Automated Fusarium Wilt Classification in Plants using VGG16 Architecture

Fusarium wilt, induced by pathogenic fungus, represents a substantial risk to world agriculture, impacting several plant species. Timely identification and precise classification of this disease are essential for optimal care and control. This research introduces an automated classification method using the VGG16 deep learning architecture to detect Fusarium wilt in plants. It collected an extensive dataset of images of healthy and diseased plant leaves. The VGG16 model, recognized for its depth and feature extraction proficiency, was fine-tuned and trained on this dataset. The model's efficacy was assessed using accuracy, precision, recall, and F1 score. The results indicated that the VGG16 architecture attained superior classification accuracy, improving conventional approaches. The proposed method enhances the detection process and offers a dependable instrument for farmers and agronomists to assess crop health. This method underscores the promise of deep learning methods in agricultural applications, facilitating future disease control and precision agriculture studies. Future efforts will concentrate on increasing the dataset and including real-time monitoring functionalities in the system.