Potato Disease Detection Using Deep Learning

Utilising deep learning methods, such as convolutional neural networks (CNNs), to recognise and categorise diseases affecting potato plants is known as “potato disease detection using deep learning.” To identify patterns and features indicating various diseases., deep learning models are trained on enormous datasets of potato plant photos, including both healthy and damaged plants. The deep learning models can precisely classify and detect the existence of numerous potato illnesses by examining the visual characteristics of the leaves or other sections of the plant. Using deep learning-based methods, it may be possible to accurately and automatically identify potato diseases, which will be helpful for early disease detection, disease prevention, and crop management. These techniques take advantage of deep learning algorithms, abilities to extract complex representations from unprocessed visual data and generate predictions based on discovered patterns. Researchers and farmers can monitor and identify diseases more effectively with the aid of deep learning, which will increase agricultural productivity and plant health.