{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T03:54:03Z","timestamp":1780545243841,"version":"3.54.1"},"reference-count":29,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2011,6,10]],"date-time":"2011-06-10T00:00:00Z","timestamp":1307664000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA).<\/jats:p>","DOI":"10.3390\/s110606270","type":"journal-article","created":{"date-parts":[[2011,6,10]],"date-time":"2011-06-10T12:35:40Z","timestamp":1307709340000},"page":"6270-6283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":115,"title":["Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination"],"prefix":"10.3390","volume":"11","author":[{"given":"Hong Y.","family":"Jeon","sequence":"first","affiliation":[{"name":"United State Department of Agriculture\u2014Agricultural Research Service, Application Technology Research Unit, 1680 Madison Ave, Wooster, OH 44691, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei F.","family":"Tian","sequence":"additional","affiliation":[{"name":"Agricultural and Biological Engineering Department, University of Illinois, 1304 W. Pennsylvania Ave., Urbana, IL 61801, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heping","family":"Zhu","sequence":"additional","affiliation":[{"name":"United State Department of Agriculture\u2014Agricultural Research Service, Application Technology Research Unit, 1680 Madison Ave, Wooster, OH 44691, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2011,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1006\/jaer.1996.0114","article-title":"Spray boom motion and spray distribution: Part 1, derivation of a mathematical relation","volume":"66","author":"Ramon","year":"1997","journal-title":"J. Agr. Eng. 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ASABE"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.13031\/2013.11053","article-title":"Distance-based control system for machine vision-based selective spraying","volume":"45","author":"Steward","year":"2002","journal-title":"Trans. ASABE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.biosystemseng.2009.09.005","article-title":"Direct application end effector for a precision weed control robot","volume":"104","author":"Jeon","year":"2009","journal-title":"Biosyst. Eng"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1614\/WS-04-044R2","article-title":"A review of remote sensing of invasive weeds and example of the early detection of spotted knapweed (Centaurea maculosa) and babysbreath (Gypsophila paniculata) with a hyperspectral sensor","volume":"53","author":"Lass","year":"2005","journal-title":"Weed Sci"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1614\/WS-04-072R1","article-title":"Translation of remote sensing data into weed management decisions","volume":"53","author":"Shaw","year":"2005","journal-title":"Weed Sci"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1614\/WS-05-54.2.346","article-title":"Using remote sensing for identification of late-season grass weed patches in wheat","volume":"54","year":"2006","journal-title":"Weed Sci"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1023\/A:1013326304427","article-title":"Weed detection using canopy reflection","volume":"3","author":"Vrindts","year":"2002","journal-title":"Precis. Agr"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.biosystemseng.2007.08.007","article-title":"A real-time, embedded, weed-detection system for use in wheat fields","volume":"98","author":"Wang","year":"2007","journal-title":"Biosyst. Eng"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.compag.2007.12.007","article-title":"Selection of the most efficient wavelength bands for discriminating weeds from crop","volume":"62","author":"Piron","year":"2008","journal-title":"Comput. Electron. Agr"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.13031\/2013.17244","article-title":"Textural imaging and discriminant analysis for distinguishing weeds for spot spraying","volume":"41","author":"Meyer","year":"1998","journal-title":"Trans. ASABE"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.13031\/2013.3103","article-title":"Weed detection using color machine vision","volume":"43","author":"Zhang","year":"2000","journal-title":"Trans. ASABE"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1006\/bioe.2002.0109","article-title":"Robotic weed control using machine vision","volume":"83","author":"Blasco","year":"2002","journal-title":"Biosyst. Eng"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.13031\/2013.13944","article-title":"Classification of broadleaf and grass weeds using gabor wavelets and an artificial neural network","volume":"46","author":"Tang","year":"2003","journal-title":"Trans. ASABE"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.compag.2008.09.001","article-title":"Improving weed pressure assessment using digital image from an experience-based reasoning approach","volume":"65","author":"Ribeiro","year":"2009","journal-title":"Comput. Electron. Agr"},{"key":"ref_19","first-page":"337","article-title":"Real-time image processing for crop\/weed discrimination in maize fields","volume":"75","author":"Ribeiro","year":"2010","journal-title":"Comput. Electron. Agr"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s11119-004-5321-1","article-title":"A review on remote sensing of weeds in agriculture","volume":"5","author":"Thorp","year":"2004","journal-title":"Precis. Agr"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/S0168-1699(98)00037-4","article-title":"Environmentally adaptive segmentation algorithm for outdoor image segmentation","volume":"21","author":"Tian","year":"1998","journal-title":"Comput. Electron. Agr"},{"key":"ref_22","first-page":"602","article-title":"Weed detection in sugar beet fields using machine vision","volume":"8","author":"Jafari","year":"2006","journal-title":"Int. J. Agr. Biol"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.biosystemseng.2006.03.014","article-title":"Application of the Hough transform for seed row localization using machine vision","volume":"94","author":"Leemans","year":"2006","journal-title":"Biosyst. Eng"},{"key":"ref_24","first-page":"715","article-title":"Weed recognition in corn fields using back-propagation neural network models","volume":"44","author":"Yang","year":"2002","journal-title":"Can. Biosyst. Eng"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1006\/bioe.2002.0117","article-title":"Weed-plant discrimination by machine vision and artificial neural network","volume":"83","author":"Cho","year":"2002","journal-title":"Biosyst. Eng"},{"key":"ref_26","unstructured":"Konolige, K, and Beymer, D (2004). Small Vision System Calibration: Calibration Addendum to the User\u2019s Manual, Videre Design."},{"key":"ref_27","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. ASABE"},{"key":"ref_28","first-page":"1761","article-title":"Outdoor field machine vision identification of tomato seedlings for automated weed control","volume":"40","author":"Tian","year":"1997","journal-title":"Trans. ASABE"},{"key":"ref_29","unstructured":"Spong, MW, Hutchinson, S, and Vidyasagar, M (2006). Robot Modeling and Control, John Wiley & Sons."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/11\/6\/6270\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:56:22Z","timestamp":1760219782000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/11\/6\/6270"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,6,10]]},"references-count":29,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2011,6]]}},"alternative-id":["s110606270"],"URL":"https:\/\/doi.org\/10.3390\/s110606270","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,6,10]]}}}