Disease Identification in Potato Leaf using Deep Learning

In the last few years, a part of artificial intelligence, which is deep learning, is in trend in various industries, including agriculture. It's a powerful tool that helps to extract important features, especially in tasks like identifying plant diseases. When it comes to recognizing plant diseases, deep learning is great because it removes the need for manually selecting features. This not only speeds up research and technological progress but also makes the process more objective. This review talks about the latest developments in deep learning technology for identifying diseases in crop leaves, and it also discusses the current trends and challenges in plant disease detection. I introduced a system that can classify two common potato diseases based on the condition of their leaves using deep learning models. Impressively, it achieves an accuracy rate of 97-98%. It's important to address the importance of early disease detection for sustainable agriculture and food security. The proposed leaf disease detection system uses deep learning models, like Convolutional Neural Networks (CNNs). In particular, this model for the identification of late blight and early blight shows exceptional accuracy, marking a significant advancement in managing crop diseases. Furthermore, we leverage artificial intelligence to improve the efficiency of leaf’s disease identification. These advancements in deep learning play a crucial role in promptly and effectively identifying plant diseases, ultimately enhancing crop production and quality.