{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T18:17:50Z","timestamp":1788286670832,"version":"build-2803163510"},"reference-count":65,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Accurate crop yield prediction is essential for agricultural planning, yet machine learning (ML) models remain highly sensitive to the quality and structure of input data. This study uses simulated datasets to systematically investigate how data structure (sample size and number of predictors), data imperfections (missing values and multicollinearity), and pre-processing methods (imputation techniques and principal component analysis) influence ML regression performance. The performance of ML-based models (RF, SVM, MLR, XGBoost, LightGBM, NNet, and kNN) on the pre-processed data was evaluated. In total, each algorithm was tested on\u00a01,728 datasets and pre-processing scenarios, yielding 12,096 model-scenario evaluations across the seven ML algorithms. Results show that missing data decreases performance (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    drops by up to 20.63%; MAE increases by 12.67%), while multicollinearity may inflate\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    values despite poorer MAE performance. Larger sample sizes consistently improve prediction accuracy (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u2009+18.87%; MAE\u2009=\u2009\u22128.16%), whereas more predictors generally reduce it (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u2009\u22128.47%; MAE\u2009=\u2009+44.74%). Regression-based imputation improved both\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    and MAE the most, while RF demonstrated greater robustness across varying conditions. These simulation findings were further validated on five real-world crop datasets (Maize, Yam, Cassava, Sorghum, and Peanuts), confirming that despite RF remains the safest and most robust default, no single pre-processing strategy is universally optimal and that the selection of imputation method and dimensionality reduction must be strictly contingent upon the dataset\u2019s specific missingness rate and correlation structure. Overall, this study highlights the complex interplay between data characteristics and pre-processing, urging the development of clearer guidelines for applying ML to agricultural datasets.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0353938","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T17:53:57Z","timestamp":1788285237000},"page":"e0353938","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sensitivity of machine learning regression models to data structure and quality in crop yield prediction"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3480-112X","authenticated-orcid":true,"given":"Mahoukp\u00e9go Luc","family":"Zinzinh\u00e9do","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M\u00e8vognon Firmin","family":"Mitchozounnou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kolawol\u00e9 Val\u00e8re","family":"Salako","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Romain","family":"Gl\u00e8l\u00e8 Kaka\u00ef","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"pone.0353938.ref001","first-page":"022022","article-title":"An Overview of Overfitting and its Solutions","volume":"1168","author":"X Ying","year":"2019","journal-title":"J Phys: Conf Ser"},{"key":"pone.0353938.ref002","doi-asserted-by":"crossref","first-page":"111903","DOI":"10.1016\/j.buildenv.2024.111903","article-title":"Ten questions concerning statistical data analysis in human-centric buildings research: A focus on thermal comfort investigations","volume":"264","author":"M Favero","year":"2024","journal-title":"Build Environ"},{"issue":"1","key":"pone.0353938.ref003","doi-asserted-by":"crossref","first-page":"7262","DOI":"10.1038\/s41467-024-51555-8","article-title":"Preseason maize and wheat yield forecasts for early warning of crop failure","volume":"15","author":"W Anderson","year":"2024","journal-title":"Nat Commun"},{"issue":"10","key":"pone.0353938.ref004","doi-asserted-by":"crossref","first-page":"6421","DOI":"10.1002\/mp.16437","article-title":"Impact of imperfection in medical imaging data on deep learning-based segmentation performance: An experimental study using synthesized data","volume":"50","author":"AM G\u00fcne\u015f","year":"2023","journal-title":"Med Phys"},{"key":"pone.0353938.ref005","doi-asserted-by":"crossref","unstructured":"Goel K, Sadeghianasl S, Andrews R, Ter Hofstede A, Wynn M, Kapugama Geeganage D, et al. Digital Health Data Imperfection Patterns and Their Manifestations in an Australian Digital Hospital. In: Proceedings of the Annual Hawaii International Conference on System Sciences. 