{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:58:33Z","timestamp":1777597113242,"version":"3.51.4"},"reference-count":17,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,10,11]],"date-time":"2017-10-11T00:00:00Z","timestamp":1507680000000},"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>Remote sensing techniques are routinely used in plant species discrimination and of weed mapping. In the presented work, successful Silybum marianum detection and mapping using multilayer neural networks is demonstrated. A multispectral camera (green-red-near infrared) attached on a fixed wing unmanned aerial vehicle (UAV) was utilized for the acquisition of high-resolution images (0.1 m resolution). The Multilayer Perceptron with Automatic Relevance Determination (MLP-ARD) was used to identify the S. marianum among other vegetation, mostly Avena sterilis L. The three spectral bands of Red, Green, Near Infrared (NIR) and the texture layer resulting from local variance were used as input. The S. marianum identification rates using MLP-ARD reached an accuracy of 99.54%. \u03a4he study had an one year duration, meaning that the results are specific, although the accuracy shows the interesting potential of S. marianum mapping with MLP-ARD on multispectral UAV imagery.<\/jats:p>","DOI":"10.3390\/s17102307","type":"journal-article","created":{"date-parts":[[2017,10,11]],"date-time":"2017-10-11T12:17:47Z","timestamp":1507724267000},"page":"2307","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Application of Multilayer Perceptron with Automatic Relevance Determination on Weed Mapping Using UAV Multispectral Imagery"],"prefix":"10.3390","volume":"17","author":[{"given":"Afroditi","family":"Tamouridou","sequence":"first","affiliation":[{"name":"Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"},{"name":"Laboratory of Remote Sensing and GIS, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1893-6301","authenticated-orcid":false,"given":"Thomas","family":"Alexandridis","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing and GIS, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"given":"Xanthoula","family":"Pantazi","sequence":"additional","affiliation":[{"name":"Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3225-8033","authenticated-orcid":false,"given":"Anastasia","family":"Lagopodi","sequence":"additional","affiliation":[{"name":"Plant Pathology Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"given":"Javid","family":"Kashefi","sequence":"additional","affiliation":[{"name":"USDA-ARS-European Biological Control Laboratory, 54623 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9485-3631","authenticated-orcid":false,"given":"Dimitris","family":"Kasampalis","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing and GIS, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"given":"Georgios","family":"Kontouris","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing and GIS, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"given":"Dimitrios","family":"Moshou","sequence":"additional","affiliation":[{"name":"Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1111\/wre.12026","article-title":"Potential uses of small unmanned aircraft systems (UAS) in weed research","volume":"53","author":"Rasmussen","year":"2013","journal-title":"Weed Res."},{"key":"ref_2","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. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.rse.2011.01.009","article-title":"Object-based crop identification using multiple vegetation indices, textural features and crop phenology","volume":"115","author":"Ngugi","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_4","first-page":"80","article-title":"Allelopathic potential of Rhazya stricta Decne on germination of Pennisetum typhoides","volume":"1","author":"Khan","year":"2011","journal-title":"Int. J. Biosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2246","DOI":"10.1080\/01431161.2016.1252475","article-title":"Evaluation of UAV imagery for mapping Silybum marianum weed patches","volume":"38","author":"Tamouridou","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pe\u00f1a, J.M., Torres-S\u00e1nchez, J., de Castro, A.I., Kelly, M., and L\u00f3pez-Granados, F. (2013). Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0077151"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Torres-S\u00e1nchez, J., L\u00f3pez-Granados, F., De Castro, A.I., and Pe\u00f1a-Barrag\u00e1n, J.M. (2013). Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0058210"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1006\/jaer.2000.0630","article-title":"PA\u2014Precision agriculture: Remote-sensing and mapping of weeds in crops","volume":"78","author":"Lamb","year":"2001","journal-title":"J. Agric. Eng. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.13031\/2013.17800","article-title":"Discriminating weeds from processing tomato plants using visible and near-infrared spectroscopy","volume":"47","author":"Slaughter","year":"2004","journal-title":"Am. Soc. Agric. Biol. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1016\/j.asoc.2010.01.011","article-title":"A computer vision approach for weeds identification through support vector machines","volume":"11","author":"Tellaeche","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1023\/A:1015590520873","article-title":"A weed species spectral detector based on neural networks","volume":"3","author":"Moshou","year":"2002","journal-title":"Precis. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1006\/jaer.1999.0519","article-title":"Crop-weed discrimination by line imaging spectroscopy","volume":"75","author":"Borregaard","year":"2000","journal-title":"J. Agric. Eng. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/S0168-1699(00)00170-8","article-title":"A Neural Network based plant classifier","volume":"31","author":"Moshou","year":"2001","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.biosystemseng.2013.02.002","article-title":"Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle","volume":"115","author":"Ortega","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1016\/j.envsoft.2008.11.012","article-title":"Increasing the accuracy of neural network classification using refined training data","volume":"24","author":"Kavzoglu","year":"2009","journal-title":"Environ. Model. Softw."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. [1st ed.].","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1162\/neco.1992.4.3.448","article-title":"A practical Bayesian framework for back-propagation networks","volume":"4","author":"MacKay","year":"1992","journal-title":"Neural Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/10\/2307\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:47:00Z","timestamp":1760208420000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/10\/2307"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,11]]},"references-count":17,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2017,10]]}},"alternative-id":["s17102307"],"URL":"https:\/\/doi.org\/10.3390\/s17102307","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10,11]]}}}