Paddy Crop Disease Prediction—A Detailed Review on Image Processing Techniques

This review article focuses on paddy disease prediction through two key phases: image processing and disease prediction. The initial phase involves image acquisition, pre-processing, and segmentation, while the subsequent phase includes feature selection, extraction, and classification. The accuracy of the initial phase significantly impacts the disease prediction system’s precision. The main objective is to evaluate diverse image processing techniques used in this initial step, considering relevant performance metrics. The review aims to identify appropriate image processing techniques for the disease prediction model, aiding researchers in technique selection. Agriculture’s role in global economies, particularly paddy’s contribution to food security, faces threats from crop diseases. Swift paddy disease prediction is essential to prevent yield losses and ensure food security. Image processing techniques provide insights into plant health, enhancing prediction accuracy. The review examines image processing methods for paddy disease prediction, including acquisition, pre-processing, and segmentation. Techniques like magnetic resonance imaging and acoustic imaging are discussed, as well as noise removal and image enhancement. Image segmentation techniques such as thresholding and clustering are explored. By highlighting trends and challenges, the review equips researchers with insights into tools for precise disease prediction and global food security considerations. This analysis contributes to optimizing image processing techniques for paddy disease prediction, enhancing agricultural sustainability.