{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:17:32Z","timestamp":1774369052901,"version":"3.50.1"},"reference-count":53,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T00:00:00Z","timestamp":1744243200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000199","name":"U.S. Department of Agriculture","doi-asserted-by":"publisher","award":["2020-67021- 32855"],"award-info":[{"award-number":["2020-67021- 32855"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Applied Earth Observation and Geoinformation"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1016\/j.jag.2025.104536","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T12:22:26Z","timestamp":1745583746000},"page":"104536","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Predicting crop yield lows through the highs via binned deep imbalanced regression: A case study on vineyards"],"prefix":"10.1016","volume":"139","author":[{"given":"Hamid","family":"Kamangir","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1369-0624","authenticated-orcid":false,"given":"Brent S.","family":"Sams","sequence":"additional","affiliation":[]},{"given":"Nick","family":"Dokoozlian","sequence":"additional","affiliation":[]},{"given":"Luis","family":"Sanchez","sequence":"additional","affiliation":[]},{"given":"J. Mason","family":"Earles","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.jag.2025.104536_b1","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A study of the behavior of several methods for balancing machine learning training data","volume":"6","author":"Batista","year":"2004","journal-title":"Sigkdd"},{"issue":"4","key":"10.1016\/j.jag.2025.104536_b2","first-page":"430","article-title":"Spatio-temporal variability in vine vigour and yield in a marlborough sauvignon blanc vineyard","volume":"25","author":"Bramley","year":"2019","journal-title":"Ajgwr"},{"issue":"2","key":"10.1016\/j.jag.2025.104536_b3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2907070","article-title":"A survey of predictive modeling on imbalanced domains","volume":"49","author":"Branco","year":"2016","journal-title":"Csur"},{"key":"10.1016\/j.jag.2025.104536_b4","series-title":"First International Workshop on Learning with Imbalanced Domains: Theory and Applications","first-page":"36","article-title":"SMOGN: a pre-processing approach for imbalanced regression","author":"Branco","year":"2017"},{"key":"10.1016\/j.jag.2025.104536_b5","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.neucom.2018.11.100","article-title":"Pre-processing approaches for imbalanced distributions in regression","volume":"343","author":"Branco","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.jag.2025.104536_b6","article-title":"Block-level macadamia yield forecasting using spatio-temporal datasets","volume":"303","author":"Brinkhoff","year":"2021","journal-title":"Macfor"},{"key":"10.1016\/j.jag.2025.104536_b7","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","article-title":"A systematic study of the class imbalance problem in convolutional neural networks","volume":"106","author":"Buda","year":"2018","journal-title":"Neural Netw."},{"issue":"1","key":"10.1016\/j.jag.2025.104536_b8","doi-asserted-by":"crossref","first-page":"70","DOI":"10.3390\/geomatics3010004","article-title":"Exploring the effect of balanced and imbalanced multi-class distribution data and sampling techniques on fruit-tree crop classification using different machine learning classifiers","volume":"3","author":"Chabalala","year":"2023","journal-title":"Geomatics"},{"key":"10.1016\/j.jag.2025.104536_b9","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"Jair"},{"key":"10.1016\/j.jag.2025.104536_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109271","article-title":"Deep imbalanced regression using cost-sensitive learning and deep feature transfer for bearing remaining useful life estimation","volume":"127","author":"Ding","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.jag.2025.104536_b11","series-title":"Ijcai","first-page":"973","article-title":"The foundations of cost-sensitive learning","author":"Elkan","year":"2001"},{"key":"10.1016\/j.jag.2025.104536_b12","series-title":"Learning from Imbalanced Data Sets","author":"Fern\u00e1ndez","year":"2018"},{"issue":"4","key":"10.1016\/j.jag.2025.104536_b13","first-page":"463","article-title":"A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches","volume":"42","author":"Galar","year":"2011","journal-title":"Tsmc"},{"issue":"1","key":"10.1016\/j.jag.2025.104536_b14","first-page":"503","article-title":"Indicators of soil quality and crop productivity assessment at a long-term experiment site in the lower Indo-Gangetic plains","volume":"39","author":"Ghorai","year":"2023","journal-title":"Soiluse"},{"key":"10.1016\/j.jag.2025.104536_b15","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google earth engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.jag.2025.104536_b16","series-title":"Soft (Gaussian CDE) regression models and loss functions","author":"Hern\u00e1ndez-Orallo","year":"2012"},{"issue":"4","key":"10.1016\/j.jag.2025.104536_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2641758","article-title":"Probabilistic