{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T17:05:19Z","timestamp":1779728719066,"version":"3.53.1"},"reference-count":99,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFD1901203"],"award-info":[{"award-number":["2023YFD1901203"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.compag.2026.111773","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T21:13:09Z","timestamp":1776201189000},"page":"111773","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Comparative evaluation of machine learning models integrated with optimization algorithms for daily transpiration estimation in citrus orchards of dry-hot valley region in China"],"prefix":"10.1016","volume":"248","author":[{"given":"Mingqing","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dianyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyu","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aoting","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wengang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2026.111773_b0005","doi-asserted-by":"crossref","first-page":"4455","DOI":"10.5194\/bg-20-4455-2023","article-title":"Sap flow and leaf gas exchange response to drought and a heatwave in urban green spaces in a Nordic city","volume":"20","author":"Ahongshangbam","year":"2023","journal-title":"Biogeosciences"},{"key":"10.1016\/j.compag.2026.111773_b0010","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.3389\/fpls.2019.01684","article-title":"Quantitative estimation of leaf heat transfer coefficients by active thermography at varying boundary layer conditions","volume":"10","author":"Albrecht","year":"2020","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111773_b0015","article-title":"Crop evapotranspiration-guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56","volume":"300","author":"Allen","year":"1998","journal-title":"FAO"},{"key":"10.1016\/j.compag.2026.111773_b0020","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.jhydrol.2015.06.057","article-title":"Global performance ranking of temperature-based approaches for evapotranspiration estimation considering K\u00f6ppen climate classes","volume":"528","author":"Almorox","year":"2015","journal-title":"J. Hydrol."},{"key":"10.1016\/j.compag.2026.111773_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2023.108324","article-title":"A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data","volume":"284","author":"Amani","year":"2023","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0030","first-page":"79","article-title":"Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley","volume":"39","author":"Bendig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.compag.2026.111773_b0035","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A comparative analysis of gradient boosting algorithms","volume":"54","author":"Bent\u00e9jac","year":"2021","journal-title":"Artif. Intell. Rev."},{"issue":"1","key":"10.1016\/j.compag.2026.111773_b0040","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"The Journal of Machine Learning Research."},{"key":"10.1016\/j.compag.2026.111773_b0045","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.geoderma.2007.10.022","article-title":"Correction of TDR-based soil water content measurements in conductive soils","volume":"143","author":"Bittelli","year":"2008","journal-title":"Geoderma"},{"key":"10.1016\/j.compag.2026.111773_b0050","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"issue":"3","key":"10.1016\/j.compag.2026.111773_b0055","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1093\/treephys\/tpv138","article-title":"Fruit load governs transpiration of olive trees","volume":"36","author":"Bustan","year":"2016","journal-title":"Tree Physiol."},{"key":"10.1016\/j.compag.2026.111773_b0060","doi-asserted-by":"crossref","unstructured":"Canton, H., 2021. Food and agriculture organization of the United Nations\u2014FAO. The Europa Directory of International Organizations 2021. Routledge.","DOI":"10.4324\/9781003179900-41"},{"key":"10.1016\/j.compag.2026.111773_b0065","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"The Journal of Machine Learning Research."},{"key":"10.1016\/j.compag.2026.111773_b0075","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.agwat.2014.01.001","article-title":"Response of relative sap flow to meteorological factors under different soil moisture conditions in rainfed jujube (Ziziphus jujuba Mill.) plantations in semiarid Northwest China","volume":"136","author":"Chen","year":"2014","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0080","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.agwat.2016.10.010","article-title":"Effects of branch removal on water use of rain-fed jujube (Ziziphus jujuba Mill.) plantations in chinese semiarid Loess Plateau region","volume":"178","author":"Chen","year":"2016","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.130397","article-title":"Incorporating dynamic schemes of canopy light extinction coefficient improves transpiration model performance for fruit plantations","volume":"627","author":"Chen","year":"2023","journal-title":"J. Hydrol."