Deep Learning for Crop Disease Detection: Techniques and Challenges

Abstract

Crop diseases are an imminent threat to the world's food security since they are so common, which makes accurate and efficient detection techniques necessary. Through image analysis, deep learning—more specifically, convolutional neural networks (CNNs)—has emerged as a game-changing method for recognizing and categorizing plant diseases. Recent developments have shown how well deep learning models work to interpret intricate patterns in agricultural data, offering encouraging answers to persistent problems with crop disease identification. This review looks at the newest methods and discusses the challenges that come with using these technologies, like inconsistent disease symptoms, high computing needs, and poor data quality. This research emphasizes the significant influence deep learning could have on improving agricultural practices and safeguarding crop health by examining recent achievements and prospective future advancements.