{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T09:02:22Z","timestamp":1776330142844,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T00:00:00Z","timestamp":1686787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Government of Andalusia, Regional Ministry of Economic Transformation, Industry, Knowledge and Universities","award":["PYC20 RE 082 USE"],"award-info":[{"award-number":["PYC20 RE 082 USE"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Variable rate application (VRA) is a crucial tool in precision agriculture, utilizing platforms such as Google Earth Engine (GEE) to access vast satellite image datasets and employ machine learning (ML) techniques for data processing. This research investigates the feasibility of implementing supervised ML models (random forest (RF), the support vector machine (SVM), gradient boosting trees (GBT), classification and regression trees (CART)) and unsupervised k-means clustering in GEE to generate accurate management zones (MZs). By leveraging Sentinel-2 satellite imagery and yielding monitor data, these models calculate vegetation indices to monitor crop health and reveal hidden patterns. The achieved classification accuracy values (0.67 to 0.99) highlight the potential of GEE and ML models for creating precise MZs, enabling subsequent VRA implementation. This leads to enhanced farm profitability, improved natural resource efficiency, and reduced environmental impact.<\/jats:p>","DOI":"10.3390\/rs15123131","type":"journal-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T02:02:20Z","timestamp":1686880940000},"page":"3131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Multilayer Data and Artificial Intelligence for the Delineation of Homogeneous Management Zones in Maize Cultivation"],"prefix":"10.3390","volume":"15","author":[{"given":"Diego Jos\u00e9","family":"Gallardo-Romero","sequence":"first","affiliation":[{"name":"Departamento de Ingenier\u00eda Aeroespacial y Mec\u00e1nica de Fluidos \u201c\u00c1rea Agroforestal\u201d, Universidad de Sevilla, 41004 Sevilla, Spain"}]},{"given":"Orly Enrique","family":"Apolo-Apolo","sequence":"additional","affiliation":[{"name":"Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Blok B, 1st Floor, 9000 Gent, Belgium"}]},{"given":"Jorge","family":"Mart\u00ednez-Guanter","sequence":"additional","affiliation":[{"name":"Corteva Agriscience, 41309 La Rinconada, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3681-1572","authenticated-orcid":false,"given":"Manuel","family":"P\u00e9rez-Ruiz","sequence":"additional","affiliation":[{"name":"Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Blok B, 1st Floor, 9000 Gent, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5504","DOI":"10.3390\/s150305504","article-title":"An Approach to Precise Nitrogen Management Using Hand-Held Crop Sensor Measurements and Winter Wheat Yield Mapping in a Mediterranean Environment","volume":"15","author":"Quebrajo","year":"2015","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0168-1699(02)00096-0","article-title":"Precision Agriculture\u2014A Worldwide Overview","volume":"36","author":"Zhang","year":"2002","journal-title":"Comput. 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