{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T20:19:49Z","timestamp":1790799589867,"version":"4.1.0"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Smart Agricultural Technology"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1016\/j.atech.2025.101151","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T04:22:34Z","timestamp":1751430154000},"page":"101151","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["Grape vine (Vitis vinifera) yield prediction using optimized weighted ensemble machine learning approach"],"prefix":"10.1016","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4290-9661","authenticated-orcid":false,"given":"Nobin Chandra","family":"Paul","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pratapsingh S.","family":"Khapte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Navyasree","family":"Ponnaganti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sushil S.","family":"Changan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangram B.","family":"Chavan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. Ravi","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhananjay D.","family":"Nangare","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. Sammi","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.atech.2025.101151_bib0001","unstructured":"Agricultural and processed food products export development authority (APEDA). Link: https:\/\/apeda.gov.in\/Grapes."},{"issue":"4","key":"10.1016\/j.atech.2025.101151_bib0002","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/0168-1699(95)98601-9","article-title":"Applying machine learning to agricultural data","volume":"12","author":"McQueen","year":"1995","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.atech.2025.101151_bib0003","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.ecolind.2019.01.059","article-title":"Design of an integrated climatic assessment indicator (ICAI) for wheat production: a case study in Jiangsu Province","volume":"101","author":"Xu","year":"2019","journal-title":"China. Ecol. Ind."},{"key":"10.1016\/j.atech.2025.101151_bib0004","article-title":"Crop yield prediction using machine learning: a systematic literature review","volume":"177","author":"Klompenburg","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.atech.2025.101151_bib0005","series-title":"2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS)","first-page":"1466","article-title":"Crop yield prediction using machine learning algorithm","author":"Reddy","year":"2021"},{"key":"10.1016\/j.atech.2025.101151_bib0006","doi-asserted-by":"crossref","first-page":"63406","DOI":"10.1109\/ACCESS.2021.3075159","article-title":"A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction","volume":"9","author":"Rashid","year":"2021","journal-title":"IEEE. Access"},{"key":"10.1016\/j.atech.2025.101151_bib0007","series-title":"2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)","first-page":"963","article-title":"An ensemble algorithm for crop yield prediction","author":"Keerthana","year":"2021"},{"issue":"1","key":"10.1016\/j.atech.2025.101151_bib0008","article-title":"A ML-AI enabled ensemble model for predicting agricultural yield","volume":"8","author":"Kundu","year":"2022","journal-title":"Cogent. Food. Agric."},{"key":"10.1016\/j.atech.2025.101151_bib0009","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s11831-021-09569-8","article-title":"Emerging trends in machine learning to predict crop yield and study its influential factors: a survey","volume":"29","author":"Bali","year":"2022","journal-title":"Arch. Comput. Methods. Eng"},{"key":"10.1016\/j.atech.2025.101151_bib0010","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.3390\/agriculture12091345","article-title":"A machine learning model for early prediction of crop yield, nested in a web application in the cloud: a case study in an olive grove in southern Spain","volume":"12","author":"Cubillas","year":"2022","journal-title":"Agriculture"},{"issue":"1","key":"10.1016\/j.atech.2025.101151_bib0011","first-page":"710","article-title":"Crop yield prediction using shuffled shepherd optimization algorithm based ACNN-OBDLSTM model in Smart Agriculture","volume":"12","author":"Rao","year":"2024","journal-title":"J. Integr. Sci. Technol."