A Novel AI-Based Approach for Better Segmentation of the Fungal and Bacterial Leaf Diseases of Rice Plant

Rice is the most consumed food for more than half the world. All over the world, approximately 15% of the rice get wasted because of leaf diseases. A computer-aided system needs a clear segmented lesion to detect such diseases, but blurriness, bad contrast, and dust particles on leaves are the challenge in proper segmentation and, further, for better feature extraction. In this work, first, SegNet deep learning model was trained to separate the weed from the images captured from the field; then, in the next step, a novel automated segmentation technique named RPK-means proposed combining random path (RP) and K-means clustering to separate lesion spots from the leaf images. The work of the model is multifold. First, the SegNet model is trained for weed separation; then, two clusters of the image are generated by K-means clustering to find out pixel coordinates lying on the lesion spot and healthy part of the leaves. Thereafter, to separate the lesion part from the background, automatic segmentation is performed by the novel random path K-means (RPK-means) method using coordinate positions obtained at the last stage. Fungal and bacterial diseases like brown spot, rice blast, sheath blight, leaf scaled, and bacterial blight have been collected from the field to perform the experiments. Experimental result shows that the performance of the deep learning classifier increased by approximately 2-6% while applying to RPK-means preprocessed images, rather than the traditional K-means segmentation technique.