{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T21:55:41Z","timestamp":1768514141835,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T00:00:00Z","timestamp":1622592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSF PFI","award":["1827551"],"award-info":[{"award-number":["1827551"]}]},{"name":"USDA NIFA","award":["NYG-625424"],"award-info":[{"award-number":["NYG-625424"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Timely and accurate monitoring has the potential to streamline crop management, harvest planning, and processing in the growing table beet industry of New York state. We used unmanned aerial system (UAS) combined with a multispectral imager to monitor table beet (Beta vulgaris ssp. vulgaris) canopies in New York during the 2018 and 2019 growing seasons. We assessed the optimal pairing of a reflectance band or vegetation index with canopy area to predict table beet yield components of small sample plots using leave-one-out cross-validation. The most promising models were for table beet root count and mass using imagery taken during emergence and canopy closure, respectively. We created augmented plots, composed of random combinations of the study plots, to further exploit the importance of early canopy growth area. We achieved a R2 = 0.70 and root mean squared error (RMSE) of 84 roots (~24%) for root count, using 2018 emergence imagery. The same model resulted in a RMSE of 127 roots (~35%) when tested on the unseen 2019 data. Harvested root mass was best modeled with canopy closing imagery, with a R2 = 0.89 and RMSE = 6700 kg\/ha using 2018 data. We applied the model to the 2019 full-field imagery and found an average yield of 41,000 kg\/ha (~40,000 kg\/ha average for upstate New York). This study demonstrates the potential for table beet yield models using a combination of radiometric and canopy structure data obtained at early growth stages. Additional imagery of these early growth stages is vital to develop a robust and generalized model of table beet root yield that can handle imagery captured at slightly different growth stages between seasons.<\/jats:p>","DOI":"10.3390\/rs13112180","type":"journal-article","created":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T21:23:41Z","timestamp":1622669021000},"page":"2180","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Predicting Table Beet Root Yield with Multispectral UAS Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7867-7674","authenticated-orcid":false,"given":"Robert","family":"Chancia","sequence":"first","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}]},{"given":"Jan","family":"van Aardt","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"}]},{"given":"Daniel","family":"Cross","sequence":"additional","affiliation":[{"name":"Love Beets USA, Rochester, NY 14606, USA"}]},{"given":"John","family":"Henderson","sequence":"additional","affiliation":[{"name":"Love Beets USA, Rochester, NY 14606, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,2]]},"reference":[{"key":"ref_1","unstructured":"USDA (2014). National Agricultural Statistics Service 2012 Census of Agriculture."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.niox.2014.05.003","article-title":"Dietary Nitrate Supplementation Improves Reaction Time in Type 2 Diabetes: Development and Application of a Novel Nitrate-Depleted Beetroot Juice Placebo","volume":"40","author":"Gilchrist","year":"2014","journal-title":"Nitric Oxide Biol. Chem."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1017\/S0007114512000190","article-title":"Blood Pressure-Lowering Effects of Beetroot Juice and Novel Beetroot-Enriched Bread Products in Normotensive Male Subjects","volume":"108","author":"Hobbs","year":"2012","journal-title":"Br. J. Nutr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s11130-010-0156-6","article-title":"Antioxidant Activity and Phenolic Content of Betalain Extracts from Intact Plants and Hairy Root Cultures of the Red Beetroot Beta Vulgaris cv. Detroit Dark Red","volume":"65","author":"Georgiev","year":"2010","journal-title":"Plant Foods Hum. Nutr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1152\/ajpregu.00206.2010","article-title":"Acute and Chronic Effects of Dietary Nitrate Supplementation on Blood Pressure and the Physiological Responses to Moderate-Intensity and Incremental Exercise","volume":"299","author":"Vanhatalo","year":"2010","journal-title":"Am. J. Physiol. Regul. Integr. Comp. Physiol."},{"key":"ref_7","first-page":"20","article-title":"Physiological Roles and Availability of Betacyanins","volume":"1","author":"Szalaty","year":"2008","journal-title":"Adv. Phytother."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Weiss, M., Jacob, F., and Duveiller, G. (2020). Remote Sensing for Agricultural Applications: A Meta-Review. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2019.111402"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty Five Years of Remote Sensing in Precision Agriculture: Key Advances and Remaining Knowledge Gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.protcy.2013.11.010","article-title":"Drivers of Precision Agriculture Technologies Adoption: A Literature Review","volume":"8","author":"Pierpaoli","year":"2013","journal-title":"Procedia Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_12","unstructured":"Schimmelpfennig, D. (2016). Farm Profits and Adoption of Precision Agriculture."