Advancing Smart Farming through Research-Based Web Application Integration: An efficient deep learning approach for classifying disease of been leaf Disease Identification in Sustainable Agriculture

Abstract

Deep Learning (DL) has emerged as a powerful technology in modern agriculture, revolutionizing practices like precision farming and disease management. Traditional methods for detecting diseases in bean leaves are manual, timeconsuming, and require domain expertise, posing challenges in large-scale operations. Convolutional Neural Networks (cnns), supported by techniques like Transfer Learning (TL) and ensemble modeling, provide an automated, efficient, and scalable solution for disease classification. This research evaluates and compares the performance of state-of-the-art DL models, including VGG-19, resnet50, mobilenetv2, Vision Transformer (vit), and Xception, to classify bean leaf diseases effectively. VGG-19 achieved the highest accuracy and was deployed as a web-based application for real-time disease detection. This study demonstrates how integrating DL into agricultural workflows can enhance productivity, promote sustainability, and ensure global food security by offering a precise and scalable solution to identify and manage bean leaf diseases