Alyas, R.M.Mohammed, A.S.2024-09-292024-09-292022978-166547483-2https://doi.org/10.1109/ICMI55296.2022.9873793https://hdl.handle.net/20.500.14619/9394IEEE Turkey Section; Istanbul Atlas University2nd International Conference on Computing and Machine Intelligence, ICMI 2022 -- 15 July 2022 through 16 July 2022 -- Istanbul -- 182557The aim of this article to introduces various image processing and machine learning techniques used to identify plant diseases based on images of diseased plants in order to recognize disease in plants from images and necessary in image processing and machine learning as they apply to the identification and categorization of plant diseases. We meticulously review more content and provide important standards. These characteristics include things like the size of the photo collection, preprocessing, segmentation techniques, classifier types, classifier resolution, and other things. To suggest and arrange our work on the classification and identification of plant diseases, we explain our study here. Then, based on the principal technical solution used in the method, each of these groups is split using machine learning techniques. Photos of plant disease samples were processed using support vector (SVM) and k-mean clustering techniques to extract color and texture information. The results show that the SVM classifier is a very good tool for detecting and identifying plant-borne diseases in agricultural crops. © 2022 IEEE.eninfo:eu-repo/semantics/closedAccessImage ProcessingImage SegmentationInternet-of -ThingsMachine LearningPlant Diseases DetectionSupport Vector MachinesConference Object10.1109/ICMI55296.2022.98737932-s2.0-85139062142N/A