},{"key":"10.1016\/j.compag.2026.111773_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2019.105923","article-title":"Partitioning of daily evapotranspiration using a modified shuttleworth-wallace model, random Forest and support vector regression, for a cabbage farmland","volume":"228","author":"Chen","year":"2020","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.129098","article-title":"Species difference of transpiration in three urban coniferous forests in a semiarid region of China","volume":"617","author":"Chen","year":"2023","journal-title":"J. Hydrol."},{"key":"10.1016\/j.compag.2026.111773_b0095","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"},{"key":"10.1016\/j.compag.2026.111773_b0100","doi-asserted-by":"crossref","first-page":"238","DOI":"10.3390\/atmos12020238","article-title":"Influence of random forest hyperparameterization on short-term runoff forecasting in an andean mountain catchment","volume":"12","author":"Contreras","year":"2021","journal-title":"Atmos."},{"key":"10.1016\/j.compag.2026.111773_b0105","article-title":"The boundary definition and study on the Land Use\/Cover Change and Landscape Pattern of the dry-hot valley in Hengduan Mountains","author":"Deng","year":"2022","journal-title":"MS Thesis. China: Yunnan University. Https:\/\/"},{"key":"10.1016\/j.compag.2026.111773_b0110","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.1016\/j.rser.2015.08.035","article-title":"Review and statistical analysis of different global solar radiation sunshine models","volume":"52","author":"Despotovic","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.compag.2026.111773_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109258","article-title":"Improving actual evapotranspiration estimates through an integrated remote sensing and cutting-edge machine learning approach","volume":"225","author":"dos Santos","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111773_b0120","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.agrformet.2005.02.003","article-title":"Transpiration of apple trees in a humid climate using heat pulse sap flow gauges calibrated with whole-canopy gas exchange chambers","volume":"130","author":"Dragoni","year":"2005","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.111773_b0125","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1093\/jxb\/erv034","article-title":"Deficit irrigation and sustainable water-resource strategies in agriculture for China\u2019s food security","volume":"66","author":"Du","year":"2015","journal-title":"J. Exp. Bot."},{"key":"10.1016\/j.compag.2026.111773_b0130","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1016\/j.agrformet.2011.03.007","article-title":"Seasonal variation in canopy reflectance and its application to determine the water status and water use by citrus trees in the Western Cape","volume":"151","author":"Dzikiti","year":"2011","journal-title":"South Africa. Agricultural and Forest Meteorology."},{"key":"10.1016\/j.compag.2026.111773_b0135","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.agrformet.2018.08.019","article-title":"Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China","volume":"263","author":"Fan","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.111773_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2020.106547","article-title":"Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models","volume":"245","author":"Fan","year":"2021","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0145","doi-asserted-by":"crossref","first-page":"e2237","DOI":"10.1002\/eco.2237","article-title":"Comparison of the performance of leaf wetness duration models for rainfed jujube (Ziziphus jujuba Mill.) plantations in the loess hilly region of China using machine learning","volume":"13","author":"Gao","year":"2020","journal-title":"Ecohydrology"},{"key":"10.1016\/j.compag.2026.111773_b0150","doi-asserted-by":"crossref","first-page":"124604","DOI":"10.1016\/j.jhydrol.2020.124604","article-title":"Non-rainfall water contributions to dryland jujube plantation evapotranspiration in the Hilly Loess Region of China","volume":"583","author":"Gao","year":"2020","journal-title":"J. Hydrol."},{"key":"10.1016\/j.compag.2026.111773_b0155","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.3390\/plants11151923","article-title":"Prediction of greenhouse tomato crop evapotranspiration using XGBoost machine learning model","volume":"11","author":"Ge","year":"2022","journal-title":"Plants"},{"key":"10.1016\/j.compag.2026.111773_b0165","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0217634","article-title":"Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction","volume":"14","author":"Ghazvinian","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.compag.2026.111773_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108255","article-title":"Hyperspectral-physiological based predictive model for transpiration in greenhouses under CO2 enrichment","volume":"213","author":"Ghiat","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111773_b0175","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1093\/treephys\/3.4.309","article-title":"Evaluation of transpiration in a Douglas-fir stand by means of sap flow measurements","volume":"3","author":"Granier","year":"1987","journal-title":"Tree Physiol."