CNN based Leaves Disease Detection in Potato Plant
Riya Verma, Rahul Mishra, Prachi Gupta, Pooja Pooja, Shivani Trivedi
Abstract
Potatoes are one of the most popular vegetables worldwide, but they are severely affected by potato leaf diseases such as early blight and late blight. Early detection and appropriate action are crucial to prevent substantial financial losses for farmers. In this study, we propose a technology that uses image processing methods to accurately detect and diagnose potato leaf diseases. The presented model employs CNN, a machine learning algorithm that performs better than others, for image classification. The model uses normal and disease-impacted potato leaves to differentiate between normal and abnormal potato leaf properties. After being analysed by the algorithm, the potato leaf is classified as normal or diseased. Our model achieves high precision with a 97% accuracy rate. This technology has the potential to reduce economic losses for potato farmers and improve the efficiency of disease detection in potato crops.