Plant leaf disease prediction system using CNN

Plant diseases pose a significant threat to the livelihoods of smallholder farmers,impacting both income and food security. However, the widespread adoption of smartphonesand advancements in computer vision have opened up new possibilities for image-baseddisease diagnosis in agriculture. Convolutional Neural Networks (CNNs) are at the forefrontof image recognition technologies and have demonstrated their potential in providing accurateand timely disease identification.This study explores the performance of a pre-trained ResNet34 model for detecting plantdiseases. The model was trained and validated using a dataset comprising 8,685 leaf images,captured under controlled conditions, and is designed to identify seven distinct plant diseasesas well as differentiate healthy leaf tissue. The developed system is deployed as a web-basedapplication, enabling users to access its diagnostic capabilities conveniently