{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T16:52:38Z","timestamp":1793206358619,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Equipment Technologes, Mooresville, IN"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1909916"],"award-info":[{"award-number":["IIS-1909916"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467155","type":"proceedings-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T02:13:10Z","timestamp":1628734390000},"page":"2773-2781","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Deep Learning based Crop Row Detection with Online Domain Adaptation"],"prefix":"10.1145","author":[{"given":"Rashed","family":"Doha","sequence":"first","affiliation":[{"name":"Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Al Hasan","sequence":"additional","affiliation":[{"name":"Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sohel","family":"Anwar","sequence":"additional","affiliation":[{"name":"Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veera","family":"Rajendran","sequence":"additional","affiliation":[{"name":"Equipment Technologies, Indianapolis, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechatronics.2004.05.005"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2960873"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biosystemseng.2011.05.001"},{"key":"e_1_3_2_1_4_1","volume-title":"Jan Bontsema, Lie Tang, Joachim M\u00fcller, and Gerrit van Straten.","author":"Bakker Tijmen","year":"2008","unstructured":"Tijmen Bakker , Hendrik Wouters , Kees Van Asselt , Jan Bontsema, Lie Tang, Joachim M\u00fcller, and Gerrit van Straten. 2008 . A vision based row detection system for sugar beet. Computers and electronics in agriculture, Vol. 60 , 1 (2008), 87--95. Tijmen Bakker, Hendrik Wouters, Kees Van Asselt, Jan Bontsema, Lie Tang, Joachim M\u00fcller, and Gerrit van Straten. 2008. A vision based row detection system for sugar beet. Computers and electronics in agriculture, Vol. 60, 1 (2008), 87--95."},{"key":"e_1_3_2_1_5_1","volume-title":"A UAV Guidance System Using Crop Row Detection and Line Follower Algorithms. Journal of Intelligent & Robotic Systems (03","author":"Basso Maik","year":"2019","unstructured":"Maik Basso and Edison Pignaton de Freitas . 2019. A UAV Guidance System Using Crop Row Detection and Line Follower Algorithms. Journal of Intelligent & Robotic Systems (03 2019 ). https:\/\/doi.org\/10.1007\/s10846-019-01006-0 10.1007\/s10846-019-01006-0 Maik Basso and Edison Pignaton de Freitas. 2019. A UAV Guidance System Using Crop Row Detection and Line Follower Algorithms. Journal of Intelligent & Robotic Systems (03 2019). https:\/\/doi.org\/10.1007\/s10846-019-01006-0"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2010.12.011"},{"key":"e_1_3_2_1_7_1","volume-title":"Kalinin","author":"Buslaev Alexander","year":"2020","unstructured":"Alexander Buslaev , Vladimir I. Iglovikov , Eugene Khvedchenya , Alex Parinov , Mikhail Druzhinin , and Alexandr A . Kalinin . 2020 . Albumentations : Fast and Flexible Image Augmentations. Information , Vol. 11 , 2 (2020). https:\/\/doi.org\/10.3390\/info11020125 10.3390\/info11020125 Alexander Buslaev, Vladimir I. Iglovikov, Eugene Khvedchenya, Alex Parinov, Mikhail Druzhinin, and Alexandr A. Kalinin. 2020. Albumentations: Fast and Flexible Image Augmentations. Information, Vol. 11, 2 (2020). https:\/\/doi.org\/10.3390\/info11020125"},{"key":"e_1_3_2_1_8_1","volume-title":"Development of line-detection algorithms for local positioning in densely seeded crops. Canadian biosystems engineering","author":"Fontaine V","year":"2006","unstructured":"V Fontaine and TG Crowe . 2006. Development of line-detection algorithms for local positioning in densely seeded crops. Canadian biosystems engineering , Vol. 48 ( 2006 ), 7. V Fontaine and TG Crowe. 2006. Development of line-detection algorithms for local positioning in densely seeded crops. Canadian biosystems engineering, Vol. 48 (2006), 7."},{"key":"e_1_3_2_1_9_1","volume-title":"Mart\u00edn Montalvo Mart\u00ednez, and Gonzalo Pajares","author":"Garc\u00eda-Santill\u00e1n Iv\u00e1n","year":"2017","unstructured":"Iv\u00e1n Garc\u00eda-Santill\u00e1n , Jos\u00e9 Guerrero , Mart\u00edn Montalvo Mart\u00ednez, and Gonzalo Pajares . 2017 . Curved and straight crop row detection by accumulation of green pixels from images in maize fields. Precision Agriculture , Vol. 19 (01 2017). https:\/\/doi.org\/10.1007\/s11119-016--9494--1 10.1007\/s11119-016--9494--1 Iv\u00e1n Garc\u00eda-Santill\u00e1n, Jos\u00e9 Guerrero, Mart\u00edn Montalvo Mart\u00ednez, and Gonzalo Pajares. 