Detection of Disease Onset in Rice Plant Leaves in Monochrome Light

Rice is one of the most important crops in the world because of its vast usage irrespective of age and gender. Machine vision and image processing techniques are widely used to detect diseases in plant leaves. In this paper, a prototype system is developed for rice disease onset detection from images in monochrome light. Initially, images of leaves of rice plant are acquired in controlled environment. After preprocessing, local binary patterns and local ternary patterns are extracted as features of the image. Support vector machine and k-nearest neighbors are applied as classifier to identify the healthy and diseased image. Training is completed on 70% of the images while testing is done on the remaining 30% images. Experimental values of the results are 0.94, 0.93, 0.98 and 0.93 for Precision, Sensitivity, Specificity and F1 Score respectively. Overall accuracy of the method is 93.55%. The results show encouraging performance of the proposed method.