{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:44:12Z","timestamp":1776275052341,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,9]],"date-time":"2022-09-09T00:00:00Z","timestamp":1662681600000},"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>Biomass is a key biophysical parameter for precision agriculture and plant breeding. Fast, accurate and non-destructive monitoring of biomass enables various applications related to crop growth. In this paper, strawberry dry biomass weight was modeled using 4 canopy geometric parameters (area, average height, volume, standard deviation of height) and 25 spectral variables (5 band original reflectance values and 20 vegetation indices (VIs)) extracted from the Unmanned Aerial Vehicle (UAV) multispectral imagery. Six regression techniques\u2014multiple linear regression (MLR), random forest (RF), support vector machine (SVM), multivariate adaptive regression splines (MARS), eXtreme Gradient Boosting (XGBoost) and artificial neural network (ANN)\u2014were employed and evaluated for biomass prediction. The ANN had the highest accuracy in a five-fold cross-validation, with R2 of 0.89~0.93, RMSE of 7.16~8.98 g and MAE of 5.06~6.29 g. As for the other five models, the addition of VIs increased the R2 from 0.77~0.80 to 0.83~0.86, and reduced the RMSE from 8.89~9.58 to 7.35~8.09 g and the MAE from 6.30~6.70 to 5.25~5.47 g, respectively. Red-edge-related VIs, including the normalized difference red-edge index (NDRE), simple ratio vegetation index red-edge (SRRedEdge), modified simple ratio red-edge (MSRRedEdge) and chlorophyll index red and red-edge (CIred&amp;RE), were the most influential VIs for biomass modeling. In conclusion, the combination of canopy geometric parameters and VIs obtained from the UAV imagery was effective for strawberry dry biomass estimation using machine learning models.<\/jats:p>","DOI":"10.3390\/rs14184511","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T04:05:41Z","timestamp":1663041941000},"page":"4511","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Prediction of Strawberry Dry Biomass from UAV Multispectral Imagery Using Multiple Machine Learning Methods"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5519-8240","authenticated-orcid":false,"given":"Caiwang","family":"Zheng","sequence":"first","affiliation":[{"name":"Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA"},{"name":"School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32603, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6182-4017","authenticated-orcid":false,"given":"Amr","family":"Abd-Elrahman","sequence":"additional","affiliation":[{"name":"Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA"},{"name":"School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32603, USA"}]},{"given":"Vance","family":"Whitaker","sequence":"additional","affiliation":[{"name":"Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA"},{"name":"Horticultural Sciences Department, University of Florida, Gainesville, FL 32603, USA"}]},{"given":"Cheryl","family":"Dalid","sequence":"additional","affiliation":[{"name":"Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA"},{"name":"Horticultural Sciences Department, University of Florida, Gainesville, FL 32603, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hofius, D., and B\u00f6rnke, F.A. 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