{"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/line-detection-and-segmentation-of-annual","title":"Line Detection and Segmentation of Annual Crops Using Hybrid Method","arxiv_id":null,"date":"2023-10-11","proceeding":"Latin American Robotics Symposium (LARS), Brazilian Symposium on Robotics (SBR) 2023 10","authors":["Érick Cardoso Gonçalves","Gustavo Pereira de Almeida","Eduardo Lawson Da Silva","Tatiana Taís Schein","Paulo Jefferson Dias de Oliveira Evald","Paulo Lilles Jorge Drews-Jr"],"abstract":"Currently, agriculture has incorporated more and\r\nmore technologies to optimize production, reducing waste and\r\nenvironmental impacts of this activity. Precision agriculture\r\naims to maximize efficiency and agricultural productivity while\r\nreducing the use of chemicals. This work proposes an automatic\r\napproach based on supervised learning and image processing\r\nmethods for crop line detection in frontal images obtained by a\r\nground camera. Along with this, data augmentation techniques\r\nare applied to increase the reference method from the literature\r\nability of the algorithm. The proposed method uses a semantic\r\nsegmentation architecture based on Deeplabv3+ architecture to\r\nsegment the region of interest, and the Canny Edge technique is\r\napplied to locate the image edges. Finally, the probabilistic Hough\r\ntransform is applied to identify the segments from the identified\r\nboundaries. The proposed method is evaluated and compared\r\nwith a variation of a reference method from the literature\r\nalgorithm, where our method presented higher performance,\r\nachieving an overall accuracy of 84.1%, even on images with\r\nhigh weed presence and crop line gaps.","url_abs":"https://ieeexplore.ieee.org/document/10332920","url_pdf":"https://ieeexplore.ieee.org/document/10332920","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":"line-detection-and-segmentation-of-annual","repo_url":"https://github.com/autoceres/VisionCERES","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"line-detection","task_name":"Line Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic 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}