{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T21:43:58Z","timestamp":1768513438689,"version":"3.49.0"},"reference-count":94,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T00:00:00Z","timestamp":1675123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Love Beets USA","award":["NYG-625424"],"award-info":[{"award-number":["NYG-625424"]}]},{"name":"Love Beets USA","award":["1827551"],"award-info":[{"award-number":["1827551"]}]},{"name":"New York Farm Viability Institute (NYFVI)","award":["NYG-625424"],"award-info":[{"award-number":["NYG-625424"]}]},{"name":"New York Farm Viability Institute (NYFVI)","award":["1827551"],"award-info":[{"award-number":["1827551"]}]},{"name":"the United States Department of Agriculture (USDA)","award":["NYG-625424"],"award-info":[{"award-number":["NYG-625424"]}]},{"name":"the United States Department of Agriculture (USDA)","award":["1827551"],"award-info":[{"award-number":["1827551"]}]},{"name":"National Institute of Food and Agriculture Health","award":["NYG-625424"],"award-info":[{"award-number":["NYG-625424"]}]},{"name":"National Institute of Food and Agriculture Health","award":["1827551"],"award-info":[{"award-number":["1827551"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation (NSF)","doi-asserted-by":"publisher","award":["NYG-625424"],"award-info":[{"award-number":["NYG-625424"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation (NSF)","doi-asserted-by":"publisher","award":["1827551"],"award-info":[{"award-number":["1827551"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>New York state is among the largest producers of table beets in the United States, which, by extension, has placed a new focus on precision crop management. For example, an operational unmanned aerial system (UAS)-based yield forecasting tool could prove helpful for the efficient management and harvest scheduling of crops for factory feedstock. The objective of this study was to evaluate the feasibility of predicting the weight of table beet roots from spectral and textural features, obtained from hyperspectral images collected via UAS. We identified specific wavelengths with significant predictive ability, e.g., we down-select &gt;200 wavelengths to those spectral indices sensitive to root yield (weight per unit length). Multivariate linear regression was used, and the accuracy and precision were evaluated at different growth stages throughout the season to evaluate temporal plasticity. Models at each growth stage exhibited similar results (albeit with different wavelength indices), with the LOOCV (leave-one-out cross-validation) R2 ranging from 0.85 to 0.90 and RMSE of 10.81\u201312.93% for the best-performing models in each growth stage. Among visible and NIR spectral regions, the 760\u2013920 nm-wavelength region contained the most wavelength indices highly correlated with table beet root yield. We recommend future studies to further test our proposed wavelength indices on data collected from different geographic locations and seasons to validate our results.<\/jats:p>","DOI":"10.3390\/rs15030794","type":"journal-article","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T02:04:52Z","timestamp":1675130692000},"page":"794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Forecasting Table Beet Root Yield Using Spectral and Textural Features from Hyperspectral UAS Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7698-2472","authenticated-orcid":false,"given":"Mohammad S.","family":"Saif","sequence":"first","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7867-7674","authenticated-orcid":false,"given":"Robert","family":"Chancia","sequence":"additional","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3864-4293","authenticated-orcid":false,"given":"Sarah","family":"Pethybridge","sequence":"additional","affiliation":[{"name":"Plant Pathology & Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell AgriTech, Cornell University, Geneva, NY 14456, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8500-7524","authenticated-orcid":false,"given":"Sean P.","family":"Murphy","sequence":"additional","affiliation":[{"name":"Plant Pathology & Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell AgriTech, Cornell University, Geneva, NY 14456, USA"}]},{"given":"Amirhossein","family":"Hassanzadeh","sequence":"additional","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3036-0088","authenticated-orcid":false,"given":"Jan","family":"van Aardt","sequence":"additional","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.3390\/nu7042801","article-title":"The Potential Benefits of Red Beetroot Supplementation in Health and Disease","volume":"7","author":"Clifford","year":"2015","journal-title":"Nutrients"},{"key":"ref_2","unstructured":"Caballero, B., Finglas, P.M., and Toldr\u00e1, F. (2016). 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