{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:39:11Z","timestamp":1783571951850,"version":"3.55.0"},"reference-count":32,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T00:00:00Z","timestamp":1626220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014189","name":"Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry","doi-asserted-by":"publisher","award":["320026-3"],"award-info":[{"award-number":["320026-3"]}],"id":[{"id":"10.13039\/501100014189","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Machine vision with deep learning is a promising type of automatic visual perception for detecting and segmenting an object effectively; however, the scarcity of labelled datasets in agricultural fields prevents the application of deep learning to agriculture. For this reason, this study proposes weakly supervised crop area segmentation (WSCAS) to identify the uncut crop area efficiently for path guidance. Weakly supervised learning has advantage for training models because it entails less laborious annotation. The proposed method trains the classification model using area-specific images so that the target area can be segmented from the input image based on implicitly learned localization. This way makes the model implementation easy even with a small data scale. The performance of the proposed method was evaluated using recorded video frames that were then compared with previous deep-learning-based segmentation methods. The results showed that the proposed method can be conducted with the lowest inference time and that the crop area can be localized with an intersection over union of approximately 0.94. Additionally, the uncut crop edge could be detected for practical use based on the segmentation results with post-image processing such as with a Canny edge detector and Hough transformation. The proposed method showed the significant ability of using automatic perception in agricultural navigation to infer the crop area with real-time level speed and have localization comparable to existing semantic segmentation methods. It is expected that our method will be used as essential tool for the automatic path guidance system of a combine harvester.<\/jats:p>","DOI":"10.3390\/s21144801","type":"journal-article","created":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T10:13:42Z","timestamp":1626257622000},"page":"4801","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Weakly Supervised Crop Area Segmentation for an Autonomous Combine Harvester"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9669-4761","authenticated-orcid":false,"given":"Wan-Soo","family":"Kim","sequence":"first","affiliation":[{"name":"Institute of Agricultural Science, Chungnam National University, Daejeon 34134, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9544-5974","authenticated-orcid":false,"given":"Dae-Hyun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taehyeong","family":"Kim","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul 08826, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyunggun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomechatronic Engineering, Sungkyunkwan University, Suwon 16419, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taeyong","family":"Sim","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Sejong University, Seoul 05006, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1212-9018","authenticated-orcid":false,"given":"Yong-Joo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Korea"},{"name":"Department of Smart Agricultural Systems, Chungnam National University, Daejeon 34134, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103323","DOI":"10.1016\/j.robot.2019.103323","article-title":"Crop edge detection based on stereo vision","volume":"123","author":"Kneip","year":"2020","journal-title":"Rob. 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