},{"key":"10.1016\/j.compag.2026.111773_b0180","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1016\/j.rser.2014.07.117","article-title":"A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects","volume":"39","author":"Gueymard","year":"2014","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.compag.2026.111773_b0185","article-title":"Identifying crop phenology using maize height constructed from multi-sources images","volume":"115","author":"Guo","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.compag.2026.111773_b0190","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jhydrol.2009.08.003","article-title":"Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling","volume":"377","author":"Gupta","year":"2009","journal-title":"J. Hydrol."},{"issue":"1","key":"10.1016\/j.compag.2026.111773_b0195","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s00704-019-02966-x","article-title":"Evaluation of artificial neural network and Penman\u2013Monteith equation for the prediction of barley standard evapotranspiration in a semi-arid region","volume":"139","author":"Hashemi","year":"2020","journal-title":"Theor. Appl. Climatol."},{"issue":"1","key":"10.1016\/j.compag.2026.111773_b0200","first-page":"1939","article-title":"Optimization of extreme learning machine model with biological heuristic algorithms to estimate daily reference evapotranspiration in Hetao Irrigation District of China","volume":"16","author":"He","year":"2022","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"10.1016\/j.compag.2026.111773_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2023.108467","article-title":"Transpiration characteristics and environmental controls of orange orchards in the dry-hot valley region of southwest China","volume":"288","author":"Hou","year":"2023","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0210","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.3390\/su16051987","article-title":"Integrating meteorological and remote sensing data to simulate cropland nocturnal evapotranspiration using machine learning","volume":"16","author":"Huang","year":"2024","journal-title":"Sustainability"},{"key":"10.1016\/j.compag.2026.111773_b0215","unstructured":"Ioffe, S., Szegedy, C., 2015, June. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning. (pp. 448-456). pmlr."},{"key":"10.1016\/j.compag.2026.111773_b0220","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1038\/nature11983","article-title":"Terrestrial water fluxes dominated by transpiration","volume":"496","author":"Jasechko","year":"2013","journal-title":"Nature"},{"key":"10.1016\/j.compag.2026.111773_b0225","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"10.1016\/j.compag.2026.111773_b0230","article-title":"Estimating reference crop evapotranspiration using optimized empirical methods with a novel improved Grey Wolf Algorithm in four climatic regions of China","volume":"291","author":"Juan","year":"2024","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2024.110379","article-title":"Reconstruction of the dynamics of sap-flow timeseries of a beech forest using a machine learning approach","volume":"362","author":"Kabala","year":"2025","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.111773_b0240","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.agwat.2016.05.007","article-title":"Improving agricultural water productivity to ensure food security in China under changing environment: from research to practice","volume":"179","author":"Kang","year":"2017","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0245","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.jhydrol.2015.06.052","article-title":"Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree","volume":"528","author":"Kisi","year":"2015","journal-title":"J. Hydrol."},{"key":"10.1016\/j.compag.2026.111773_b0250","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.agrformet.2013.09.003","article-title":"A review of approaches for evapotranspiration partitioning","volume":"184","author":"Kool","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.111773_b0255","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.3390\/agriculture14071141","article-title":"A review of precision irrigation water-saving technology under changing climate for enhancing water use efficiency, crop yield, and environmental footprints","volume":"14","author":"Lakhiar","year":"2024","journal-title":"Agriculture"},{"key":"10.1016\/j.compag.2026.111773_b0260","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1002\/hyp.9768","article-title":"A coupled surface resistance model to estimate crop evapotranspiration in arid region of northwest China","volume":"28","author":"Li","year":"2014","journal-title":"Hydrol. Process."