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Stroppiana, D., Migliazzi, M., Chiarabini, V., Crema, A., Musanti, M., Franchino, C., and Villa, P. (2015, January 26\u201331). Rice Yield Estimation Using Multispectral Data from UAV: A Preliminary Experiment in Northern Italy. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326869"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"204","DOI":"10.3389\/fpls.2019.00204","article-title":"Remote Estimation of Rice Yield with Unmanned Aerial Vehicle (UAV) Data and Spectral Mixture Analysis","volume":"10","author":"Duan","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Maresma, \u00c1., Lloveras, J., and Martinez-Casasnovas, J.A. (2018). Use of Multispectral Airborne Images to Improve In-Season Nitrogen Management, Predict Grain Yield and Estimate Economic Return of Maize in Irrigated High Yielding Environments. Remote Sens., 10.","DOI":"10.3390\/rs10040543"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Barzin, R., Pathak, R., Lotfi, H., Varco, J., and Bora, G.C. (2020). Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn. Remote Sens., 12.","DOI":"10.3390\/rs12152392"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.plantsci.2018.10.022","article-title":"A Rapid Monitoring of NDVI across the Wheat Growth Cycle for Grain Yield Prediction Using a Multi-Spectral UAV Platform","volume":"282","author":"Hassan","year":"2019","journal-title":"Plant Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2017.05.003","article-title":"Predicting Grain Yield in Rice Using Multi-Temporal Vegetation Indices from UAV-Based Multispectral and Digital Imagery","volume":"130","author":"Zhou","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.2134\/agronj2019.04.0260","article-title":"Relationship of Drone-Based Vegetation Indices with Corn and Sugarbeet Yields","volume":"111","author":"Olson","year":"2019","journal-title":"Agron. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2619","DOI":"10.2134\/agronj2019.03.0219","article-title":"Can We Select Sugarbeet Harvesting Dates Using Drone-based Vegetation Indices?","volume":"111","author":"Olson","year":"2019","journal-title":"Agron. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"299","DOI":"10.2134\/agronj2016.03.0150","article-title":"Comparison of Satellite Imagery and Ground-Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower","volume":"109","author":"Bu","year":"2017","journal-title":"Agron. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"63","DOI":"10.2134\/agronj1969.00021962006100010021x","article-title":"Emergence Time, Seed Quality, and Planting Depth Effects on Yield and Survival of Cotton (Gossypium hirsutum L.)","volume":"61","author":"Wanjura","year":"1969","journal-title":"Agron. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"162","DOI":"10.3146\/PS19-8.1","article-title":"Impact of First True Leaf Photosynthetic Efficiency on Peanut Plant Growth under Different Early-Season Temperature Conditions","volume":"46","author":"Virk","year":"2019","journal-title":"Peanut Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"131","DOI":"10.2134\/agronj2001.931131x","article-title":"In-Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy Reflectance","volume":"93","author":"Raun","year":"2001","journal-title":"Agron. J."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Al-Gaadi, K.A., Hassaballa, A.A., Tola, E., Kayad, A.G., Madugundu, R., Alblewi, B., and Assiri, F. (2016). Prediction of Potato Crop Yield Using Precision Agriculture Techniques. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0162219"},{"key":"ref_26","unstructured":"(2021, March 11). MicaSense RedEdge-M User Manual. Available online: https:\/\/support.micasense.com\/hc\/en-us\/articles\/115003537673-RedEdge-M-User-Manual-PDF-."},{"key":"ref_27","unstructured":"(2021, March 11). Pix4D Pix4D Mapper. Available online: https:\/\/www.pix4d.com\/product\/pix4dmapper-photogrammetry-software."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mamaghani, B., and Salvaggio, C. (2019). Multispectral Sensor Calibration and Characterization for SUAS Remote Sensing. Sensors, 19.","DOI":"10.3390\/s19204453"},{"key":"ref_29","unstructured":"(2021, May 18). Pix4Dmapper Support: Radiometric Corrections. Available online: https:\/\/support.pix4d.com\/hc\/en-us\/articles\/202559509-Radiometric-corrections."},{"key":"ref_30","unstructured":"(2021, May 18). MicaSense DLS 2 Integration Guide. Available online: https:\/\/support.micasense.com\/hc\/en-us\/articles\/360011569434-DLS-2-Integration-Guide."},{"key":"ref_31","unstructured":"(2021, March 11). L3Harris ENVI. Available online: https:\/\/www.l3harrisgeospatial.com\/Software-Technology\/ENVI."},{"key":"ref_32","unstructured":"Photonics, H. (2021, May 19). Hyperspectral Software Headwall Photonics Software. Available online: https:\/\/www.headwallphotonics.com\/software."