New Method for Tomato Disease Detection Based on Image Segmentation and Cycle-GAN Enhancement.

Abstract

A major concern in data-driven deep learning (DL) is how to maximize the capability of a model for limited datasets. The lack of high-performance datasets limits intelligent agriculture development. Recent studies have shown that image enhancement techniques can alleviate the limitations of datasets on model performance. Existing image enhancement algorithms mainly perform in the same category and generate highly correlated samples. Directly using authentic images to expand the dataset, the environmental noise in the image will seriously affect the model's accuracy. Hence, this paper designs an automatic leaf segmentation algorithm (AISG) based on the EISeg segmentation method, separating the leaf information with disease spot characteristics from the background noise in the picture. This algorithm enhances the network model's ability to extract disease features. In addition, the Cycle-GAN network is used for minor sample data enhancement to realize cross-category image transformation. Then, MobileNet was trained by transfer learning on an enhanced dataset. The experimental results reveal that the proposed method achieves a classification accuracy of 98.61% for the ten types of tomato diseases, surpassing the performance of other existing methods. Our method is beneficial in solving the problems of low accuracy and insufficient training data in tomato disease detection. This method can also provide a reference for the detection of other types of plant diseases.

Plant phenotyping relevance

トマト葉の病斑を対象に、自動葉分割と画像拡張を組み合わせた病害検出手法を開発・評価しており、植物の病害状態の画像ベース推定が研究の中心である。

Code and data availability

The supplied blocks describe a tomato disease detection method using AISG segmentation, Cycle-GAN augmentation, and MobileNet, with a custom 18,318-image dataset. However, no blocks contain data availability, code availability, or supplementary deposit statements with author URLs. The PlantVillage dataset is cited pre-

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