Potato Disease

Potato disease detection is a crucial area of research aiming to improve crop yields and food security by enabling rapid and accurate identification of various potato diseases and pests. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs) such as DenseNet, Inception, MobileNet, and ResNet architectures, often enhanced with techniques like transfer learning, data augmentation (including GAN-based methods), and attention mechanisms to improve accuracy and efficiency. These models are being developed for both high-accuracy classification and lightweight deployment on embedded devices for real-time field applications. The improved accuracy and speed of these automated systems offer significant potential for optimizing disease management strategies and minimizing economic losses in potato farming.