},{"key":"10.1016\/j.compag.2026.111773_b0265","doi-asserted-by":"crossref","DOI":"10.1029\/2023WR034535","article-title":"Fast and accurate estimation of evapotranspiration for smart agriculture","volume":"59","author":"Li","year":"2023","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.compag.2026.111773_b0270","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0168-1923(02)00015-1","article-title":"Determination of daily evaporation and evapotranspiration of winter wheat and maize by large-scale weighing lysimeter and micro-lysimeter","volume":"111","author":"Liu","year":"2002","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.111773_b0275","article-title":"Heterogeneity analysis of main driving factors affecting potential evapotranspiration changes across different climate regions","volume":"912","author":"Liu","year":"2024","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.compag.2026.111773_b0280","article-title":"Hyperparameter optimization of machine learning models for predicting actual evapotranspiration","volume":"100661","author":"Liyew","year":"2025","journal-title":"Machine Learning with Applications."},{"issue":"10","key":"10.1016\/j.compag.2026.111773_b0285","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1007\/s10462-025-11298-4","article-title":"A review of feature selection methods for actual evapotranspiration prediction","volume":"58","author":"Liyew","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.compag.2026.111773_b0290","series-title":"Advances in Neural Information Processing Systems","first-page":"30","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"issue":"9","key":"10.1016\/j.compag.2026.111773_b0295","doi-asserted-by":"crossref","first-page":"4703","DOI":"10.1007\/s00170-023-12264-6","article-title":"Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning","volume":"128","author":"Ma","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"6","key":"10.1016\/j.compag.2026.111773_b0300","doi-asserted-by":"crossref","first-page":"2134","DOI":"10.1111\/j.1365-2486.2010.02375.x","article-title":"Reconciling the optimal and empirical approaches to modelling stomatal conductance","volume":"17","author":"Medlyn","year":"2011","journal-title":"Glob. Chang. Biol."},{"key":"10.1016\/j.compag.2026.111773_b0305","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey wolf optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"10.1016\/j.compag.2026.111773_b0310","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2025.180438","article-title":"A novel hybrid model for actual evapotranspiration estimation in data-scarce arid regions: Integrating modified Budyko and machine learning models using deep learning","volume":"1001","author":"Mohammadnezhad","year":"2025","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.compag.2026.111773_b0315","first-page":"205","volume":"Vol. 19","author":"Monteith","year":"1965","journal-title":"Evaporation and Environment. Symposia of the Society for Experimental Biology"},{"key":"10.1016\/j.compag.2026.111773_b0320","doi-asserted-by":"crossref","first-page":"474","DOI":"10.2166\/hydro.2025.258","article-title":"Advances in machine learning for agricultural water management: a review of techniques and applications","volume":"27","author":"Mortazavizadeh","year":"2025","journal-title":"J. Hydroinf."},{"key":"10.1016\/j.compag.2026.111773_b0325","doi-asserted-by":"crossref","first-page":"5295","DOI":"10.5194\/hess-28-5295-2024","article-title":"Seasonal variation in landcover estimates reveals sensitivities and opportunities for environmental models","volume":"28","author":"Myers","year":"2023","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"10.1016\/j.compag.2026.111773_b0330","doi-asserted-by":"crossref","first-page":"2851","DOI":"10.5194\/essd-14-2851-2022","article-title":"A 30 m annual maize phenology dataset from 1985 to 2020 in China","volume":"14","author":"Niu","year":"2022","journal-title":"Earth Syst. Sci. Data"},{"key":"10.1016\/j.compag.2026.111773_b0335","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2021.108370","article-title":"Evapotranspiration simulation from a sparsely vegetated agricultural field in a semi-arid agro-ecosystem using Penman-Monteith models","volume":"303","author":"Nyolei","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.111773_b0340","article-title":"Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation","volume":"76","author":"Pagano","year":"2023","journal-title":"Eco. Inform."},{"key":"10.1016\/j.compag.2026.111773_b0345","first-page":"9","article-title":"Evaporation: an introductory survey","volume":"4","author":"Penman","year":"1956","journal-title":"Neth. J. Agric. Sci."