Detecting Bean Crop Health: A ResNet50-based Approach to Disease Classification

This research investigates the application of deep learning techniques, specifically the ResNet50 model, for the detection and classification of bean leaf diseases. By analyzing a dataset of bean leaf images, the model effectively classifies leaf conditions into healthy, angular leaf spot, bean rust, and unknown categories, achieving an accuracy of 94%. The study demonstrates the potential of deep learning in enhancing the efficiency and accuracy of disease detection in bean crops. By enabling early detection and diagnosis, this approach can significantly reduce crop losses due to diseases, leading to improved agricultural productivity and sustainable farming practices.