Detecting Common Diseases of Potato Leaf Applying Deep Learning Techniques

Potatoes are a widely recognized vegetable that has been cultivated in India for several decades. However, diseases like early blight and late blight have negatively impacted potato production, increasing production costs. We intend to create an automated and effective disease diagnosis mechanism utilizing potato leaf images to overcome this issue and digitize the system. Our primary goal is to employ a convolutional neural network (CNN) algorithm for diagnosing potato diseases. This research paper proposes a machine learning and image processing-based automated system for detecting and classifying potato leaf diseases. Image processing techniques provide an excellent approach for disease detection and analysis. In this analysis, we divide the pictures into categories: healthy and unhealthy potato leaves. We have collected over 2152 pictures from various sources, including Kaggle, and utilized pre-trained models for accurate recognition and classification of healthy and diseased leaves. The program achieves an impressive accuracy of 99.13%. By making use of advanced technology and machine learning algorithms, we can significantly improve potato production and combat the negative impact of diseases. In the end, this method increases productivity and yields in the potato farming sector by providing a scalable and effective means of detecting diseases in potato crops.