Instinctive Recognition of Pathogens in Rice Using Reformed Fractional Differential Segmentation and Innovative Fuzzy Logic-Based Probabilistic Neural Network

Rice is an essential primary food crop in the world, and it plays a significant part country’s economy. It most often eaten stable great demand market as world’s population continues to expand. output should be boosted fulfil growing demand. As result, yield of plant crops diminishes, creating environment conducive spread infectious illnesses. To boost production agricultural fields, necessary remove diseases from environment. This study presents ways for recognising three types rice diseases, well healthy leaf, plants. includes image capture, preprocessing, segmentation, feature extraction, classification illnesses, among other techniques. Following K-means features are extracted utilising criteria, which colour, shape, texture, generate final product. Colour, texture parameters used extraction features. proposed that novel intensity-based technique retrieve colour infected section, whereas form such area diameter, characteristics section using grey-level co-occurrence matrix. The retrieved depending on All previous techniques were surpassed by fuzzy logic-based probabilistic neural network range performance metrics, with new obtaining greater accuracy. Finally, result validated fivefold cross-validation method, accuracy bacterial leaf blight, brown spot, blast being 95.20 percent, 97.60 99.20 98.40 respectively, 95.40 percent disease spot.