An Efficient Technique For Predicting Plant Leaf Diseases In Digital Image Processing

: The digital image processing techniques has been widely used in various applications. One of the application is agriculture. In agriculture plant disease detection and diagnosis using digital image processing techniques focused on accurate segmentation of healthy and diseased tissue. In various segmentation methods, Semi-automatic segmentation was most widely utilized which was based on gray scale histogram. In a novel semi-automatic segmentation process, pixels along the edges were removed and then color conversion was done. After color conversion, contrast enhancement of an image and pixel value adjustments were performed to improve the image quality. Histogram with 100 bins was constructed for identifying the diseased tissue from the healthier part of a leaf image. Finally, segmentation of diseased leaf was found based on the histogram bins. Such bins were searched manually which is not easy for all cases. Moreover, detection accuracy was degraded by the influence of reflection light and distortion regions in an acquired image. Hence Quality Assessment Method Scheme (QAMS) algorithm used to remove both reflection light and distortion from image. For automatic separation of diseased part from the healthier regions in a leaf image an optimization algorithm is required. The method employs Convolutional Neural Networks (CNN) algorithm to automatically define the histogram bins and separate diseased part from the healthier regions in a leaf image. After segmenting the diseased leaf image, the classification is done by Support Vector Machine (SVM) to detect the leaf diseases. The method provides better detection accuracy and computational time is reduced.