Image-Based Plant Disease Detects using Machine Learning

Abstract

Computational algorithms are used for the processing of numerical images. It includes a wide variety of algorithms that can prevent problems during processing, such as noise buildup and signal distortion, and that can be applied to input data. In the context of multivariate systems, digital image processing can be modeled and images are defined in two dimensions (perhaps more). To automatically identify and classify plant leaf diseases, we need to plan and develop a software solution using image processing. The image processing method has primarily four steps. The first step is the structure of the color transformation of the Red Green Blue (RGB) leaf images and it will be translated into a grayscale image after enrichment using Otsu. Using the K-means clustering technique, the images are at hand in the second step. At step 3, Gray Level Co-Occurrence Matrix computes and extracts segmented contaminated objects. To categorize the disease according to the characteristics computed and obtained from the target image, we use the Multi-Class Support Vector Machine (MCSVM). The proposed approach was tested using a baseline data set and showed greater accuracy than the use of Artificial Neural Networks (ANN). In conclusion, the proposed classification model, the MCSVM, is highly effective in identifying leaf diseases and can work well for different types of diseases