{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T22:11:09Z","timestamp":1780524669656,"version":"3.54.1"},"reference-count":27,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T00:00:00Z","timestamp":1600041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Automated robotic platforms are an important part of precision agriculture solutions for sustainable food production. Agri-robots require robust and accurate guidance systems in order to navigate between crops and to and from their base station. Onboard sensors such as machine vision cameras offer a flexible guidance alternative to more expensive solutions for structured environments such as scanning lidar or RTK-GNSS. The main challenges for visual crop row guidance are the dramatic differences in appearance of crops between farms and throughout the season and the variations in crop spacing and contours of the crop rows. Here we present a visual guidance pipeline for an agri-robot operating in strawberry fields in Norway that is based on semantic segmentation with a convolution neural network (CNN) to segment input RGB images into crop and not-crop (i.e., drivable terrain) regions. To handle the uneven contours of crop rows in Norway\u2019s hilly agricultural regions, we develop a new adaptive multi-ROI method for fitting trajectories to the drivable regions. We test our approach in open-loop trials with a real agri-robot operating in the field and show that our approach compares favourably to other traditional guidance approaches.<\/jats:p>","DOI":"10.3390\/s20185249","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T09:04:53Z","timestamp":1600074293000},"page":"5249","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4937-2051","authenticated-orcid":false,"given":"Vignesh Raja","family":"Ponnambalam","sequence":"first","affiliation":[{"name":"Faculty of Science and Technology, Norwegian University of Life Sciences, 1430 \u00c5s, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marianne","family":"Bakken","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Norwegian University of Life Sciences, 1430 \u00c5s, Norway"},{"name":"SINTEF Digital, Forskningsveien 1, 0373 Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9967-0308","authenticated-orcid":false,"given":"Richard J. D.","family":"Moore","sequence":"additional","affiliation":[{"name":"SINTEF Digital, Forskningsveien 1, 0373 Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9650-3910","authenticated-orcid":false,"given":"Jon","family":"Glenn Omholt Gjevestad","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Norwegian University of Life Sciences, 1430 \u00c5s, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P\u00e5l","family":"Johan From","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Norwegian University of Life Sciences, 1430 \u00c5s, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Grimstad, L., and From, P.J. (2017). The Thorvald II Agricultural Robotic System. Robotics, 6.","DOI":"10.3390\/robotics6040024"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xiong, Y., From, P.J., and Isler, V. (2018, January 21\u201325). Design and evaluation of a novel cable-driven gripper with perception capabilities for strawberry picking robots. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8460705"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Grimstad, L., Zakaria, R., Le, T.D., and From, P.J. (2018, January 1\u20135). A novel autonomous robot for greenhouse applications. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594233"},{"key":"ref_4","unstructured":"Ahamed, T., Kulmutiwat, S., Thanpattranon, P., Tuntiwut, S., Ryozo, N., and Takigawa, T. (2011, January 7\u201310). Monitoring of plant growth using laser range finder. Proceedings of the American Society of Agricultural and Biological Engineers Annual International Meeting, Louisville, KY, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"English, A., Ross, P., Ball, D., Upcroft, B., and Corke, P. (October, January 28). Learning crop models for vision-based guidance of agricultural robots. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353516"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1243\/095440705X34667","article-title":"Hough-transform-based vision algorithm for crop row detection of an automated agricultural vehicle","volume":"219","author":"Zhang","year":"2005","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.ifacol.2019.12.506","article-title":"Development of Navigation System for Tea Field Machine Using Semantic Segmentation","volume":"52","author":"Lin","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.compag.2007.07.006","article-title":"A vision based row detection system for sugar beet","volume":"60","author":"Bakker","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/S0168-1699(02)00140-0","article-title":"Determination of crop rows by image analysis without segmentation","volume":"38","author":"Olsen","year":"2003","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.biosystemseng.2017.01.013","article-title":"Automatic detection of curved and straight crop rows from images in maize fields","volume":"156","author":"Montalvo","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Woebbecke, D.M., Meyer, G.E., Von Bargen, K., and Mortensen, D.A. (1993, January 12). Plant species identification, size, and enumeration using machine vision techniques on near-binary images. Proceedings of the Applicationsin Optical Science and Engineering, Boston, MA, USA.","DOI":"10.1117\/12.144030"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.compag.2008.03.009","article-title":"Verification of color vegetation indices for automated crop imaging applications","volume":"63","author":"Meyer","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11149","DOI":"10.1016\/j.eswa.2012.03.040","article-title":"Support vector machines for crop\/weeds identification in maize fields","volume":"39","author":"Guerrero","year":"2012","journal-title":"Exp. Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.biosystemseng.2008.08.001","article-title":"Development of a stereovision sensing system for 3D crop row structure mapping and tractor guidance","volume":"101","author":"Kise","year":"2008","journal-title":"Biosyst. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3394","DOI":"10.1109\/LRA.2018.2852841","article-title":"Crop row detection on tiny plants with the pattern hough transform","volume":"3","author":"Winterhalter","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.patcog.2016.01.013","article-title":"Crop row detection by global energy minimization","volume":"55","author":"Cupec","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Milioto, A., Lottes, P., and Stachniss, C. (2018, January 21\u201325). Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNs. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8460962"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"112","DOI":"10.21595\/mme.2018.19840","article-title":"Automatic semantic segmentation and classification of remote sensing data for agriculture","volume":"4","author":"Jadhav","year":"2018","journal-title":"Math. Models Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ma, X., Deng, X., Qi, L., Jiang, Y., Li, H., Wang, Y., and Xing, X. (2019). Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0215676"},{"key":"ref_21","unstructured":"Varshney, V. (2017). Supervised and Unsupervised Learning for Plant and Crop Row Detection in Precision Agriculture. [Ph.D. Thesis, Kansas State University]."},{"key":"ref_22","unstructured":"Bentley, L., MacInnes, J., Bhadani, R., and Bose, T. (2019). A Pseudo-Derivative Method for Sliding Window Path Mapping in Robotics-Based Image Processing, CAT Vehicle Research Experience for Undergraduates."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.compag.2010.09.013","article-title":"Automatic segmentation of relevant textures in agricultural images","volume":"75","author":"Guijarro","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.1016\/j.eswa.2014.10.033","article-title":"Automatic detection of crop rows based on multi-ROIs","volume":"42","author":"Jiang","year":"2015","journal-title":"Exp. Syst. Appl."},{"key":"ref_25","unstructured":"Wada, K. (2020, May 01). Labelme: Image Polygonal Annotation with Python. Available online: https:\/\/github.com\/wkentaro\/labelme."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","unstructured":"Gupta, D. (2020, June 06). Implementation of Various Deep Image Segmentation Models in Keras. Available online: https:\/\/github.com\/divamgupta\/image-segmentation-keras."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5249\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:09:53Z","timestamp":1760177393000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5249"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,14]]},"references-count":27,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20185249"],"URL":"https:\/\/doi.org\/10.3390\/s20185249","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,14]]}}}