A Deep Classification and Detection Model for Potato Disese

Potatoes are among the most significant crops worldwide, with their cultivation becoming increasingly popular over recent decades. However, potato production is frequently hindered by various diseases, which elevate production costs for farmers and impact their livelihoods. An efficient, automated disease detection system is crucial to enhancing potato yield and modernizing agricultural practices. Our primary idea is that of diagnosing potato diseases via images of potato leaves using the assistance of advanced machine learning technologies. The project introduces a solution based on machine learning to identify and classify potato leaf disease. Image processing proves to be a highly effective method for detecting these conditions. Here, we have utilized a dataset of over 4,634 images diseased potato leaves, obtained from the publicly accessible Plant Village dataset on Kaggle. We employed several pre-trained models for disease recognition and classification. Our approach got an impressive accuracy of 99% when evaluated on a test set having 25% of the data, with the remaining 75% used for training. The findings from our study show that machine learning significantly outperforms traditional ideas in detecting potato diseases. Our system offers are liable tool for farmers to ensure healthier crops and increased productivity, paving the way for improved disease management in potato cultivation.