[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120376-en":3,"doc-seo-120376-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120376,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing - Research Article","Maize is a major crop in Sudan, yet outbreaks of leaf diseases can substantially reduce production and threaten crop survival. Conventional diagnosis relies on visual field inspection and expert laboratory tests, which demand highly skilled personnel and are often slow. This research applies traditional machine learning with image processing to detect maize diseases, using 600 images from the PlantVillage dataset. K-means segmentation and texture/statistical feature extraction are combined with four classifiers to identify common rust, gray leaf spot, and healthy leaves. Results achieve 90%–92.7% accuracy, with SVM and artificial neural networks performing best.","Journal of Agricultural Sciences (Tarim Bilimleri Dergisi) 2024, 30 (3) : 464 – 476 DOI: 10.15832/ankutbd.1288298  \nJournal of Agricultural Sciences  \n(Tarim Bilimleri Dergisi)  \nJ Agr Sci-Tarim Bilie-ISSN: 2148-9297 [jas.ankara.edu.tr](jas.ankara.edu.tr)  \nMachine Learning-based for Automatic Detection of Corn-Plant Diseases Using  \nImage Processing  \nKhaled Adil Dawood Idressa*, Omsalma Alsadig Adam Gadallab, Yeşim Benal Öztekina,  \nGeofrey Prudence Baituc  \naOndokuz Mayis University, Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering, Samsun, TÜRKİYE b University of Khartoum, Faculty of Agriculture, Department of Agricultural Engineering, Khartoum, SUDAN  \nc University of Dar es Salaam, College of Agriculture and Food Technology, Department of Agricultural Engineering, Dar es Salaam, TANZANIA  \nARTICLE INFO  \nResearch Article  \nCorresponding Author: Khaled Adil Dawood Idress, E-mail: [adilkhaled850@gmail.com](adilkhaled850@gmail.com)  \n[Received: 26 April 2023 / Revised: 06 January 2024 / Accepted: 15 January 2024 / Online: 23 July 2024](Received: 26 April 2023 / Revised: 06 January 2024 / Accepted: 15 January 2024 / Online: 23 July 2024)  \nCite this article  \nIdress K A D, Gadalla O A A, Öztekin Y B, Baitu G P (2024) . Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing. Journal of Agricultural Sciences (Tarim Bilimleri Dergisi), 30(3):464-476 . DOI: 10. 15832/ankutbd.1288298  \nABSTRACT  \nCorn is one of the major crops in Sudan. Disease outbreaks can significantly reduce maize production, causing huge damage. Conventionally, disease diagnosis is made through visual inspection of the damage in fields or through laboratory tests conducted by experts on the affected plant parts of the crop. This process typically requires highly skilled personnel, and it can be time-consuming to complete the necessary tasks. Machine learning methods can be implemented to rapidly and accurately detect disease and reduce the risk of crop failure due to disease outbreaks. This study aimed to use traditional machine learning techniques to detect maize diseases using image processing techniques. A  \ntotal of 600 images were obtained from the open-source Plant Village dataset for experimentation. In this study, image segmentation was done using K-means clustering, and a total of 4 GLCM texture features and two statistical features were extracted from the images. In this study, four traditional machine learning algorithms were applied to detect diseased maize leaves (common rust and gray leaf spot) and healthy maize leaves. The results showed that all the algorithms performed well in identifying the diseased and healthy leaves, with accuracy rates ranging from 90% to 92.7% . The highest accuracy scores were obtained with support vector machine and artificial neural networks, respectively.  \nKeywords: Maize Disease, Traditional Machine Learning, Image Processing, Feature Extraction  \n1. Introduction  \nMaize (Zea mays L.) is one of the most widely cultivated and consumed cereal crops worldwide, after wheat and rice, and is recognized as the \"queen of cereals\" . It holds significant economic importance for resource-limited farmers in developing countries. Maize is not only utilized as a primary food source but also for industrial purposes, including biofuel production, starch, and oil extraction. Unfortunately, like other crops, plant diseases are a significant challenge that farmers face worldwide. Maize diseases occur yearly and significantly impede maize production (Subramanian et al. 2022) . The diseases affecting maize crops have the potential to cause varying degrees of harm, from moderate to severe, to the overall production of the crop. Reports indicate that the annual damage caused by pathogenic diseases alone ranges from 4% to 14% of total maize production (Oerke & Dehne 2004) . During its growth, maize leaves are exposed to several disease risks such as grey leaf","cbCaiv17BDmSXrn9","https://ap.wps.com/l/cbCaiv17BDmSXrn9","pdf",1407928,1,13,"English","en",105,"# Introduction\n## Background and significance of maize diseases\n## Conventional diagnosis approaches and limitations\n## Automated computer vision systems and ML vs DL\n# Materials and Methods\n## Dataset and image segmentation (K-means)\n## Feature extraction (GLCM texture and statistical features)\n## Classification algorithms\n# Results and Discussion\n## Performance metrics and accuracy comparison\n## Best-performing models\n# Conclusion","[{\"question\":\"What problem does the study address in maize farming?\",\"answer\":\"The study targets the production losses caused by maize leaf disease outbreaks, where early and accurate diagnosis is difficult with conventional approaches.\"},{\"question\":\"How were the images processed to prepare for classification?\",\"answer\":\"The method uses K-means clustering for image segmentation, then extracts GLCM texture features and additional statistical features from the segmented images.\"},{\"question\":\"Which machine learning models achieved the highest accuracy?\",\"answer\":\"Support vector machine (SVM) and artificial neural networks produced the highest accuracy scores, with overall performance ranging from 90% to 92.7%.\"}]","Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing - 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