Precision Agriculture Science and Technology ISSN:2672-0086(Print) 2713-5632(Online) 정밀농업과학기술

Recent advancements in plant disease identification have leveraged image processing and deep learning techniques for automated detection. Visual deep learning systems are employed to identify diseases accurately in the agricultural sector. This study focuses on reviewing the use of image processing and deep learning approaches in the accurate identification of pepper (Capsicum annuum L.) plant diseases. In most cases, it is quite difficult to classify the infected bacterial spots on pepper plants that affect productivity and quality, leading to substantial economic losses in the agricultural industry. To manage the issues, image processing and deep learning techniques have been applied to diagnose bacterial spots in pepper plants from the symptoms found on the leaves. Various methodologies for data augmentation and deep learning methods of embedding, multitask learning, transfer learning, and meta-learning are also discussed. It summarized how models are optimized for performance with reference to existing studies and potential challenges for AI applications in plant disease recognition. Finally, the review concludes with key findings and future directions and highlights the immense potential of deep learning as a valuable tool for accurate and automated identification and practical applications in pepper disease management.