Authors: Ms. Komala R, Shreya Sankannavar,
Abstract: The farming industry is a mainstay of the world economy, with potato cultivation contributing immensely to food security. Despite this, potato plants are very prone to several diseases like Earl y Blight, Late Blight, and bacterial infections, causing them to experience tremendous losses in yields. Conventional methods of disease detection involve the use of manual checking, which is time-consuming, labor-intensive, and inaccurate because of human error. To overcome such challenges, the project suggests a mac hine learning-based automatic potato disease detection system. The suggested system applies image processing and deep learning models to identify and classify diseases from leaf images with high accuracy. A dataset of healthy and diseased potato leaf images is preprocessed and utilized for training a convolutional neural network (CNN) model. The model is trained to classify different diseases and healthy leaves by learning from visual attributes. After training, the model detects diseases with high accuracy in real- time, allowing timely intervention and minimizing crop loss. This framework can help farmers and agricultural professionals keep track of crop health more effectively, increasing productivity and encouraging sustainable agriculture. The project indicates the use of machine learning in precision farming and how it can revolutionize conventio nal farming practices.
DOI: http://doi.org/