2023. p. 3235\u201344.","DOI":"10.24251\/HICSS.2023.398"},{"issue":"15","key":"pone.0353938.ref006","doi-asserted-by":"crossref","first-page":"331","DOI":"10.4054\/DemRes.2012.26.15","article-title":"Estimating trends in the total fertility rate with uncertainty using imperfect data: Examples from West Africa","volume":"26","author":"L Alkema","year":"2012","journal-title":"Demogr Res"},{"issue":"1","key":"pone.0353938.ref007","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s12992-021-00795-0","article-title":"COVID-19 data are messy: analytic methods for rigorous impact analyses with imperfect data","volume":"18","author":"MA Stoto","year":"2022","journal-title":"Global Health"},{"key":"pone.0353938.ref008","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.4054\/DemRes.2020.43.38","article-title":"Estimation of older adult mortality from imperfect data: A comparative review of methods using Burkina Faso censuses","volume":"43","author":"S Ouedraogo","year":"2020","journal-title":"DemRes"},{"issue":"1","key":"pone.0353938.ref009","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3390\/fishes7010052","article-title":"Modelling Fish Growth with Imperfect Data: The Case of Trachurus picturatus","volume":"7","author":"A Neves","year":"2022","journal-title":"Fishes"},{"issue":"5","key":"pone.0353938.ref010","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.2134\/jeq2009.0449","article-title":"Influence of data preprocessing on the quantitative determination of nutrient content in poultry manure by near infrared spectroscopy","volume":"39","author":"LJ Chen","year":"2010","journal-title":"J Environ Qual"},{"issue":"1","key":"pone.0353938.ref011","doi-asserted-by":"crossref","DOI":"10.3390\/agriculture13010095","article-title":"Smart Weather Data Management Based on Artificial Intelligence and Big Data Analytics for Precision Agriculture","volume":"13","author":"CE Hachimi","year":"2023","journal-title":"Agriculture"},{"key":"pone.0353938.ref012","article-title":"Multiple regression for physiological data analysis: the problem of multicollinearity","volume":"249","author":"BK Slinker","year":"1985","journal-title":"Am J Physiol"},{"key":"pone.0353938.ref013","doi-asserted-by":"crossref","unstructured":"Follin JM, Girres JF, Olteanuraimond AM, Sheeren D. The origins of imperfection in geographic data [Book chapter]. Geographic Data Imperfection 1: From Theory to Applications. 2019. p. 25\u201344.","DOI":"10.1002\/9781119507284.ch3"},{"issue":"1","key":"pone.0353938.ref014","doi-asserted-by":"crossref","first-page":"6","DOI":"10.3847\/1538-4365\/ad7c4a","article-title":"Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters","volume":"275","author":"M EskandariNasab","year":"2024","journal-title":"ApJS"},{"issue":"9","key":"pone.0353938.ref015","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.1007\/s11269-024-03806-y","article-title":"Evaluating the Performance of Several Data Preprocessing Methods Based on GRU in Forecasting Monthly Runoff Time Series","volume":"38","author":"W Wang","year":"2024","journal-title":"Water Resour Manage"},{"issue":"3","key":"pone.0353938.ref016","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1108\/eb001000","article-title":"MANAGEMENT SCIENCE AND THE CHIEF EXECUTIVE","volume":"10","author":"D Hertz","year":"1972","journal-title":"Manage Decis"},{"key":"pone.0353938.ref017","doi-asserted-by":"crossref","DOI":"10.5334\/dsj-2024-044","article-title":"Identifying Inconsistencies in Data Quality Between FAOSTAT, WOAH, UN Agriculture Census, and National Data","volume":"23","author":"I McKechnie","year":"2024","journal-title":"Data Sci J"},{"issue":"15","key":"pone.0353938.ref018","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.3390\/rs11151745","article-title":"Potato Yield Prediction Using Machine Learning Techniques and Sentinel 2 Data","volume":"11","author":"D G\u00f3mez","year":"2019","journal-title":"Remote Sens"},{"issue":"7","key":"pone.0353938.ref019","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.3390\/app9071459","article-title":"Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands","volume":"9","author":"H Mao","year":"2019","journal-title":"Appl Sci"},{"issue":"3","key":"pone.0353938.ref020","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/grs.12233","article-title":"Yield modeling for prediction of regional whole\u2010crop barley productivity","volume":"65","author":"L Guan","year":"2019","journal-title":"Grassland Sci"},{"key":"pone.0353938.ref021","doi-asserted-by":"crossref","unstructured":"Malik P, Sengupta S, Jadon JS. Comparative Analysis of Soil Properties to Predict Fertility and Crop Yield using Machine Learning Algorithms. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). 2021. p. 1004\u20137.","DOI":"10.1109\/Confluence51648.2021.9377147"},{"key":"pone.0353938.ref022","doi-asserted-by":"crossref","first-page":"1000224","DOI":"10.3389\/fpls.2022.1000224","article-title":"County-scale crop yield prediction by integrating crop simulation with machine learning models","volume":"13","author":"SS Sajid","year":"2022","journal-title":"Front Plant Sci"},{"issue":"3","key":"pone.0353938.ref023","doi-asserted-by":"crossref","DOI":"10.1088\/2631-8695\/ad780f","article-title":"Data analytics in ensemble learning for effective crop yield prediction","volume":"6","author":"D Tripathi","year":"2024","journal-title":"Eng Res Express"},{"issue":"11","key":"pone.0353938.ref024","doi-asserted-by":"crossref","first-page":"3004","DOI":"10.3390\/en14113004","article-title":"Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling","volume":"14","author":"K Alibabaei","year":"2021","journal-title":"Energies"},{"issue":"14","key":"pone.0353938.ref025","doi-asserted-by":"crossref","first-page":"40393","DOI":"10.1007\/s11042-023-17178-9","article-title":"Analysis of missing data and comparing the accuracy of imputation methods using wheat crop data","volume":"83","author":"P Saini","year":"2023","journal-title":"Multimed Tools Appl"},{"issue":"12","key":"pone.0353938.ref026","doi-asserted-by":"crossref","first-page":"312","DOI":"10.3390\/computers13120312","article-title":"PM2.5 Time Series Imputation with Moving Averages, Smoothing, and Linear Interpolation","volume":"13","author":"A Flores","year":"2024","journal-title":"Computers"},{"issue":"4","key":"pone.0353938.ref027","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1080\/02664763.2015.1077377","article-title":"Missing value imputation method for disaster decision-making using K nearest neighbor","volume":"43","author":"X Ma","year":"2015","journal-title":"J Appl Stat"},{"key":"pone.0353938.ref028","doi-asserted-by":"crossref","unstructured":"Lee Y, Leite WL. A comparison of random forest-based missing imputation methods for covariates in propensity score analysis. Psychol Methods. 2024. doi: 10.1037\/met0000676 38869857","DOI":"10.1037\/met0000676"},{"issue":"1","key":"pone.0353938.ref029","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1002\/mpr.329","article-title":"Multiple imputation by chained equations: what is it and how does it work?","volume":"20","author":"MJ Azur","year":"2011","journal-title":"Int J Methods Psychiatr Res"},{"issue":"10","key":"pone.0353938.ref030","doi-asserted-by":"crossref","first-page":"3313","DOI":"10.1007\/s00500-017-2567-x","article-title":"A fuzzy K-nearest neighbor classifier to deal with imperfect data","volume":"22","author":"JM Cadenas","year":"2017","journal-title":"Soft Comput"},{"issue":"3","key":"pone.0353938.ref031","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.jss.2010.11.887","article-title":"Missing data imputation by utilizing information within incomplete instances","volume":"84","author":"S Zhang","year":"2011","journal-title":"J Syst Softw"},{"issue":"16","key":"pone.0353938.ref032","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.3390\/rs13163322","article-title":"Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning","volume":"13","author":"D Li","year":"2021","journal-title":"Remote Sens"},{"issue":"7","key":"pone.0353938.ref033","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.3390\/agronomy10071046","article-title":"Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms","volume":"10","author":"F Abbas","year":"2020","journal-title":"Agronomy"},{"key":"pone.0353938.ref034","doi-asserted-by":"crossref","first-page":"100048","DOI":"10.1016\/j.atech.2022.100048","article-title":"Agricultural decision system based on advanced machine learning models for yield prediction: Case of East African countries","volume":"2","author":"R Aworka","year":"2022","journal-title":"Smart Agric Technol"},{"issue":"7","key":"pone.0353938.ref035","doi-asserted-by":"crossref","first-page":"432","DOI":"10.3390\/drones7070432","article-title":"Individualized Indicators and Estimation Methods for Tiger Nut (Cyperus esculentus L.) Tubers Yield Using Light Multispectral UAV and Lightweight CNN Structure","volume":"7","author":"D