Abstract
Detecting diseases in tomato leaves at an early stage is crucial for preventing crop damage and improving food security. Traditional diagnostic methods are often inefficient, requiring significant expertise and time. To address this challenge, we explore AI-driven approaches, integrating DL and ML methods for automated disease detection. This study employs CNNs, specifically leveraging the VGG16 architecture for feature extraction. Additionally, we compare its effectiveness with classical classifiers such as KNN and SVM. Using a publicly available dataset of healthy and diseased tomato leaves, our results indicate that CNN-based models outperform conventional machine learning classifiers in both accuracy and efficiency. Moreover, integrating IoT-based analytics enhances early detection, reducing crop losses and promoting sustainable agricultural practices.
