A Layout for Citrus Plant Disease Detection Using Automation Tools - Bobbinpreet Kaur , Tripti Sharma ,Bhawna Goyal ,Ayush Dogra (International Journal of Advanced Science and Technology, 2020)
The Computer Aided Diagnostic (CAD) tools have gained a significant position in the toolsrequired for improvement and expansion of quality in production yield by reducing manual error indetection and diagnosis of disease. The CAD tools have properties of expediting the decision makingcapability of the system on the complex disease data automatically. Since these plant diseases can befatal in terms of economic loss and nutrition loss, there is an increase in need of identification ofdisease at early stage so as to meet nutritional needs of human beings. The use of these computeraided methods has made it possible to reduce the production loss and early detection of diseases. Thisarticle proposes a complete layout for detection of Citrus disease using image processingmethodology. The images acquired from the plant fields are processed in terms of noise removal andcontrast improvement in order to pass through subsequent stages accurately. The automation ofdisease detection comprise of various stages namely-image acquisition, preprocessing, imagesegmentation, feature extraction, feature selection and classification. For carrying out detection theacquired image is generally taken as leaf or fruit as the major symptoms of disease appears on theleaf and fruit portion. After image acquisition the image is pre-processed in terms of contrastenhancement and color space conversion. The pre-processed image is passed through segmentationstage for selection of region of interest and the features are extracted to form Image feature vector.These feature vector act as training data for classifier. The results of the classifier are quantified interms of efficiency calculated through the values of true and false cases. This include True positive,False positive, True negative and False negative