},{"key":"10.1016\/j.atech.2025.101151_bib0012","series-title":"2024 2nd International Conference on Computer, Communication and Control (IC4)","first-page":"1","article-title":"An adaptive deep learning model for crop yield prediction","author":"Gadupudi","year":"2024"},{"key":"10.1016\/j.atech.2025.101151_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108439","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":"Comput. Electron. Agric."},{"issue":"9","key":"10.1016\/j.atech.2025.101151_bib0014","doi-asserted-by":"crossref","first-page":"5197","DOI":"10.1002\/jsfa.13362","article-title":"Models for predicting coffee yield from chemical characteristics of soil and leaves using machine learning","volume":"104","author":"De Oliveira Faria","year":"2024","journal-title":"J. Sci. Food. Agric"},{"issue":"10","key":"10.1016\/j.atech.2025.101151_bib0015","doi-asserted-by":"crossref","DOI":"10.1088\/2515-7620\/ad7e81","article-title":"Crop yield prediction with environmental and chemical variables using optimized ensemble predictive model in machine learning","volume":"6","author":"Krishnadoss","year":"2024","journal-title":"Environ. Res. Commun."},{"key":"10.1016\/j.atech.2025.101151_bib0016","article-title":"Crop yield prediction using effective deep learning and dimensionality reduction approaches for Indian regional crops. E-Prime-Advances in Electrical engineering","volume":"8","author":"Subramaniam","year":"2024","journal-title":"Electron. Energy"},{"key":"10.1016\/j.atech.2025.101151_bib0017","series-title":"In 2019 Fifth International Conference on Image Information Processing (ICIIP)","first-page":"125","article-title":"Crop yield prediction using machine learning algorithms","author":"Nigam","year":"2019"},{"key":"10.1016\/j.atech.2025.101151_bib0018","first-page":"10922","article-title":"Analysis of agricultural crop yield prediction using statistical techniques of machine learning","volume":"46","author":"Pant","year":"2021","journal-title":"Mater. Today:. Proc."},{"key":"10.1016\/j.atech.2025.101151_bib0019","series-title":"International Conference on Computational Intelligence","first-page":"485","article-title":"Crop yield prediction using stacking ensemble model","author":"Rao","year":"2023"},{"issue":"6","key":"10.1016\/j.atech.2025.101151_bib0020","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1016\/j.jia.2023.02.011","article-title":"Ensemble learning prediction of soybean yields in China based on meteorological data","volume":"22","author":"Li","year":"2023","journal-title":"J. Integr. Agric"},{"issue":"5","key":"10.1016\/j.atech.2025.101151_bib0021","doi-asserted-by":"crossref","first-page":"518","DOI":"10.18520\/cs\/v125\/i5\/518-529","article-title":"Spatial approach for the estimation of average yield of cotton using reduced number of crop cutting experiments","volume":"125","author":"Paul","year":"2023","journal-title":"Curr. Sci"},{"issue":"1","key":"10.1016\/j.atech.2025.101151_bib0022","doi-asserted-by":"crossref","first-page":"225","DOI":"10.3390\/agriculture13010225","article-title":"Crop yield prediction using machine learning models: case of Irish potato and maize","volume":"13","author":"Kuradusenge","year":"2023","journal-title":"Agriculture"},{"issue":"8","key":"10.1016\/j.atech.2025.101151_bib0023","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42979-024-03536-3","article-title":"Crop yield prediction using ensemble machine learning techniques","volume":"5","author":"Kuppan","year":"2024","journal-title":"SN. Comput. Sci."},{"key":"10.1016\/j.atech.2025.101151_bib0024","series-title":"2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), (I-SMAC)","first-page":"1831","article-title":"Predicting crop yield based on stacking ensemble model in machine learning","author":"Umamaheswari","year":"2024"},{"issue":"7","key":"10.1016\/j.atech.2025.101151_bib0025","doi-asserted-by":"crossref","first-page":"284","DOI":"10.3390\/drones8070284","article-title":"Wheat yield prediction using machine learning method based on UAV remote sensing data","volume":"8","author":"Yang","year":"2024","journal-title":"Drones"},{"key":"10.1016\/j.atech.2025.101151_bib0026","article-title":"Field scale wheat yield prediction using ensemble machine learning techniques","volume":"9","author":"Gawdiya","year":"2024","journal-title":"Smart. Agric. Technol."},{"issue":"8","key":"10.1016\/j.atech.2025.101151_bib0027","doi-asserted-by":"crossref","first-page":"3313","DOI":"10.3390\/app14083313","article-title":"Enhancing crop yield predictions with PEnsemble 4: ioT and ML-driven for precision agriculture","volume":"14","author":"Pukrongta","year":"2024","journal-title":"Appl. Sci."