A hybrid deep learning and fuzzy logic framework for robust tomato disease detection and classification.

Abstract

Early and precise diagnosis of diseases in tomato plants is critical in ensuring productivity in agriculture and reducing losses caused by diseases. Classical classification approaches, however, are often limited by different image quality, different lighting, low resolution, and imbalanced classes. In order to confront these issues, this paper suggests a hybrid ensemble model that integrates the advantages of deep learning, fuzzy logic, and a generative model to classify diseases successfully. The suggested approach combines three strong convolutional neural networks, ResNet-50, EfficientNet-B0, and DenseNet-121, into the adaptive ensemble system. Individual models are combined to form the final decision depending on the predictive accuracy and confidence level. The use of fuzzy logic to refine intelligently the decision-making process provides more flexibility than the decisions of the static ensemble methods. A Conditional Generative Adversarial Network (C-GAN) is used to alleviate the problem of class imbalance and overfitting through the production of multiple synthetic images of high quality. This, in turn, significantly enhances the generalization of models and even gives a balanced representation of the disease’s classes. The hybrid structure achieved a classification accuracy of 99.19% when tested on the PlantVillage dataset and outperformed the traditional ensemble and classical techniques. The results highlight the potential of the hybrid approach for real-world agricultural applications. This offers a scalable, accurate, and intelligent solution for automated plant disease diagnosis. This study contributes a novel, interpretable, and performance-driven model that can support sustainable agriculture through timely and precise disease management in tomato crops.

Plant phenotyping relevance

トマト葉画像から病害状態を分類する深層学習・ファジー論理・生成モデルの統合手法を開発し、PlantVillage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す。

Code and data availability

The paper uses four public tomato leaf image datasets (PlantVillage, Tomato Leaves, PlantDoc, Tomato-Village) plus two Mendeley-hosted datasets for robustness evaluation, and declares a public GitHub repository of C-GAN-generated tomato crop images under Code availability. No trained model checkpoints or analysis code/