{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T19:28:44Z","timestamp":1773689324684,"version":"3.50.1"},"reference-count":96,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T00:00:00Z","timestamp":1614729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Conservation tillage methods through leaving the crop residue cover (CRC) on the soil surface protect it from water and wind erosions. Hence, the percentage of the CRC on the soil surface is very critical for the evaluation of tillage intensity. The objective of this study was to develop a new methodology based on the semiautomated fuzzy object based image analysis (fuzzy OBIA) and compare its efficiency with two machine learning algorithms which include: support vector machine (SVM) and artificial neural network (ANN) for the evaluation of the previous CRC and tillage intensity. We also considered the spectral images from two remotely sensed platforms of the unmanned aerial vehicle (UAV) and Sentinel-2 satellite, respectively. The results indicated that fuzzy OBIA for multispectral Sentinel-2 image based on Gaussian membership function with overall accuracy and Cohen\u2019s kappa of 0.920 and 0.874, respectively, surpassed machine learning algorithms and represented the useful results for the classification of tillage intensity. The results also indicated that overall accuracy and Cohen\u2019s kappa for the classification of RGB images from the UAV using fuzzy OBIA method were 0.860 and 0.779, respectively. The semiautomated fuzzy OBIA clearly outperformed machine learning approaches in estimating the CRC and the classification of the tillage methods and also it has the potential to substitute or complement field techniques.<\/jats:p>","DOI":"10.3390\/rs13050937","type":"journal-article","created":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T05:10:16Z","timestamp":1614748216000},"page":"937","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Comparative Approach of Fuzzy Object Based Image Analysis and Machine Learning Techniques Which Are Applied to Crop Residue Cover Mapping by Using Sentinel-2 Satellite and UAV Imagery"],"prefix":"10.3390","volume":"13","author":[{"given":"Payam","family":"Najafi","sequence":"first","affiliation":[{"name":"Department of Biosystems Engineering, Faculty of Agriculture, University of Tabriz, 29 Bahman Blvd, Tabriz 5166616471, Iran"}]},{"given":"Bakhtiar","family":"Feizizadeh","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, Faculty of Planning and Environmental Sciences, University of Tabriz, 29 Bahman Blvd, Tabriz 5166616471, Iran"}]},{"given":"Hossein","family":"Navid","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering, Faculty of Agriculture, University of Tabriz, 29 Bahman Blvd, Tabriz 5166616471, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kopittke, P.M., Menzies, N.W., Wang, P., McKenna, B.A., and Lombi, E. 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