Implementation of Custom-Based Mobile-Network Model for Early Blight Detection in Tomatoes

This study introduces an advanced framework for plant disease detection, specifically classifying tomato images into “Early Blight” and “Healthy” categories. Utilizing a fusion of artificial intelligence and computer vision, the research employs the MobileNet architecture enriched with custom convolutional layers for enhanced feature extraction. The model's adaptability to different dataset sizes highlights its robustness, with performance benchmarks indicating up to 100% accuracy using classifiers like Random Forest, SVM, and Gradient Boosting. The framework further leverages ensemble classifiers to refine prediction accuracy, addressing the real-world complexities of variable lighting and environmental conditions. In its entirety, the research offers a scalable, accurate, and systematic approach to automated plant disease detection, with implications for bolstering global food security and sustainable agriculture.