{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-smartphone-application-to-detection-and","title":"A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust","arxiv_id":"1904.00742","date":"2019-03-19","proceeding":null,"authors":["Giuliano L. Manso","Helder Knidel","Renato A. Krohling","Jose A. Ventura"],"abstract":"Generally, the identification and classification of plant diseases and/or\npests are performed by an expert . One of the problems facing coffee farmers in\nBrazil is crop infestation, particularly by leaf rust Hemileia vastatrix and\nleaf miner Leucoptera coffeella. The progression of the diseases and or pests\noccurs spatially and temporarily. So, it is very important to automatically\nidentify the degree of severity. The main goal of this article consists on the\ndevelopment of a method and its i implementation as an App that allow the\ndetection of the foliar damages from images of coffee leaf that are captured\nusing a smartphone, and identify whether it is rust or leaf miner, and in turn\nthe calculation of its severity degree. The method consists of identifying a\nleaf from the image and separates it from the background with the use of a\nsegmentation algorithm. In the segmentation process, various types of\nbackgrounds for the image using the HSV and YCbCr color spaces are tested. In\nthe segmentation of foliar damages, the Otsu algorithm and the iterative\nthreshold algorithm, in the YCgCr color space, have been used and compared to\nk-means. Next, features of the segmented foliar damages are calculated. For the\nclassification, artificial neural network trained with extreme learning machine\nhave been used. The results obtained shows the feasibility and effectiveness of\nthe approach to identify and classify foliar damages, and the automatic\ncalculation of the severity. The results obtained are very promising according\nto experts.","url_abs":"http://arxiv.org/abs/1904.00742v1","url_pdf":"http://arxiv.org/pdf/1904.00742v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-smartphone-application-to-detection-and","repo_url":"https://github.com/FrexG/ycgcr_leaf_segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}