Potato Disease Prediction using Deep Learning

Abstract

This research paper introduces deep learning to find an early solution to potato disease, especially for early blight and late blight. This research also analyses in detail the existing work related to potato maladies, machine learning applications in farming, and the integration of profound learning for plant wellbeing monitoring. The research integrates deep learning models with user-friendly web interface, help farmers identify and potato disease and therewith take action. The system has an education model, use server backend design Fast API, and a frontend website built with React. System architecture is designed accordingly both are practical and useful. Backend is designed to be used by Fast API, today's website be strong and work hard server-side operations. It supports backend systems learning models and Perform data processing tasks. After all, the website is designed to be used by React, Provide answers and insights interface for customers. A combination of these systems makes sure the platform is correct powerful and easy to use, this is true even for those of lesser intelligence. In addition to its diagnostic capabilities, the system also includes training products designed to help farmers learn about related disease products and best practices for managing them. This education model is broad entering the web interface, also provide useful information resources that can help disease prevention and control. In general, the research represents major advances in agricultural technology, provide advanced technology to farmers to improve crop health and productivity Through early detection and informed decision making.