Li","year":"2023","journal-title":"Drones"},{"issue":"8","key":"pone.0353938.ref036","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0230888","article-title":"Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada","volume":"15","author":"Z Coulibali","year":"2020","journal-title":"PLoS One"},{"key":"pone.0353938.ref037","doi-asserted-by":"crossref","first-page":"103016","DOI":"10.1016\/j.agsy.2020.103016","article-title":"Machine learning for large-scale crop yield forecasting","volume":"187","author":"D Paudel","year":"2021","journal-title":"Agric Syst"},{"key":"pone.0353938.ref038","doi-asserted-by":"crossref","first-page":"127254","DOI":"10.1016\/j.eja.2024.127254","article-title":"Machine learning based on functional principal component analysis to quantify the effects of the main drivers of wheat yields","volume":"159","author":"F Bonneu","year":"2024","journal-title":"Eur J Agron"},{"issue":"7","key":"pone.0353938.ref039","doi-asserted-by":"crossref","DOI":"10.3390\/drones8070284","article-title":"Wheat yield prediction using machine learning method based on UAV remote sensing data","volume":"8","author":"S Yang","year":"2024","journal-title":"Drones"},{"issue":"8","key":"pone.0353938.ref040","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1016\/j.csda.2005.03.011","article-title":"Using principal components for estimating logistic regression with high-dimensional multicollinear data","volume":"50","author":"AM Aguilera","year":"2006","journal-title":"Computat Stat Data Analys"},{"issue":"17","key":"pone.0353938.ref041","article-title":"Evaluation of three feature dimension reduction techniques for machine learning-based crop yield prediction models","volume":"22","author":"HT Pham","year":"2022","journal-title":"Sensors"},{"issue":"1","key":"pone.0353938.ref042","doi-asserted-by":"crossref","first-page":"17754","DOI":"10.1038\/s41598-021-97221-7","article-title":"An interaction regression model for crop yield prediction","volume":"11","author":"J Ansarifar","year":"2021","journal-title":"Sci Rep"},{"issue":"12","key":"pone.0353938.ref043","doi-asserted-by":"crossref","DOI":"10.3390\/agriculture13122259","article-title":"Prediction of Potato (Solanum tuberosum L.) Yield Based on Machine Learning Methods","volume":"13","author":"J Kurek","year":"2023","journal-title":"Agriculture"},{"issue":"18","key":"pone.0353938.ref044","doi-asserted-by":"crossref","first-page":"5293","DOI":"10.3390\/s20185293","article-title":"Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning","volume":"20","author":"C Sun","year":"2020","journal-title":"Sensors (Basel)"},{"issue":"9","key":"pone.0353938.ref045","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0330454","article-title":"DiabetesXpertNet: An innovative attention-based CNN for accurate type 2 diabetes prediction","volume":"20","author":"R Farnoosh","year":"2025","journal-title":"PLoS One"},{"issue":"1","key":"pone.0353938.ref046","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1186\/s12859-023-05465-z","article-title":"Prediction of diabetes disease using an ensemble of machine learning multi-classifier models","volume":"24","author":"K Abnoosian","year":"2023","journal-title":"BMC Bioinform"},{"key":"pone.0353938.ref047","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.infsof.2015.07.004","article-title":"An empirical analysis of data preprocessing for machine learning-based software cost estimation","volume":"67","author":"J Huang","year":"2015","journal-title":"Inform Softw Technol"},{"key":"pone.0353938.ref048","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.procs.2019.12.111","article-title":"Data dimensional reduction and principal components analysis","volume":"163","author":"N Salem","year":"2019","journal-title":"Procedia Comput Sci"},{"issue":"1","key":"pone.0353938.ref049","first-page":"1","article-title":"Multivariate Adaptive Regression Splines","volume":"19","author":"HF Jerome","year":"1991","journal-title":"The Annals of Statistics"},{"key":"pone.0353938.ref050","doi-asserted-by":"crossref","first-page":"21689","DOI":"10.1038\/srep21689","article-title":"Multiple Imputation for General Missing Data Patterns in the Presence of High-dimensional Data","volume":"6","author":"Y Deng","year":"2016","journal-title":"Sci Rep"},{"issue":"5","key":"pone.0353938.ref051","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1177\/0962280213511027","article-title":"Multiple imputation in the presence of high-dimensional data","volume":"25","author":"Y Zhao","year":"2016","journal-title":"Stat Methods Med Res"},{"key":"pone.0353938.ref052","unstructured":"Liu Y, Wang Y, Zhang J. New machine learning algorithm: Random forest. