{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T19:43:52Z","timestamp":1790711032007,"version":"4.1.0"},"reference-count":34,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T00:00:00Z","timestamp":1614384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ19C130005"],"award-info":[{"award-number":["LQ19C130005"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31901410"],"award-info":[{"award-number":["31901410"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Visual navigation is developing rapidly and is of great significance to improve agricultural automation. The most important issue involved in visual navigation is extracting a guidance path from agricultural field images. Traditional image segmentation methods may fail to work in paddy field, for the colors of weed, duckweed, and eutrophic water surface are very similar to those of real rice seedings. To deal with these problems, a crop row segmentation and detection algorithm, designed for complex paddy fields, is proposed. Firstly, the original image is transformed to the grayscale image and then the treble-classification Otsu method classifies the pixels in the grayscale image into three clusters according to their gray values. Secondly, the binary image is divided into several horizontal strips, and feature points representing green plants are extracted. Lastly, the proposed double-dimensional adaptive clustering method, which can deal with gaps inside a single crop row and misleading points between real crop rows, is applied to obtain the clusters of real crop rows and the corresponding fitting line. Quantitative validation tests of efficiency and accuracy have proven that the combination of these two methods constitutes a new robust integrated solution, with attitude error and distance error within 0.02\u00b0 and 10 pixels, respectively. The proposed method achieved better quantitative results than the detection method based on typical Otsu under various conditions.<\/jats:p>","DOI":"10.3390\/rs13050901","type":"journal-article","created":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T20:55:44Z","timestamp":1614459344000},"page":"901","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Crop Row Segmentation and Detection in Paddy Fields Based on Treble-Classification Otsu and Double-Dimensional Clustering Method"],"prefix":"10.3390","volume":"13","author":[{"given":"Yue","family":"Yu","sequence":"first","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yidan","family":"Bao","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jichun","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hangjian","family":"Chu","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6752-1757","authenticated-orcid":false,"given":"Yong","family":"He","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7027-8911","authenticated-orcid":false,"given":"Yufei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.biosystemseng.2016.07.009","article-title":"Multi-crop-row detection algorithm based on binocular vision","volume":"150","author":"Zhai","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.compag.2019.04.009","article-title":"Development of a positioning system using UAV-based computer vision for an airboat navigation in paddy field","volume":"162","author":"Liu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.compag.2017.09.008","article-title":"A visual navigation algorithm for paddy field weeding robot based on image understanding","volume":"143","author":"Zhang","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"259","DOI":"10.13031\/2013.27838","article-title":"Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions","volume":"38","author":"Woebbecke","year":"1995","journal-title":"Trans. Asae"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. ManCybern."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"7095","DOI":"10.3390\/s110707095","article-title":"Mapping Wide Row Crops with Video Sequences Acquired from a Tractor Moving at Treatment Speed","volume":"11","author":"Ribeiro","year":"2011","journal-title":"Sensors"},{"key":"ref_8","unstructured":"Hough, P.V.C. (1962). A Method and Means for Recognizing Complex Patterns. (3,069,654), U.S. Patent."},{"key":"ref_9","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":"Wera","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/BF00735439","article-title":"Vision-guidance of agricultural vehicles","volume":"2","author":"Billingsley","year":"1995","journal-title":"Auton. Robot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"11889","DOI":"10.1016\/j.eswa.2012.02.117","article-title":"Automatic detection of crop rows in maize fields with high weeds pressure","volume":"39","author":"Montalvo","year":"2012","journal-title":"Expert Syst. Appl. Int. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.eswa.2012.07.073","article-title":"Automatic expert system based on images for accuracy crop row detection in maize field","volume":"40","author":"Guerrero","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_13","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":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s11119-016-9494-1","article-title":"Curved and straight crop row detection by accumulation of green pixels from images in maize fields","volume":"19","author":"Guerrero","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.compag.2017.09.028","article-title":"Crop rows and weeds detection in maize fields applying a computer vision system based on geometry","volume":"142","author":"Guerrero","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/s10846-019-01006-0","article-title":"A UAV Guidance System Using Crop Row Detection and Line Follower Algorithms","volume":"97","author":"Basso","year":"2019","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.compag.2019.05.002","article-title":"Automatic detection of cereal rows by means of pattern recognition techniques","volume":"162","author":"Tenhunen","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"37440","DOI":"10.1109\/ACCESS.2020.2973756","article-title":"A New Automatic Real-time Crop Row Recognition based on SoC-FPGA","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1007\/s11119-020-09732-4","article-title":"A template-free machine vision-based crop row detection algorithm","volume":"22","author":"Rabab","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Romeo, J., Pajares, G., Montalvo, M., Guerrero, J.M., Guijarro, M., and Ribeiro, A. (2012). Crop Row Detection in Maize Fields Inspired on the Human Visual Perception. Sci. World J.","DOI":"10.1100\/2012\/484390"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Fue, K., Porter, W., Barnes, E., Li, C., and Rains, G. (2020). Evaluation of a Stereo Vision System for Cotton Row Detection and Boll Location Estimation in Direct Sunlight. Agronomy, 10.","DOI":"10.3390\/agronomy10081137"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"71272","DOI":"10.1109\/ACCESS.2020.2987642","article-title":"Deep Neural Network-Based System for Autonomous Navigation in Paddy Field","volume":"8","author":"Adhikari","year":"2020","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ponnambalam, V.R., Bakken, M., Moore, R.J.D., Gjevestad, J.G.O., and From, P.J. (2020). Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields. Sensors, 20.","DOI":"10.3390\/s20185249"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.compag.2016.04.024","article-title":"A survey of image processing techniques for plant extraction and segmentation in the field","volume":"125","author":"Hamuda","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Woebbecke, D., Meyer, G., Von Bargen, K., and Mortensen, D. (1993). Plant Species Identification, Size, and Enumeration Using Machine Vision Techniques on Near-Binary Images. Proc. SPIE, 1836.","DOI":"10.1117\/12.144030"},{"key":"ref_28","unstructured":"Kataoka, T., Kaneko, T., Okamoto, H., and Hata, S. (2003, January 20\u201324). Crop growth estimation system using machine vision. Proceedings of the IEEE\/ASME International Conference on Advanced Intelligent Mechatronics, Kobe, Japan."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s11119-005-6787-1","article-title":"Automated Crop and Weed Monitoring in Widely Spaced Cereals","volume":"7","author":"Hague","year":"2006","journal-title":"Precis. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.compag.2018.05.030","article-title":"A universal estimation model of fractional vegetation cover for different crops based on time series digital photographs","volume":"151","author":"Zhang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","unstructured":"Matsui, N., and Mita, K. (1990). Method of Reducing Digital Images. (4,931,881), U.S. Patent."},{"key":"ref_32","unstructured":"Guoquan, J., and Cuijun, Z. (2010, January 22\u201324). A vision system based crop rows for agricultural mobile robot. Proceedings of the 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, China."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.compag.2018.09.014","article-title":"Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method","volume":"154","author":"Zhang","year":"2018","journal-title":"Comput. Electron. Agric."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/901\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:30:02Z","timestamp":1760160602000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/901"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,27]]},"references-count":34,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13050901"],"URL":"https:\/\/doi.org\/10.3390\/rs13050901","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,27]]}}}