Exploring the Deep Learning Techniques in Plant Disease Detection: A Review of Recent Advances

In agriculture, protecting crop yield is one of the most critical aspects of avoiding crop waste and ensuring food security around the world. One of the most critical aspects of preserving yield is protecting it from pests and plant diseases. With the advancement in the field of Artificial Intelligence (AI), it has been applied to different domains, and one such field is agriculture, where we can incorporate AI. Deep learning (DL), which is a subset of Artificial Intelligence, has gained lots of attention toward plant disease detection in the present day because of its better accuracy and performance in comparison with other techniques like machine learning (ML), etc. In this paper, we provide a comprehensive review of the current research work by utilizing deep learning for plant disease detection. We study the different models and architectures proposed by different authors and try to identify the pros and cons of the proposed methodology. We also discuss the various datasets that have been used in research work for detecting plant diseases. Finally, we describe the possible challenges in implementing deep learning models and discuss the future roadmap that can be followed by trying to identify the research gaps.