Comprehensive Survey on Datasets, Models, and Future Directions in Plant Disease Prediction

In recent years, advancements in computer vision (CV) and machine learning (ML) have facilitated significant progress in the field of plant disease prediction and detection. The growing danger to worldwide food security because of plant diseases necessitates the development of accurate and efficient methods for early disease identification. This paper provides an extensive overview of the current landscape of plant disease prediction using plant images, focusing on datasets, models, and potential future directions. Initially, publicly available datasets that comprise annotated images of healthy and diseased plants, enabling researchers to develop and evaluate predictive models are analyzed. These datasets, encompassing a wide range of crops and diseases, serve as crucial resources for training and benchmarking various algorithms. Also, explore both traditional and modern approaches, including expert systems, ML, and deep learning (DL) algorithms. Model architectures, transfer learning strategies, and ensemble techniques are discussed in terms of their effectiveness in disease classification and localization. This paper also addresses the challenges faced in plant disease prediction, such as data scarcity, model robustness, and scalability. Provide a rendition of the present state of research and identify potential avenues for future exploration, this paper aims to contribute to the advancement of plant disease prediction methods, fostering more resilient and productive agricultural practices.