reframing for cost-sensitive regression","volume":"8","author":"Hern\u0144dez-Orallo","year":"2014","journal-title":"TKDD"},{"issue":"24","key":"10.1016\/j.jag.2025.104536_b18","doi-asserted-by":"crossref","first-page":"16494","DOI":"10.3390\/ijerph192416494","article-title":"Air quality modeling with the use of regression neural networks","volume":"19","author":"Hoffman","year":"2022","journal-title":"Ijerph"},{"issue":"2","key":"10.1016\/j.jag.2025.104536_b19","first-page":"148","article-title":"Manipulating the postharvest period and its impact on vine productivity of Semillon grapevines","volume":"57","author":"Holzapfel","year":"2006","journal-title":"Ajev"},{"issue":"12","key":"10.1016\/j.jag.2025.104536_b20","doi-asserted-by":"crossref","first-page":"5186","DOI":"10.1016\/j.csda.2007.11.008","article-title":"An adjusted boxplot for skewed distributions","volume":"52","author":"Hubert","year":"2008","journal-title":"Comput. Statist. Data Anal."},{"key":"10.1016\/j.jag.2025.104536_b21","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.neucom.2018.11.099","article-title":"Cost-sensitive support vector machines","volume":"343","author":"Iranmehr","year":"2019","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.jag.2025.104536_b22","first-page":"1","article-title":"Soil quality estimation using environmental covariates and predictive models: an example from tropical soils of Nigeria","volume":"11","author":"Isong","year":"2022","journal-title":"Ecoproc"},{"key":"10.1016\/j.jag.2025.104536_b23","article-title":"Large-scale spatio-temporal yield estimation via deep learning using satellite and management data fusion in vineyards","volume":"216","author":"Kamangir","year":"2024","journal-title":"Cej"},{"key":"10.1016\/j.jag.2025.104536_b24","series-title":"Applied Regression Analysis and Other Multivariable Methods","author":"Kleinbaum","year":"2013"},{"issue":"4","key":"10.1016\/j.jag.2025.104536_b25","first-page":"221","article-title":"Learning from imbalanced data: open challenges and future directions","volume":"5","author":"Krawczyk","year":"2016","journal-title":"Pai"},{"key":"10.1016\/j.jag.2025.104536_b26","series-title":"Icml","first-page":"179","article-title":"Addressing the curse of imbalanced training sets: one-sided selection","author":"Kubat","year":"1997"},{"key":"10.1016\/j.jag.2025.104536_b27","first-page":"1","article-title":"Multiple crop yield estimation and forecasting using MERRA-2 model, satellite-gauge and MODIS satellite data by time series and regression modelling approach","author":"Kumar","year":"2022","journal-title":"Geo"},{"key":"10.1016\/j.jag.2025.104536_b28","first-page":"158","article-title":"Spatial variability of grape yield and its association with soil water depletion within a vineyard of arid northwest China","volume":"179","author":"Li","year":"2017","journal-title":"Awm"},{"key":"10.1016\/j.jag.2025.104536_b29","series-title":"Cvpr","first-page":"2980","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"},{"key":"10.1016\/j.jag.2025.104536_b30","series-title":"Cvpr","first-page":"2537","article-title":"Large-scale long-tailed recognition in an open world","author":"Liu","year":"2019"},{"key":"10.1016\/j.jag.2025.104536_b31","series-title":"Eccv","first-page":"181","article-title":"Exploring the limits of weakly supervised pretraining","author":"Mahajan","year":"2018"},{"key":"10.1016\/j.jag.2025.104536_b32","first-page":"191","article-title":"Field-level crop yield estimation with prisma and Sentinel-2","volume":"187","author":"Marshall","year":"2022","journal-title":"Isprs"},{"issue":"7","key":"10.1016\/j.jag.2025.104536_b33","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.5194\/acp-5-1855-2005","article-title":"The libradtran software package for radiative transfer calculations-description and examples of use","volume":"5","author":"Mayer","year":"2005","journal-title":"Acpd"},{"issue":"22","key":"10.1016\/j.jag.2025.104536_b34","doi-asserted-by":"crossref","first-page":"3636","DOI":"10.3390\/w14223636","article-title":"Monthly streamflow prediction by metaheuristic regression approaches considering satellite precipitation data","volume":"14","author":"Mehraein","year":"2022","journal-title":"Water"},{"issue":"3","key":"10.1016\/j.jag.2025.104536_b35","doi-asserted-by":"crossref","first-page":"454","DOI":"10.3390\/rs17030454","article-title":"A survey of methods for addressing imbalance data problems in agriculture applications","volume":"17","author":"Miftahushudur","year":"2025","journal-title":"Remote. Sens."