{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T12:05:24Z","timestamp":1784117124620,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T00:00:00Z","timestamp":1600214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["WIS03026"],"award-info":[{"award-number":["WIS03026"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the environment. Recently, unmanned aerial vehicles (UAVs) have become increasingly popular in precision agriculture due to their flexibility in data acquisition and improved spatial and spectral resolutions. In addition, compared with natural color and multispectral imagery, hyperspectral data can provide higher spectral fidelity which is important for modelling crop traits. In this study, we conducted end-of-season potato tuber yield and tuber set predictions using in-season UAV-based hyperspectral images and machine learning. Specifically, six mainstream machine learning models, i.e., ordinary least square (OLS), ridge regression, partial least square regression (PLSR), support vector regression (SVR), random forest (RF), and adaptive boosting (AdaBoost), were developed and compared across potato research plots with different irrigation rates at the University of Wisconsin Hancock Agricultural Research Station. Our results showed that the tuber set could be better predicted than the tuber yield, and using the multi-temporal hyperspectral data improved the model performance. Ridge achieved the best performance for predicting tuber yield (R2 = 0.63) while Ridge and PLSR had similar performance for predicting tuber set (R2 = 0.69). Our study demonstrated that hyperspectral imagery and machine learning have good potential to help potato growers efficiently manage their irrigation practices.<\/jats:p>","DOI":"10.3390\/s20185293","type":"journal-article","created":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T10:30:12Z","timestamp":1600252212000},"page":"5293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6824-4082","authenticated-orcid":false,"given":"Chen","family":"Sun","sequence":"first","affiliation":[{"name":"Biological Systems Engineering, University of Wisconsin\u2013Madison, Madison, WI 53706, USA"},{"name":"Key Laboratory of Spectral Imaging Technology, Xi\u2019an Institute of Optics and Precision Mechanics, CAS, Xi\u2019an 710119, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luwei","family":"Feng","sequence":"additional","affiliation":[{"name":"Biological Systems Engineering, University of Wisconsin\u2013Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7816-672X","authenticated-orcid":false,"given":"Zhou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Biological Systems Engineering, University of Wisconsin\u2013Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchi","family":"Ma","sequence":"additional","affiliation":[{"name":"Biological Systems Engineering, University of Wisconsin\u2013Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Trevor","family":"Crosby","sequence":"additional","affiliation":[{"name":"Horticulture, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mack","family":"Naber","sequence":"additional","affiliation":[{"name":"Horticulture, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[{"name":"Horticulture, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.fcr.2016.04.033","article-title":"Relationships between race-specific and race-non-specific resistance to potato late blight and length of potato vegetation period in various sources of resistance","volume":"196","author":"Plich","year":"2016","journal-title":"Field Crop. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0168-1923(91)90056-V","article-title":"Water stress as a constraint on growth in the potato crop. 1. Model development","volume":"53","author":"Jefferies","year":"1991","journal-title":"Agric. For. Meteorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/S0378-3774(01)00151-2","article-title":"Production of muskmelon (Cucumis melo L.) under controlled deficit irrigation in a semi-arid climate","volume":"54","author":"Fabeiro","year":"2002","journal-title":"Agric. Water Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105750","DOI":"10.1016\/j.agwat.2019.105750","article-title":"Yield and quality of potato tuber and its water productivity are influenced by alternate furrow irrigation in a raised bed system","volume":"224","author":"Sarker","year":"2019","journal-title":"Agric. Water Manag."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, Z., Feng, L., Du, Q., and Runge, T. (2020). Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States. Remote Sens., 12.","DOI":"10.3390\/rs12081232"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"809","DOI":"10.3389\/fpls.2019.00809","article-title":"California almond yield prediction at the orchard level with a machine learning approach","volume":"10","author":"Zhang","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.rse.2017.06.043","article-title":"The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields","volume":"199","author":"Guan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_8","first-page":"1193","article-title":"A light-weight multispectral sensor for micro UAV\u2014Opportunities for very high resolution airborne remote sensing","volume":"37","author":"Nebiker","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Masjedi, A., Zhao, J., and Crawford, M.M. (2017). Prediction of Sorghum Biomass Based on Image Based Features Derived from Time Series of UAV Images, IEEE.","DOI":"10.1109\/IGARSS.2017.8128413"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1109\/JSTARS.2018.2813263","article-title":"Boresight calibration of GNSS\/INS-assisted push-broom hyperspectral scanners on UAV platforms","volume":"11","author":"Habib","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shen, X., Cao, L., Yang, B., Xu, Z., and Wang, G. (2019). Estimation of forest structural attributes using spectral indices and point clouds from UAS-based multispectral and RGB imageries. Remote Sens., 11.","DOI":"10.3390\/rs11070800"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chew, R., Rineer, J., Beach, R., O\u2019Neil, M., Ujeneza, N., Lapidus, D., Miano, T., Hegarty-Craver, M., Polly, J., and Temple, D.S. (2020). Deep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images. Drones, 4.","DOI":"10.3390\/drones4010007"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13007-019-0399-7","article-title":"The estimation of crop emergence in potatoes by UAV RGB imagery","volume":"15","author":"Li","year":"2019","journal-title":"Plant Methods"},{"key":"ref_14","first-page":"14","article-title":"Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data\u2013potential of unmanned aerial vehicle imagery","volume":"66","author":"Roosjen","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105025","DOI":"10.1016\/j.compag.2019.105025","article-title":"Optical sensing for early spring freeze related blueberry bud damage detection: Hyperspectral imaging for salient spectral wavelengths identification","volume":"167","author":"Gao","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kawamura, K., Ikeura, H., Phongchanmaixay, S., and Khanthavong, P. (2018). Canopy hyperspectral sensing of paddy fields at the booting stage and PLS regression can assess grain yield. Remote Sens., 10.","DOI":"10.3390\/rs10081249"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105299","DOI":"10.1016\/j.compag.2020.105299","article-title":"Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat","volume":"172","author":"Moghimi","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Oehlschl\u00e4ger, J., Schmidhalter, U., and Noack, P.O. (2018). UAV-Based Hyperspectral Sensing for Yield Prediction in Winter Barley, IEEE.","DOI":"10.1109\/WHISPERS.2018.8747260"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0304-3800(00)00364-1","article-title":"National spatial crop yield simulation using GIS-based crop production model","volume":"136","author":"Priya","year":"2001","journal-title":"Ecol. Model."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.agrformet.2014.03.012","article-title":"Water limitations on potato yield in Estonia assessed by crop modelling","volume":"194","author":"Saue","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.fcr.2014.06.017","article-title":"Potato, sweet potato, and yam models for climate change: A review","volume":"166","author":"Raymundo","year":"2014","journal-title":"Field Crop. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"32","DOI":"10.21475\/ajcs.18.12.01.pne570","article-title":"Improving the prediction of potato productivity: APSIM-Potato model parameterization and evaluation in Tasmania, Australia","volume":"12","author":"Borus","year":"2018","journal-title":"Aust. J. Crop Sci."},{"key":"ref_23","unstructured":"Roth, O., Derron, J., Fischlin, A., Nemecek, T., and Ulrich, M. (1990). Implementation and Parameter Adaptation of a Potato Crop Simulation Model Combined with a Soil Water Subsystem, International Agricultural Centre."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s11540-016-9321-0","article-title":"Forecasting yield and tuber size of processing potatoes in South Africa using the LINTUL-potato-DSS model","volume":"59","author":"Machakaire","year":"2016","journal-title":"Potato Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1016\/j.agrformet.2010.07.008","article-title":"On the use of statistical models to predict crop yield responses to climate change","volume":"150","author":"Lobell","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"G\u00f3mez, D., Salvador, P., Sanz, J., and Casanova, J.L. (2019). Potato yield prediction using machine learning techniques and sentinel 2 data. Remote Sens., 11.","DOI":"10.3390\/rs11151745"},{"key":"ref_27","unstructured":"(2020, January 06). Potato (Solanum Tuberosum). Available online: http:\/\/bioweb.uwlax.edu\/bio203\/s2009\/bradley_adam\/Growth.htm."