{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T04:51:39Z","timestamp":1769575899665,"version":"3.49.0"},"reference-count":57,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T00:00:00Z","timestamp":1610409600000},"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>Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha\u22121 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.<\/jats:p>","DOI":"10.3390\/rs13020232","type":"journal-article","created":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T20:11:31Z","timestamp":1610482291000},"page":"232","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3255-5361","authenticated-orcid":false,"given":"Tatiana Fernanda","family":"Canata","sequence":"first","affiliation":[{"name":"Department of Biosystems Engineering, \u201cLuiz de Queiroz\u201d College of Agriculture (ESALQ), University of Sao Paulo (USP), 11 Padua Dias Avenue, Piracicaba 13418-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8242-8435","authenticated-orcid":false,"given":"Marcelo Chan Fu","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering, \u201cLuiz de Queiroz\u201d College of Agriculture (ESALQ), University of Sao Paulo (USP), 11 Padua Dias Avenue, Piracicaba 13418-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2675-767X","authenticated-orcid":false,"given":"Leonardo Felipe","family":"Maldaner","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering, \u201cLuiz de Queiroz\u201d College of Agriculture (ESALQ), University of Sao Paulo (USP), 11 Padua Dias Avenue, Piracicaba 13418-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7250-3780","authenticated-orcid":false,"given":"Jos\u00e9 Paulo","family":"Molin","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering, \u201cLuiz de Queiroz\u201d College of Agriculture (ESALQ), University of Sao Paulo (USP), 11 Padua Dias Avenue, Piracicaba 13418-900, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e20180055","DOI":"10.1590\/1678-992x-2018-0055","article-title":"Applying the NDVI from satellite images in delimiting management zones for annual crops","volume":"77","author":"Damian","year":"2020","journal-title":"Sci. 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