Potato Crop Disease Prediction using Deep Learning

Abstract

Numerous plant diseases have distinctive visual signs that can be used to recognize and categorize them. Identifying defects in food goods, especially potatoes, requires the use of machine vision and image processing techniques. The claim that potatoes are the most popular vegetable in the world, as made by a rising number of agricultural authorities, are taken into consideration by many countries. Despite the fanfare, potatoes are severely harmed by potato leaf diseases. Many different diseases, including early blight, late blight, and others, will attack potato plants and show their symptoms in the leaves. If these outbreaks are discovered at the beginning stage and timely treatment is considered, the farmer would not be at risk of suffering significant financial losses. The suggested model would effectively identify and diagnose potato leaf stand illnesses using image processing techniques. The Convolutional Neural Network model is utilized in this study to determine the disease from photos of the potato leaf. CNN is used for image classification and performs better than other algorithms in machine learning. These images are then examined using the algorithm provided, and the potato crop leaf is classified as either standard or unhealthy. This promising result led to 96.8% precision, which is highly significant.