Potato Leaf Disease Detection and Classification Using Deep Learning Technique

With the advancement of agriculture technology and the use of machine learning in diagnosis plant disease, it becomes more effective to research in plant health agriculture development. This paper talks about how deep learning and convolutional neural networks can help identify potato diseases. It looks at some research that used these methods to accurately recognize and group potato diseases. It also covers topics like weakly supervised learning, spectral imaging, and combining CNN and RNN for potato disease detection. It shows how transfer learning and hyperspectral imaging can improve how well deep learning models diagnose potato diseases. It compares different methods like k-means, multiclass SVM, deep belief networks, and convolutional ne-ural networks to see how good deep learning models are. It suggests that deep learning can find and group different types of potato diseases and proposes a deep learning model that can automatically diagnose crop diseases and pests on potatoes. It ends by saying deep learning can provide reliable and early warning systems for farmers and improve farming practices.