},{"key":"ref_33","unstructured":"(2021, May 24). Using ENVI: Atmospheric Correction\u2014Empirical Line Correction. Available online: https:\/\/www.l3harrisgeospatial.com\/docs\/atmosphericcorrection.html#empirical_line_calibration."},{"key":"ref_34","unstructured":"Boggs, T. (2021, March 11). Spectral Python (SPy). Available online: http:\/\/www.spectralpython.net\/."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5282","DOI":"10.1080\/01431161.2013.789147","article-title":"Mineralogical Mapping of Southern Namibia by Application of Continuum-Removal MSAM Method to the HyMap Data","volume":"34","author":"Oshigami","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1111\/jvs.12193","article-title":"Semi-Supervised Classification of Vegetation: Preserving the Good Old Units and Searching for New Ones","volume":"25","author":"Tichy","year":"2014","journal-title":"J. Veg. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1080\/01431169208904102","article-title":"RGB-NDVI Colour Composites for Visualizing Forest Change Dynamics","volume":"13","author":"Sader","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gallo, B.C., Dematt\u00ea, J.A.M., Rizzo, R., Safanelli, J.L., de Mendes, W.S., Lepsch, I.F., Sato, M.V., Romero, D.J., and Lacerda, M.P.C. (2018). Multi-Temporal Satellite Images on Topsoil Attribute Quantification and the Relationship with Soil Classes and Geology. Remote Sens., 10.","DOI":"10.3390\/rs10101571"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/S0176-1617(96)80285-9","article-title":"Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements near 700 Nm","volume":"148","author":"Gitelson","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1109\/TPAMI.1979.4766926","article-title":"A Problem of Dimensionality: A Simple Example","volume":"3","author":"Trunk","year":"1979","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and Photographic Infrared Linear Combinations for Monitoring Vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_43","unstructured":"Sripada, R.P. (2005). Determining in-Season Nitrogen Requirements for Corn Using Aerial Color-Infrared Photography. [Ph.D. Thesis, North Carolina State University]."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"28","DOI":"10.2307\/1942049","article-title":"Relationships between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types","volume":"5","author":"Gamon","year":"1995","journal-title":"Ecol. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A Modified Soil Adjusted Vegetation Index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between Leaf Chlorophyll Content and Spectral Reflectance and Algorithms for Non-Destructive Chlorophyll Assessment in Higher Plant Leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral Vegetation Indices and Novel Algorithms for Predicting Green LAI of Crop Canopies: Modeling and Validation in the Context of Precision Agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_49","first-page":"152","article-title":"Extraction of Vegetation Information from Visible Unmanned Aerial Vehicle Images","volume":"31","author":"Xiaoqin","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between Leaf Pigment Content and Spectral Reflectance across a Wide Range of Species, Leaf Structures and Developmental Stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1080\/10106040108542184","article-title":"Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat","volume":"16","author":"Louhaichi","year":"2001","journal-title":"Geocarto Int."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Afifi, A., May, S., Donatello, R., and Clark, V. (2019). Practical Multivariate Analysis, CRC Press. [6th ed.].","DOI":"10.1201\/9781315203737"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecolind.2013.01.041","article-title":"NDVI Saturation Adjustment: A New Approach for Improving Cropland Performance Estimates in the Greater Platte River Basin, USA","volume":"30","author":"Gu","year":"2013","journal-title":"Ecol. Indic."},{"key":"ref_54","first-page":"88","article-title":"Evaluating the Relationship between Biomass, Percent Groundcover and Remote Sensing Indices across Six Winter Cover Crop Fields in Maryland, United States","volume":"39","author":"Prabhakara","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_55","first-page":"024519","article-title":"Yield Modeling of Snap Bean Based on Hyperspectral Sensing: A Greenhouse Study","volume":"14","author":"Hassanzadeh","year":"2020","journal-title":"JARS"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.asoc.2015.08.027","article-title":"A Semi-Supervised System for Weed Mapping in Sunflower Crops Using Unmanned Aerial Vehicles and a Crop Row Detection Method","volume":"37","year":"2015","journal-title":"Appl. Soft Comput."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2180\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:10:21Z","timestamp":1760163021000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2180"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,2]]},"references-count":56,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13112180"],"URL":"https:\/\/doi.org\/10.3390\/rs13112180","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,2]]}}}