},{"key":"10.1016\/j.jag.2025.104536_b36","first-page":"245","article-title":"A review of methods and regression models using satellite imageries on phytoplankton\u2019s water quality parameters estimation","volume":"48","author":"Muhamad","year":"2023","journal-title":"Iaars"},{"issue":"2","key":"10.1016\/j.jag.2025.104536_b37","doi-asserted-by":"crossref","first-page":"133","DOI":"10.30536\/j.ijreses.2022.v19.a3830","article-title":"Spatial machine learning for monitoring tea leaves and crop yield estimation using sentinel-2 imagery,(a case of Gunung Mas Plantation, Bogor)","volume":"19","author":"Nuraeni","year":"2023","journal-title":"Ijreses"},{"issue":"15","key":"10.1016\/j.jag.2025.104536_b38","doi-asserted-by":"crossref","first-page":"2436","DOI":"10.3390\/rs12152436","article-title":"Using satellite thermal-based evapotranspiration time series for defining management zones and spatial association to local attributes in a vineyard","volume":"12","author":"Ohana-Levi","year":"2020","journal-title":"Remote"},{"issue":"3","key":"10.1016\/j.jag.2025.104536_b39","doi-asserted-by":"crossref","DOI":"10.24940\/ijird\/2022\/v11\/i3\/MAR22049","article-title":"Regression models in forecasting crop yield under climate change scenarios","volume":"11","author":"Ombogo","year":"2022","journal-title":"Ijird"},{"key":"10.1016\/j.jag.2025.104536_b40","series-title":"Conit","first-page":"1","article-title":"A comparative analysis of crop yield prediction using regression","author":"Rai","year":"2022"},{"key":"10.1016\/j.jag.2025.104536_b41","series-title":"Utility-Based Regression","author":"Ribeiro","year":"2011"},{"key":"10.1016\/j.jag.2025.104536_b42","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1007\/s10994-020-05900-9","article-title":"Imbalanced regression and extreme value prediction","volume":"109","author":"Ribeiro","year":"2020","journal-title":"Mach. Learn."},{"issue":"1","key":"10.1016\/j.jag.2025.104536_b43","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.eij.2020.02.007","article-title":"A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks","volume":"22","author":"Sambasivam","year":"2021","journal-title":"Egypt. Inform. J."},{"key":"10.1016\/j.jag.2025.104536_b44","series-title":"Sentinel 2 data","author":"Sentinel2","year":"2022"},{"key":"10.1016\/j.jag.2025.104536_b45","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1007\/s10994-021-06023-5","article-title":"Density-based weighting for imbalanced regression","volume":"110","author":"Steininger","year":"2021","journal-title":"Mach. Learn."},{"issue":"1","key":"10.1016\/j.jag.2025.104536_b46","first-page":"191","article-title":"On strategies for imbalanced text classification using SVM: A comparative study","volume":"48","author":"Sun","year":"2009","journal-title":"Dss"},{"issue":"3","key":"10.1016\/j.jag.2025.104536_b47","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1111\/exsy.12081","article-title":"Resampling strategies for regression","volume":"32","author":"Torgo","year":"2015","journal-title":"Expert Syst."},{"key":"10.1016\/j.jag.2025.104536_b48","series-title":"Exploratory Data Analysis","author":"Tukey","year":"1977"},{"key":"10.1016\/j.jag.2025.104536_b49","article-title":"Learning to model the tail","volume":"30","author":"Wang","year":"2017","journal-title":"Neurips"},{"issue":"2","key":"10.1016\/j.jag.2025.104536_b50","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s10618-008-0117-y","article-title":"Guest editorial: special issue on utility-based data mining","volume":"17","author":"Weiss","year":"2008","journal-title":"Data Min. Knowl. Discov."},{"key":"10.1016\/j.jag.2025.104536_b51","doi-asserted-by":"crossref","DOI":"10.3389\/fenvs.2021.701288","article-title":"Comparison of resampling algorithms to address class imbalance when developing machine learning models to predict foodborne pathogen presence in agricultural water","volume":"9","author":"Weller","year":"2021","journal-title":"Front. Environ. Sci."},{"key":"10.1016\/j.jag.2025.104536_b52","series-title":"Icml","first-page":"11842","article-title":"Delving into deep imbalanced regression","author":"Yang","year":"2021"},{"issue":"2","key":"10.1016\/j.jag.2025.104536_b53","doi-asserted-by":"crossref","first-page":"315","DOI":"10.20870\/oeno-one.2021.55.2.4598","article-title":"Proximal sensing of vineyard soil and canopy vegetation for determining vineyard spatial variability in plant physiology and berry chemistry","volume":"55","author":"Yu","year":"2021","journal-title":"Oeno"}],"container-title":["International Journal of Applied Earth Observation and Geoinformation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1569843225001839?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1569843225001839?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T18:36:36Z","timestamp":1748284596000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1569843225001839"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":53,"alternative-id":["S1569843225001839"],"URL":"https:\/\/doi.org\/10.1016\/j.jag.2025.104536","relation":{},"ISSN":["1569-8432"],"issn-type":[{"value":"1569-8432","type":"print"}],"subject":[],"published":{"date-parts":[[2025,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Predicting crop yield lows through the highs via binned deep imbalanced regression: A case study on vineyards","name":"articletitle","label":"Article Title"},{"value":"International Journal of Applied Earth Observation and Geoinformation","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jag.2025.104536","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"104536"}}