Classification of Plant Diseases With ResNet-GAN Integration: Comparative Analysis of Machine Learning And Deep Learning Methods

Abstract

Accurate and effective classification of plant diseases is critical for increasing yield and quality in agricultural production, minimizing economic losses through early detection of diseases, and implementing sustainable agriculture approaches. This study presents an approach for detecting and classifying plant leaf diseases. We compare the performance of machine learning and deep learning-based models, and we use GAN-based data synthesis methods on a dataset we created to improve the model performance. ResNet-based feature extraction is performed for machine learning methods, and XGBoost, Random Forest, SVM, and InceptionV3 models are evaluated. In contrast, AlexNet, VGG16, VGG19, DenseNet, and ResNet models are examined within the scope of deep learning. The study was analyzed in three classes: Phytophthora Infestans, Potassium Deficiency, and Healthy, and tested on data obtained from 21 different plant species. According to the model performances obtained, the deep learning-based ResNet model showed the highest success in all performance metrics and achieved 98% accuracy, showing superior performance compared to other methods. In the study, a comprehensive evaluation of multiple classification, GAN-based data synthesis, machine learning, and deep learning models was carried out. A valuable contribution was made to the existing studies in the literature.

Keywords

• Plant disease classification
• Data synthesis
• Deep learning
• Machine learning
• Image processing

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