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Detection of Unhealthy citrus leaves using Machine Learning Technique

Precision agriculture increases the productivity with fewer resources and contributes in decision making process. The production of plant gets widely affected by the diseases which degrades the quality of the plants and causes financial loss to the farmers. In past decades, image processing methods are widely used to detect and classify the disease of the plant. In tropical areas of the world, citrus plants are the grown. To prevent plants from the diseases, detection of diseases in early stages is helpful for farmers. Therefore, this paper detects the healthy and unhealthy citrus leaves using different techniques. The Otsu thresholding is performed for segmenting the image and extracting the features. Further, optimized set of features are selected using various metaheuristics algorithm such as WOA, LOA, CSO and DE. The classification is performed using Support vector machine (SVM) and Multilayer perceptron (MLP). The different performance quality measures are evaluated such as recall, precision. F1-score, sensitivity and accuracy. The overall results shows that MLP provides accuracy of 74.8% and SVM gives 61.6% by utilizing WOA. The experimental analysis shows that MLP provides more accuracy than SVM.