},{"key":"10.1016\/j.compag.2026.111773_b0350","doi-asserted-by":"crossref","unstructured":"Penman, H.L., 1948. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences. 193, 120-145. https:\/\/doi.org\/10.1098\/rspa.1948.0037.","DOI":"10.1098\/rspa.1948.0037"},{"key":"10.1016\/j.compag.2026.111773_b0355","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1038\/s43016-022-00592-x","article-title":"More accurate specification of water supply shows its importance for global crop production","volume":"3","author":"Proctor","year":"2022","journal-title":"Nat. Food"},{"key":"10.1016\/j.compag.2026.111773_b0360","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110599","article-title":"Meticulous estimation of maize actual evapotranspiration: a comprehensive explainable CatBoost algorithm reinforced with Jackknife uncertainty paradigm","volume":"237","author":"Rahimi","year":"2025","journal-title":"Comput. Electron. Agric."},{"issue":"1","key":"10.1016\/j.compag.2026.111773_b0365","doi-asserted-by":"crossref","first-page":"6736","DOI":"10.1038\/s41598-024-56770-3","article-title":"An evolutionary parsimonious approach to estimate daily reference evapotranspiration","volume":"14","author":"Ruiz-Ortega","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compag.2026.111773_b0370","doi-asserted-by":"crossref","first-page":"5419","DOI":"10.3390\/rs15225419","article-title":"Utility of Leaf Area Index for monitoring Phenology of Russian Forests","volume":"15","author":"Shabanov","year":"2023","journal-title":"Remote Sens. (Basel)"},{"key":"10.1016\/j.compag.2026.111773_b0375","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2020.106019","article-title":"Deficit irrigation effect on fruit yield, quality and water use efficiency: a long-term study on P\u00eara-IAC sweet orange","volume":"231","author":"Silveira","year":"2020","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0380","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"10.1016\/j.compag.2026.111773_b0385","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/2.294849","article-title":"Genetic algorithms: a survey","volume":"27","author":"Srinivas","year":"1994","journal-title":"Computer"},{"key":"10.1016\/j.compag.2026.111773_b0390","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/0038-092X(93)90124-7","article-title":"Improved statistical procedure for the evaluation of solar radiation estimation models","volume":"51","author":"Stone","year":"1993","journal-title":"Sol. Energy"},{"key":"10.1016\/j.compag.2026.111773_b0395","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2023.109344","article-title":"Revisiting the role of transpiration in the variation of ecosystem water use efficiency in China","volume":"332","author":"Sun","year":"2023","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.111773_b0400","doi-asserted-by":"crossref","first-page":"7183","DOI":"10.1029\/2000JD900719","article-title":"Summarizing multiple aspects of model performance in a single diagram","volume":"106","author":"Taylor","year":"2001","journal-title":"J. Geophys. Res. Atmos."},{"key":"10.1016\/j.compag.2026.111773_b0405","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.agrformet.2017.03.018","article-title":"Environmental and physiological controls on sap flow in a subhumid mountainous catchment in North China","volume":"240","author":"Tie","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.111773_b0410","doi-asserted-by":"crossref","first-page":"910","DOI":"10.3390\/w11050910","article-title":"A brief review of random forests for water scientists and practitioners and their recent history in water resources","volume":"11","author":"Tyralis","year":"2019","journal-title":"Water"},{"key":"10.1016\/j.compag.2026.111773_b0415","series-title":"The nature of statistical learning theory","author":"Vapnik","year":"2013"},{"key":"10.1016\/j.compag.2026.111773_b0420","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2023.109414","article-title":"What determines the time lags of sap flux with solar radiation and vapor pressure deficit?","volume":"333","author":"Wan","year":"2023","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.111773_b0425","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.131732","article-title":"Soil water content and vapor pressure deficit affect ecosystem water use efficiency through different pathways","volume":"640","author":"Wang","year":"2024","journal-title":"J. Hydrol."},{"key":"10.1016\/j.compag.2026.111773_b0430","doi-asserted-by":"crossref","DOI":"10.1029\/2020WR027367","article-title":"Improving evapotranspiration model performance by treating energy imbalance and interaction","volume":"56","author":"Wei","year":"2020","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.compag.2026.111773_b0435","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.131856","article-title":"Daily trunk radial growth patterns in relation to precipitation in orange trees in the dry-hot valley region of Southwest China","volume":"641","author":"Wei","year":"2024","journal-title":"J. Hydrol."