Detection of Rice Diseases by the Fusion of Optimization based K-means Clustering Algorithm and Faster Region based Convolutional Neural Network

Abstract

One of the most important food crops in the world is rice, which is highly affected by various plant diseases and pests. Even though there are so many methods to address the concern, detection accuracy is a hectic challenge, which needs to be boosted for an enjoyable farming environment. In the present study a rice disease detection technique was implemented by the fusion of Sailfish optimization – K-means (SCM-KM) and the Faster Region Based Convolutional Neural Network (Faster R-CNN) method. For the optimization of the KM clustering method, Sailfish Optimizer was coupled with the Maximum and Minimum distance algorithm, as well as Chaos theory. The 2D Filtering Mask and Weighted Multilevel Median Filter(2DFM-AMMF) were used to eliminate the sounds. With the aid of the Faster 2D-Otsu technique, the target leaf lesion was segmented from the image. The SCM-KM method is used for detection of rice disease. The Rice diseases were characterized and classified by Region Proposal Networks (RPN) and Faster R-CNN method. Comparative analysis of the SCM-KM+ Faster R-CNN method was performed using the metrics sensitivity, accuracy, and specificity. The proposed detection method produced elevated performance over similar bench marking frameworks.