Efficient Tomato Disease Detection Using MaxMin-Diffusion Mechanism and Lightweight Techniques.

Abstract

This paper proposes a disease detection model based on the maxmin-diffusion mechanism, aimed at improving the accuracy and robustness of disease detection tasks in the agricultural field. With the development of smart agriculture, automated disease detection has become one of the key tasks driving agricultural modernization. Traditional disease detection models often suffer from significant accuracy loss and robustness issues when dealing with complex disease types and dynamically changing time-series data. To address these problems, this paper introduces the maxmin-diffusion mechanism, which dynamically adjusts attention weights to enhance the model's focus on key disease regions while suppressing interference from irrelevant areas, significantly improving the segmentation accuracy of disease regions. Through a series of experiments, the proposed model demonstrates outstanding performance across various disease detection tasks. For bacterial spot disease detection, the model achieves a precision of 0.98, recall of 0.95, accuracy of 0.96, and mIoU of 0.96, indicating that it can efficiently and accurately identify disease regions even in complex backgrounds. Compared to traditional self-attention and CBAM mechanisms, the maxmin-diffusion mechanism shows significant advantages in fine-grained feature extraction and time-series data processing, particularly in the recognition of dynamically changing disease regions, where it exhibits higher detection accuracy and robustness. Furthermore, the model underwent lightweight optimization, enabling the proposed disease detection model to not only achieve high-precision detection but also run efficiently on resource-constrained mobile devices. This provides strong technical support for the application of smart agriculture.

Plant phenotyping relevance

トマト葉の病斑領域を画像から分割・検出する新規モデルを開発し、精度・頑健性・モバイル実行性を評価しており、植物の病害状態の取得方法が研究の中心である。

Code and data availability

The paper describes a self-collected tomato disease video/image dataset from Bayannur and a custom maxmin-diffusion model, but the supplied blocks contain no data availability statement, no public repository deposit, and no code/model release. The only URL present is the CC BY license notice, which is not a paper asset

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