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2012. p. 246\u201352. Available from: https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-84867640767&doi=10.1007%2f978-3-642-34062-8_32&partnerID=40&md5=73f4e3124d7e3beefa2405665c761b72"},{"issue":"6","key":"pone.0353938.ref053","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0156571","article-title":"Random Forests for Global and Regional Crop Yield Predictions","volume":"11","author":"JH Jeong","year":"2016","journal-title":"PLoS One"},{"key":"pone.0353938.ref054","volume-title":"Support vector regression","author":"F Zhang","year":"2019"},{"issue":"2","key":"pone.0353938.ref055","first-page":"182","article-title":"Prediction of Rice Cultivation in India\u2014Support Vector Regression Approach with Various Kernels for Non-Linear Patterns","volume":"3","author":"KK Paidipati","year":"2021","journal-title":"AgriEng"},{"key":"pone.0353938.ref056","unstructured":"Wu Y. Linear regression in machine learning [Conference paper]. Proceedings of SPIE - The International Society for Optical Engineering. 2022. 12163 p. Available from: https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85131830947&doi=10.1117%2f12.2628053&partnerID=40&md5=e31f3a4b9bec0fcdc67c6190a09f0acf"},{"issue":"5","key":"pone.0353938.ref057","doi-asserted-by":"crossref","first-page":"885","DOI":"10.3390\/agronomy11050885","article-title":"The Application of Multiple Linear Regression and Artificial Neural Network Models for Yield Prediction of Very Early Potato Cultivars before Harvest","volume":"11","author":"M Piekutowska","year":"2021","journal-title":"Agronomy"},{"issue":"11","key":"pone.0353938.ref058","doi-asserted-by":"crossref","first-page":"218","DOI":"10.21037\/atm.2016.03.37","article-title":"Introduction to machine learning: k-nearest neighbors","volume":"4","author":"Z Zhang","year":"2016","journal-title":"Ann Transl Med"},{"key":"pone.0353938.ref059","doi-asserted-by":"crossref","first-page":"70","DOI":"10.12688\/f1000research.75469.2","article-title":"K-nearest-neighbor algorithm to predict the survival time and classification of various stages of oral cancer: a machine learning approach","volume":"11","author":"R Siddalingappa","year":"2023","journal-title":"F1000Res"},{"issue":"23","key":"pone.0353938.ref060","doi-asserted-by":"crossref","first-page":"2873","DOI":"10.3390\/rs11232873","article-title":"Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques","volume":"11","author":"A Kayad","year":"2019","journal-title":"Remote Sens"},{"issue":"5","key":"pone.0353938.ref061","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1007\/s11119-022-09897-0","article-title":"Statistical and machine learning methods for crop yield prediction in the context of precision agriculture","volume":"23","author":"H Burdett","year":"2022","journal-title":"Precision Agric"},{"issue":"1","key":"pone.0353938.ref062","doi-asserted-by":"crossref","first-page":"3393","DOI":"10.1038\/s41598-020-59943-y","article-title":"Agricultural productivity in relation to climate and cropland management in West Africa","volume":"10","author":"A Mechiche-Alami","year":"2020","journal-title":"Sci Rep"},{"key":"pone.0353938.ref063","first-page":"174","article-title":"Machine learning techniques for cereal crops yield prediction: A comprehensive review","volume":"8","author":"SPG Tahi","year":"2024","journal-title":"Appl Model Simul"},{"key":"pone.0353938.ref064","doi-asserted-by":"crossref","first-page":"105709","DOI":"10.1016\/j.compag.2020.105709","article-title":"Crop yield prediction using machine learning: A systematic literature review","volume":"177","author":"T van Klompenburg","year":"2020","journal-title":"Comput Electron Agric"},{"issue":"9","key":"pone.0353938.ref065","doi-asserted-by":"crossref","first-page":"422","DOI":"10.3390\/a16090422","article-title":"Machine-Learning-Based Imputation Method for Filling Missing Values in Ground Meteorological Observation Data","volume":"16","author":"C Li","year":"2023","journal-title":"Algorithms"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0353938","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T17:54:12Z","timestamp":1788285252000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0353938"}},"subtitle":[],"editor":[{"given":"Rajib","family":"Chowdhury","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,9,1]]},"references-count":65,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9,1]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0353938","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,1]]}}}