},{"key":"10.1016\/j.atech.2025.101151_bib0028","article-title":"Ensemble regression based Extra Tree Regressor for hybrid crop yield prediction system","volume":"35","author":"Sudhamathi","year":"2024","journal-title":"Meas.:. Sens."},{"key":"10.1016\/j.atech.2025.101151_bib0029","doi-asserted-by":"crossref","unstructured":"Yewle A.D., Mirzayeva L., Karaku\u015f O. Multi-modal Data fusion and deep ensemble learning for accurate crop yield prediction, 2025; arXiv. prepr.arXiv:2502.06062.","DOI":"10.2139\/ssrn.5185505"},{"key":"10.1016\/j.atech.2025.101151_bib0030","series-title":"2011 3rd International Conference on Electronics Computer Technology","first-page":"209","article-title":"Feature reduction using principal component analysis for agricultural data set","volume":"2","author":"Mishra","year":"2011"},{"key":"10.1016\/j.atech.2025.101151_bib0031","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.ecolind.2018.09.025","article-title":"Use of principal component analysis for parameter selection for development of a novel water quality index: a case study of river Ganga India","volume":"96","author":"Tripathi","year":"2019","journal-title":"Ecol. Indic"},{"key":"10.1016\/j.atech.2025.101151_bib0032","first-page":"1","article-title":"Selection of the minimum data set and quantitative soil quality indices for different azalea forest communities in southwestern China","author":"Cao","year":"2024","journal-title":"Plant. Soil"},{"key":"10.1016\/j.atech.2025.101151_bib0033","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s11240-010-9776-7","article-title":"Transgenic tomato cv. Pusa Uphar expressing a bacterial mannitol-1-phosphate dehydrogenase gene confers abiotic stress tolerance","volume":"103","author":"Khare","year":"2010","journal-title":"Plant. Cell. Tissue. Organ. Cult"},{"key":"10.1016\/j.atech.2025.101151_bib0034","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1023\/A:1010729821876","article-title":"Kinetic imaging of chlorophyll fluorescence using modulated light","volume":"66","author":"Nedbal","year":"2000","journal-title":"Photosyn. Res"},{"issue":"7","key":"10.1016\/j.atech.2025.101151_bib0035","first-page":"2534","article-title":"Effect of \u03b3-radiation on post-harvest storage life and quality of onion bulb under ambient condition","volume":"57","author":"Sharma","year":"2020","journal-title":"Food. Sci. Technol"},{"issue":"1","key":"10.1016\/j.atech.2025.101151_bib0036","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/S0167-8809(01)00174-8","article-title":"A comparison of soil quality indexing methods for vegetable production systems in Northern California","volume":"90","author":"Andrews","year":"2002","journal-title":"Agricul., Ecosyst. Environ."},{"key":"10.1016\/j.atech.2025.101151_bib0037","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.2136\/sssaj2000.6462115x","article-title":"Identification of regional soilquality factors and indicators. I. Central and Southern High-Plains","volume":"64","author":"Brejda","year":"2000","journal-title":"Soil. Sci. Soc.Am. J"},{"key":"10.1016\/j.atech.2025.101151_bib0038","first-page":"5","volume":"45","year":"2001"},{"key":"10.1016\/j.atech.2025.101151_bib0039","series-title":"Neural Networks for Pattern Recognition","author":"Bishop","year":"1995"},{"key":"10.1016\/j.atech.2025.101151_bib0040","series-title":"Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining","first-page":"785","article-title":"Xgboost: a scalable tree boosting system","author":"Chen","year":"2016"},{"issue":"1","key":"10.1016\/j.atech.2025.101151_bib0041","first-page":"1","article-title":"Multivariate adaptive regression splines","volume":"19","author":"Friedman","year":"1991","journal-title":"Ann. Stat."},{"key":"10.1016\/j.atech.2025.101151_bib0042","article-title":"Forecasting recessions: can we do better on MARS","volume":"83","author":"Sephton","year":"2001","journal-title":"Fed. Reserve. Bank. St. Louis. Rev."