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.2134\/agronj15.0150","article-title":"Canopeo: A powerful new tool for measuring fractional green canopy cover","volume":"107","author":"Patrignani","year":"2015","journal-title":"Agron. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1139\/cjb-2016-0009","article-title":"Improving plant biomass estimation in the field using partial least squares regression and ridge regression","volume":"94","author":"Ohsowski","year":"2016","journal-title":"Botany"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/s12393-016-9147-1","article-title":"Partial least squares regression (PLSR) applied to NIR and HSI spectral data modeling to predict chemical properties of fish muscle","volume":"9","author":"Cheng","year":"2017","journal-title":"Food Eng. Rev."},{"key":"ref_31","unstructured":"Vapnik, V., Golowich, S.E., and Smola, A.J. (1997). Support Vector Method for Function Approximation, Regression Estimation and Signal Processing, MIT Press."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.cj.2016.01.008","article-title":"Estimation of biomass in wheat using random forest regression algorithm and remote sensing data","volume":"4","author":"Zhou","year":"2016","journal-title":"Crop J."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wu, X., Chen, Z., Ren, F., Feng, L., and Du, Q. (2019). Optimizing the predictive ability of machine learning methods for landslide susceptibility mapping using smote for lishui city in zhejiang province, china. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16030368"},{"key":"ref_34","unstructured":"Solomatine, D.P., and Shrestha, D.L. (2004). AdaBoost. RT: A Boosting Algorithm for Regression Problems, IEEE."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/BF02877248","article-title":"Relationships between stem number, tuber set and yield of Russet Burbank potatoes","volume":"60","author":"Iritani","year":"1983","journal-title":"Am. Potato J."},{"key":"ref_36","unstructured":"Tibshirani, R. (2013). Modern Regression 1: Ridge Regression, SAGE Publications Ltd."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"287","DOI":"10.2307\/351539","article-title":"Ridge regression as a technique for analyzing models with multicollinearity","volume":"44","author":"Kidwell","year":"1982","journal-title":"J. Marriage Fam."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.3390\/rs70202109","article-title":"Using ridge regression models to estimate grain yield from field spectral data in bread wheat (Triticum aestivum L.) grown under three water regimes","volume":"7","author":"Hernandez","year":"2015","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e0212200","DOI":"10.1371\/journal.pone.0212200","article-title":"Estimation of physiological genomic estimated breeding values (PGEBV) combining full hyperspectral and marker data across environments for grain yield under combined heat and drought stress in tropical maize (Zea mays L.)","volume":"14","author":"Trachsel","year":"2019","journal-title":"PLoS ONE"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.chemolab.2004.01.002","article-title":"Comparing support vector machines to PLS for spectral regression applications","volume":"73","author":"Thissen","year":"2004","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Awad, M., and Khanna, R. (2015). Support vector regression. Efficient Learning Machines, Springer.","DOI":"10.1007\/978-1-4302-5990-9"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Feng, L., Zhang, Z., Ma, Y., Du, Q., Williams, P., Drewry, J., and Luck, B. (2020). Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning. Remote Sens., 12.","DOI":"10.3390\/rs12122028"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.jmapro.2019.04.023","article-title":"Random forest-based real-time defect detection of Al alloy in robotic arc welding using optical spectrum","volume":"42","author":"Zhang","year":"2019","journal-title":"J. Manuf. Process."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Menze, B.H., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., and Hamprecht, F.A. (2009). A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinform., 10.","DOI":"10.1186\/1471-2105-10-213"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.asoc.2014.08.009","article-title":"AdaBoost based bankruptcy forecasting of Korean construction companies","volume":"24","author":"Heo","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/BF02358456","article-title":"Interrelationships of the number of initial sprouts, stems, stolons and tubers per potato plant","volume":"33","author":"Haverkort","year":"1990","journal-title":"Potato Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5293\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:10:32Z","timestamp":1760177432000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5293"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,16]]},"references-count":46,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20185293"],"URL":"https:\/\/doi.org\/10.3390\/s20185293","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,16]]}}}