},{"key":"10.1016\/j.compag.2026.111773_b0440","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.agrformet.2015.12.003","article-title":"Evapotranspiration partitioning through in-situ oxygen isotope measurements in an oasis cropland","volume":"230","author":"Wen","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.111773_b0445","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2022.128947","article-title":"Simulation of daily maize evapotranspiration at different growth stages using four machine learning models in semi-humid regions of northwest China","volume":"617","author":"Wu","year":"2023","journal-title":"J. Hydrol."},{"key":"10.1016\/j.compag.2026.111773_b0450","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2022.107889","article-title":"Estimation of daily apple tree transpiration in the Loess Plateau region of China using deep learning models","volume":"273","author":"Xing","year":"2022","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0460","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2025.109321","article-title":"Improved estimation of stomatal conductance by combining high-throughput plant phenotyping data and weather variables through machine learning","volume":"309","author":"Zhang","year":"2025","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0470","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.agwat.2018.06.007","article-title":"Comparing ET-VPD hysteresis in three agroforestry ecosystems in a subtropical humid karst area","volume":"208","author":"Zhang","year":"2018","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0455","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2024.109238","article-title":"Application of various canopy resistance calculation methods in vineyard evapotranspiration simulation at daily scale in Northwest China","volume":"307","author":"Zhang","year":"2025","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0475","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1016\/j.agwat.2011.02.003","article-title":"Changes in evapotranspiration over irrigated winter wheat and maize in North China Plain over three decades","volume":"98","author":"Zhang","year":"2011","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0465","doi-asserted-by":"crossref","first-page":"512","DOI":"10.3390\/agronomy15030512","article-title":"Evapotranspiration partitioning for croplands based on eddy covariance measurements and machine learning models","volume":"15","author":"Zhang","year":"2025","journal-title":"Agronomy"},{"key":"10.1016\/j.compag.2026.111773_b0480","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1016\/j.scitotenv.2019.01.104","article-title":"Throughfall and stemflow heterogeneity under the maize canopy and its effect on soil water distribution at the row scale","volume":"660","author":"Zheng","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.compag.2026.111773_b0485","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2024.108807","article-title":"A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain","volume":"296","author":"Zhou","year":"2024","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111773_b0490","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109862","article-title":"Estimating reference evapotranspiration using hybrid models optimized by bio-inspired algorithms combined with key meteorological factors","volume":"230","author":"Zhou","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111773_b0495","doi-asserted-by":"crossref","first-page":"5905","DOI":"10.1038\/s41598-024-55243-x","article-title":"A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction","volume":"14","author":"Zhou","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compag.2026.111773_b0500","doi-asserted-by":"crossref","first-page":"10456","DOI":"10.3390\/app122010456","article-title":"Optimization of the random forest hyperparameters for power industrial control systems intrusion detection using an improved grid search algorithm","volume":"12","author":"Zhu","year":"2022","journal-title":"Appl. Sci."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926003686?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926003686?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T16:47:25Z","timestamp":1779727645000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926003686"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":99,"alternative-id":["S0168169926003686"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.111773","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Comparative evaluation of machine learning models integrated with optimization algorithms for daily transpiration estimation in citrus orchards of dry-hot valley region in China","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.111773","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111773"}}