2017. Curved and straight crop row detection by accumulation of green pixels from images in maize fields. Precision Agriculture, Vol. 19 (01 2017). https:\/\/doi.org\/10.1007\/s11119-016--9494--1"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2007.06.003"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (11 2013","author":"Girshick Ross","year":"2013","unstructured":"Ross Girshick , Jeff Donahue , Trevor Darrell , and Jitendra Malik . 2013 . Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation . Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (11 2013 ). https:\/\/doi.org\/10.1109\/CVPR.2014.81 10.1109\/CVPR.2014.81 Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2013. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (11 2013). https:\/\/doi.org\/10.1109\/CVPR.2014.81"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2017.09.028"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcm.2010.11.030"},{"key":"e_1_3_2_1_14_1","volume-title":"Automatic detection of crop rows based on multi-ROIs. Expert systems with applications","author":"Jiang Guoquan","year":"2015","unstructured":"Guoquan Jiang , Zhiheng Wang , and Hongmin Liu . 2015. Automatic detection of crop rows based on multi-ROIs. Expert systems with applications , Vol. 42 , 5 ( 2015 ), 2429--2441. Guoquan Jiang, Zhiheng Wang, and Hongmin Liu. 2015. Automatic detection of crop rows based on multi-ROIs. Expert systems with applications, Vol. 42, 5 (2015), 2429--2441."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICWAPR.2010.5576422"},{"key":"e_1_3_2_1_16_1","volume-title":"Neural Information Processing Systems","volume":"25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey Hinton . 2012 . ImageNet Classification with Deep Convolutional Neural Networks . Neural Information Processing Systems , Vol. 25 (01 2012). https:\/\/doi.org\/10.1145\/3065386 10.1145\/3065386 Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems, Vol. 25 (01 2012). https:\/\/doi.org\/10.1145\/3065386"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3015891"},{"key":"e_1_3_2_1_18_1","volume-title":"Tracking of row structure in three crops using image analysis. Computers and electronics in agriculture","author":"Marchant JA","year":"1996","unstructured":"JA Marchant . 1996. Tracking of row structure in three crops using image analysis. Computers and electronics in agriculture , Vol. 15 , 2 ( 1996 ), 161--179. JA Marchant. 1996. Tracking of row structure in three crops using image analysis. Computers and electronics in agriculture, Vol. 15, 2 (1996), 161--179."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00205"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"A. Milioto P. Lottes and C. Stachniss. 2018. Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs. In 2018 IEEE International Conference on Robotics and Automation (ICRA). 2229--2235.  A. Milioto P. Lottes and C. Stachniss. 2018. Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs. In 2018 IEEE International Conference on Robotics and Automation (ICRA). 2229--2235.","DOI":"10.1109\/ICRA.2018.8460962"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/0168-1699(94)00044-Q"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20185249"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.08.027"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1100\/2012\/484390"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1243\/095440705X34667"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.3390\/s110707095"},{"key":"e_1_3_2_1_28_1","volume-title":"Theory, Techniques and Applications. (02","author":"Shehata Allam","year":"2015","unstructured":"Allam Shehata , Sherien Mohammad , Mohamed Abdallah , and Mohammad Ragab . 2015. A Survey on Hough Transform , Theory, Techniques and Applications. (02 2015 ). Allam Shehata, Sherien Mohammad, Mohamed Abdallah, and Mohammad Ragab. 2015. A Survey on Hough Transform, Theory, Techniques and Applications. (02 2015)."},{"key":"e_1_3_2_1_29_1","volume-title":"Giles Tetteh, and Bjoern H Menze.","author":"Shit Suprosanna","year":"2020","unstructured":"Suprosanna Shit , Johannes C Paetzold , Anjany Sekuboyina , Andrey Zhylka , Ivan Ezhov , Alexander Unger , Josien PW Pluim , Giles Tetteh, and Bjoern H Menze. 