recognition is difficult due to the influence factors of open environment, such as the distortion of disease spots caused by light, the occlusion of branches and leaves, and the confusion of soil attachment and real disease spots. To solve the problem of unbalanced distribution of tomato leaf disease samples in open environment and the big difference of similar diseases, this study proposes a single-modal recognition architecture for tomato leaf diseases based on Efficient localization and Physical information and Dynamic adaptive optimization Network (EPDNet). Firstly, an efficient positioning feature enhancement module is designed to effectively enhance the network’s attention to important regions by calculating and fusing the horizontal and vertical attention weights. Then, a physical information neural network-cross entropy hybrid loss function was designed to ensure the accuracy of prediction, while restricting the smoothness and continuity of the feature map to improve the robustness of the model. Finally, a dynamic adaptive optimization algorithm was designed to iteratively update the learning rate to improve the feature discrimination ability, so as to reduce the identification differences of diseases within the class. Experimental results show that the accuracy of EPDNet on the tomato leaf dataset reaches 93.62%, and the F1 score reaches 93.31 %, which is significantly better than the existing methods. This study provides an effective solution for the application of deep learning methods in crop diseases.

Plant phenotyping relevance

トマト葉の病斑・病害状態を画像から認識する深層学習手法を開発し、データセットで性能評価しているため、植物病害フェノタイピング手法が中心である。

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