Investigation of classical segmentation's impact on paddy disease classification performance - Hemanthakumar R. Kappali; Sadyojatha KalapurMath (Universitas Ahmad Dahlan, 2023)
The key source of information for disease diagnosis and classification in paddy diseases is the leaves. Applying hybrid techniques, such as image processing-pattern recognition (IP-PR) and computer vision-based technologies, is the answer to assessing the health of plants. The following paddy diseases are considered in this paper: bacterial leaf blight (BLB), brown spot (BS), leaf smut (LS), and narrow brown spot (NBS) from the machine learning repository. A classical colour threshold-based segmentation method is implemented newly to separate the patterns of image pixels into the diseased part and the normal part. The human visual impression (VI), a subjective method, and a parametric-based method with an average error rate (ER) and overlap rate (OR) are used to assess the uniqueness of the suggested segmentation technique. Using a multi-class support vector machine (MSVM) classifier, the analysis yielded segmented images using the proposed method with an accuracy of 92% over the existing method with an accuracy of 76.60%. The BLB disease achieved the highest identification accuracy of 91%. Our proposed method evaluates the segmentation performance and achieved consistent accuracy higher than the previous segmentation work.