},{"key":"10.1016\/j.atech.2025.101151_bib0043","first-page":"4","volume":"2","author":"Williams","year":"2006"},{"key":"10.1016\/j.atech.2025.101151_bib0044","first-page":"155","article-title":"Support vector regression machines","volume":"9","author":"Drucker","year":"1997","journal-title":"Adv. Neural. Inf. Process. Syst"},{"key":"10.1016\/j.atech.2025.101151_bib0045","series-title":"The Nature of Statistical Learning Theory","author":"Vapnik","year":"1995"},{"key":"10.1016\/j.atech.2025.101151_bib0046","series-title":"Empirical inference: Festschrift in Honor of Vladimir N. Vapnik","first-page":"13","article-title":"Early history of support vector machines","author":"Chervonenkis","year":"2013"},{"key":"10.1016\/j.atech.2025.101151_bib0047","doi-asserted-by":"crossref","first-page":"2457","DOI":"10.1007\/s00477-018-1585-2","article-title":"Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment","volume":"32","author":"Yin","year":"2018","journal-title":"Stoch. Environ. Res. Risk. Assess."},{"key":"10.1016\/j.atech.2025.101151_bib0048","article-title":"Optimizing ensemble weights and hyperparameters of machine learning models for regression problems","volume":"7","author":"Shahhosseini","year":"2022","journal-title":"Mach. Learn. Appl."},{"issue":"3","key":"10.1016\/j.atech.2025.101151_bib0049","first-page":"492","article-title":"A stacking ensemble learning model for monthly rainfall prediction in the Taihu Basin, China","volume":"14","author":"Gu","year":"2022","journal-title":"Water. (Basel)"},{"issue":"1","key":"10.1016\/j.atech.2025.101151_bib0050","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1002\/wics.14","article-title":"Ridge regression","volume":"1","author":"McDonald","year":"2009","journal-title":"Wiley. Interdiscip. Rev.:. Comput. Stat."},{"key":"10.1016\/j.atech.2025.101151_bib0051","series-title":"Applied Predictive Modeling","first-page":"13","author":"Kuhn","year":"2013"},{"key":"10.1016\/j.atech.2025.101151_bib0052","first-page":"18","article-title":"Classifcation and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R. News"},{"key":"10.1016\/j.atech.2025.101151_bib0053","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v011.i09","article-title":"Zeileis A. kernlab-an S4 package for kernel methods in {R}","volume":"11","author":"Karatzoglou","year":"2004","journal-title":"J. Stat. Softw"},{"key":"10.1016\/j.atech.2025.101151_bib0054","series-title":"R: a Language and Environment For Statistical Computing","year":"2022"},{"issue":"3","key":"10.1016\/j.atech.2025.101151_bib0055","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models part I-A discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol"},{"key":"10.1016\/j.atech.2025.101151_bib0056","series-title":"EGU general assembly conference abstracts","first-page":"237","article-title":"Application of a normalized Nash-Sutcliffe efficiency to improve the accuracy of the Sobol'sensitivity analysis of a hydrological model","author":"Nossent","year":"2012"},{"key":"10.1016\/j.atech.2025.101151_bib0057","series-title":"Agricultural Statistics at a Glance 2023, Directorate of Economics and Statistics","year":"2023"},{"key":"10.1016\/j.atech.2025.101151_bib0058","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. Env."},{"issue":"7","key":"10.1016\/j.atech.2025.101151_bib0059","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1080\/2150704X.2025.2502176","article-title":"Optimized soil adjusted vegetation index mapping of Pune district using Google Earth Engine","volume":"16","author":"Paul","year":"2025","journal-title":"Remote. Sens. Lett."},{"key":"10.1016\/j.atech.2025.101151_bib0060","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s40009-025-01617-2","article-title":"Modified normalized difference water index mapping of Pune District using Google Earth Engine","volume":"48","author":"Paul","year":"2025","journal-title":"Natl. Acad. Sci. Lett."}],"container-title":["Smart Agricultural Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2772375525003831?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2772375525003831?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T10:33:29Z","timestamp":1765967609000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2772375525003831"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":60,"alternative-id":["S2772375525003831"],"URL":"https:\/\/doi.org\/10.1016\/j.atech.2025.101151","relation":{},"ISSN":["2772-3755"],"issn-type":[{"value":"2772-3755","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Grape vine (Vitis vinifera) yield prediction using optimized weighted ensemble machine learning approach","name":"articletitle","label":"Article Title"},{"value":"Smart Agricultural Technology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.atech.2025.101151","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"101151"}}