2020 . clDice--a Topology-Preserving Loss Function for Tubular Structure Segmentation . arXiv preprint arXiv:2003.07311 (2020). Suprosanna Shit, Johannes C Paetzold, Anjany Sekuboyina, Andrey Zhylka, Ivan Ezhov, Alexander Unger, Josien PW Pluim, Giles Tetteh, and Bjoern H Menze. 2020. clDice--a Topology-Preserving Loss Function for Tubular Structure Segmentation. arXiv preprint arXiv:2003.07311 (2020)."},{"key":"e_1_3_2_1_30_1","volume-title":"Determination of crop rows by image analysis without segmentation. Computers and electronics in agriculture","author":"S\u00f8gaard Henning Tangen","year":"2003","unstructured":"Henning Tangen S\u00f8gaard and Hans J\u00f8rgen Olsen . 2003. Determination of crop rows by image analysis without segmentation. Computers and electronics in agriculture , Vol. 38 , 2 ( 2003 ), 141--158. Henning Tangen S\u00f8gaard and Hans J\u00f8rgen Olsen. 2003. Determination of crop rows by image analysis without segmentation. Computers and electronics in agriculture, Vol. 38, 2 (2003), 141--158."},{"key":"e_1_3_2_1_31_1","volume-title":"Pattern Recognition","volume":"55","author":"Ivan","year":"2016","unstructured":"Ivan Vidovi?, Robert Cupec , and Zeljko Hocenski . 2016 . Crop Row Detection by Global Energy Minimization . Pattern Recognition , Vol. 55 (01 2016). https:\/\/doi.org\/10.1016\/j.patcog.2016.01.013 10.1016\/j.patcog.2016.01.013 Ivan Vidovi?, Robert Cupec, and Zeljko Hocenski. 2016. Crop Row Detection by Global Energy Minimization. Pattern Recognition, Vol. 55 (01 2016). https:\/\/doi.org\/10.1016\/j.patcog.2016.01.013"},{"key":"e_1_3_2_1_32_1","volume-title":"The Free Encyclopedia. https:\/\/en.wikipedia.org\/w\/index.php?title=Overfitting&oldid=953810115 [Online","author":"Wikipedia Wikipedia","year":"2020","unstructured":"Wikipedia contributors. 2020. Overfitting -- Wikipedia , The Free Encyclopedia. https:\/\/en.wikipedia.org\/w\/index.php?title=Overfitting&oldid=953810115 [Online ; accessed 1- May - 2020 ]. Wikipedia contributors. 2020. Overfitting -- Wikipedia, The Free Encyclopedia. https:\/\/en.wikipedia.org\/w\/index.php?title=Overfitting&oldid=953810115 [Online; accessed 1-May-2020 ]."},{"key":"e_1_3_2_1_33_1","volume-title":"Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method. Computers and Electronics in Agriculture (09","author":"Zhang Xiya","year":"2018","unstructured":"Xiya Zhang , Xiaona Li , Baohua Zhang , Jun Zhou , Guangzhao Tian , Yingjun Xiong , and Baoxing Gu. 2018. Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method. Computers and Electronics in Agriculture (09 2018 ). https:\/\/doi.org\/10.1016\/j.compag.2018.09.014 10.1016\/j.compag.2018.09.014 Xiya Zhang, Xiaona Li, Baohua Zhang, Jun Zhou, Guangzhao Tian, Yingjun Xiong, and Baoxing Gu. 2018. Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method. Computers and Electronics in Agriculture (09 2018). https:\/\/doi.org\/10.1016\/j.compag.2018.09.014"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3077129"},{"key":"e_1_3_2_1_35_1","volume-title":"Comparing U-Net convolutional network with mask R-CNN in the performances of pomegranate tree canopy segmentation","author":"Zhao Tiebiao","unstructured":"Tiebiao Zhao , Yonghuan Yang , Haoyu Niu , Dong Wang , and YangQuan Chen . 2018. Comparing U-Net convolutional network with mask R-CNN in the performances of pomegranate tree canopy segmentation . In Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII, Allen M. Larar, Makoto Suzuki, and Jianyu Wang (Eds.), Vol. 10780 . International Society for Optics and Photonics, SPIE , 210 -- 218. https:\/\/doi.org\/10.1117\/12.2325570 10.1117\/12.2325570 Tiebiao Zhao, Yonghuan Yang, Haoyu Niu, Dong Wang, and YangQuan Chen. 2018. Comparing U-Net convolutional network with mask R-CNN in the performances of pomegranate tree canopy segmentation. In Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII, Allen M. Larar, Makoto Suzuki, and Jianyu Wang (Eds.), Vol. 10780. International Society for Optics and Photonics, SPIE, 210 -- 218. https:\/\/doi.org\/10.1117\/12.2325570"}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467155","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3447548.3467155","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467155","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:18:27Z","timestamp":1750177107000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":35,"alternative-id":["10.1145\/3447548.3467155","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467155","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}