LEAF DISEASE DETECTION USING DEEP LEARNING

Potato is a crucial global crop, susceptible to various diseases, especially Early Blight and Late Blight, which significantly impact yield and quality. By leveraging a Kaggle dataset containing potato leaf images, Detecting diseases at an early stage prevents the spread and reduces financial losses for farmers. Early disease detection is crucial for minimizing crop loss and preventing economic damage. This research contributes to smart farming by enhancing the precision and efficiency. The model’s performance was evaluated through metrics such as accuracy, training, and validation loss. However, the current model is limited to detecting only two specific diseases, highlighting the need for further improvements. The approach demonstrates potential for integrating AI in smart farming to enhance agricultural productivity. With the ever-increasing demand for sustainable agriculture, early and accurate disease detection is crucial to mitigate crop losses and reduce the reliance on harmful pesticides. It represents a significant advancement in precision agriculture, contributing to enhanced food security and environmental sustainability.