Plant Image Recognition and Disease Prediction using CNN

India's economy is heavily reliant on agriculture, ranking among the top crop producers globally. With farm production contributing approximately 18.1 percent to the nation's GDP, it holds a pivotal role in the agribusiness sector. Challenges such as unproductive farmland and crop diseases hinder annual food production rates. Accurately detecting crop infections is challenging, as inaccuracies could lead to decreased yields and diminished market value. Innovative approaches are thus crucial to facilitate early detection and the development of resilient crops. This study aims to integrate agricultural practices with artificial intelligence to mitigate losses from plant leaf infections. Crops like apples, potatoes, cherries, and grapes exhibit distinct disease symptoms, enabling prompt identification and preventive actions. Employing four models utilizing various CNN architectures—VGG16, VGG19, InceptionV3, and ResNet50—we aimed to identify and predict plant diseases. Through trials on the standard PlantVillage dataset, VGG19 demonstrated superior accuracy compared to other models. We categorized the data into 38 classes for prediction purposes. This proposed model targets agriculture and agro- based businesses, empowering farmers with early intervention strategies to enhance crop productivity.