Detection of Plant Leaf Diseases Using K‒mean++ Intermeans Thresholding Algorithm

Abstract

In the field of agricultural information, the plant leaf disease detection is highly important for both farmer life and environment. To improve the accuracy of plant leaf disease detection and reduce the image processing time, the improved K‒mean++ clustering and intermeans thresholding method are proposed in this study. The proposed algorithms are used for training and testing diseases in plant leaf images in two different databases. Of the proposed methods, the intermeans algorithm will be selected based on different thresholding values. The optimal value of thresholding-i.e., the intermeans algorithm-will help increase the accuracy and speed of classifying diseases in plant leaf images. This method will be also used with unseen images of plant leaf. The experimental result of the detection of plant leaf diseases achieves an average detection accuracy of 98.10%. When compared with the results based on standard K‒mean clustering, the current method gives better results around 23.20%. The proposed algorithm is more effective than the standard algorithms for detecting plant leaf diseases, as well as the reduction in cots in the computational power of computers.