[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124703-en":3,"doc-seo-124703-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124703,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation","Accurate estimation of daily actual evapotranspiration (ETa) is critical for agricultural sustainability and effective water management, especially under intensifying drought conditions. The study evaluates artificial-intelligence approaches as alternatives to costly Eddy Covariance measurements by comparing Multi-Layer Perceptron (MLP) and Random Forest (RF) models for a Mediterranean citrus orchard. Feature selection uses sensor-based inputs with importance analysis and correlation screening, yielding 12 candidate combinations. Models are calibrated under regulated deficit irrigation (RDI) to estimate ETa and reduce water use, achieving up to 38.5% savings on average versus full irrigation. Soil water content (SWC) is highlighted as a key predictor, and RF attains the best results with seven features (RMSE 0.39 mm/day, R2 0.84), while joint SWC, weather, and satellite data improves forecast performance over meteorology-only inputs.","Ecological Informatics 76 (2023) 102133  \nContents lists available at ScienceDirect  \nEcological Informatics  \njournal [homepage:](homepage: www.elsevier.com/locate/ecolinf)[ www.elsevier.com/locate/ecolinf](homepage: www.elsevier.com/locate/ecolinf)  \n| Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation |  |  |  |\n| --- | --- | --- | --- |\n| Antonino Pagano a, c, *, Federico Amato a, Matteo Ippolito b, Dario De Carob, Daniele Croce a, c, Antonio Motisib, Giuseppe Provenzanob, Ilenia Tinnirello a, c\u003Cbr>a Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy\u003Cbr>b Department Agriculture, Food and Forest Sciences, University of Palermo, Viale delle Scienze, Building 4, 90128 Palermo, Italy c CNIT-Consorzio Nazionale Interuniversitario per le Telecomunicazioni, Parma, Italy |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Actual evapotranspiration Machine learning Artificial Neural Network Multi-Layer Perceptron Random Forest\u003Cbr>Citrus orchard\u003Cbr>Regulated deficit irrigation |  | Precise estimations of actual evapotranspiration (ETa) are essential for various environmental issues, including those related to agricultural ecosystem sustainability and water management. Indeed, the increasing demands of agricultural production, coupled with increasingly frequent drought events in many parts of the world, necessitate a more careful evaluation of crop water requirements.\u003Cbr>Artificial Intelligence-based models represent a promising alternative to the most common measurement techniques, e.g. using expensive Eddy Covariance (EC) towers. In this context, the main challenges are choosing the best possible model and selecting the most representative features. The objective of this research is to evaluate two different machine learning algorithms, namely Multi-Layer Perceptron (MLP) and Random Forest (RF), to predict daily actual evapotranspiration (ETa) in a citrus orchard typical of the Mediterranean ecosystem using different feature combinations. With many features available coming from various infield sensors, a thorough analysis was performed to measure feature importance, scatter matrix observations, and Pearson’s correlation coefficient calculation, which resulted in the selection of 12 promising feature combinations. The models were calibrated under regulated deficit irrigation (RDI) conditions to estimate ETa and save irrigation water. On average up to 38.5% water savings were obtained, compared to full irrigation. Moreover, among the different input variables adopted, the soil water content (SWC) feature appears to have a prominent role in the prediction of ETa . Indeed, the presented results show that by choosing the appropriate input features, the accuracy of the proposed machine learning models remains acceptable even when the number of features is reduced to only 4. The best performance was achieved by the Random Forest method, with seven input features, obtaining a root mean square error (RMSE) and a coefficient of determination (R2 ) of 0.39 mm/day and 0.84, respectively. Finally, the results show that the joint use of SWC, weather and satellite data significantly improves the performance of evapotranspiration forecasts compared to models using only meteorological variables. |  |\n\n1. Introduction  \nAccording to the recent global report on water use published by UNESCO, irrigation represents about 70% of the global consumption of available freshwater (WWDR, 2021). Therefore, adopting sustainable agriculture is of paramount importance to minimize water consumption. In this context, pushing the agricultural system as a whole toward ecologically sustainable solutions is a major challenge given the increasing insufficiency of water availability (Gangopadhyay et al., 2023). Recent research suggests that farmers should be encouraged to  \nadopt new solutions, particularly in drought-prone r","cbCaijKdvmo87gT1","https://ap.wps.com/l/cbCaijKdvmo87gT1","pdf",1716451,1,17,"English","en",105,"# Introduction\n## Water use context and need for sustainable irrigation\n## Regulated deficit irrigation (RDI) and monitoring requirements","[{\"question\":\"Why is predicting daily actual evapotranspiration (ETa) important for citrus orchards?\",\"answer\":\"ETa estimation supports agricultural ecosystem sustainability and water management. It helps meet crop water requirements more carefully, particularly during increasing drought events.\"},{\"question\":\"Which machine learning models are compared in the study, and what is the main goal?\",\"answer\":\"The research compares Multi-Layer Perceptron (MLP) and Random Forest (RF). The goal is to predict daily ETa using different feature combinations and to identify the most representative inputs.\"},{\"question\":\"How do the results evaluate irrigation water savings under regulated deficit irrigation (RDI)?\",\"answer\":\"The models are calibrated under RDI to estimate ETa and reduce irrigation water. On average, the study reports up to 38.5% water savings compared with full irrigation.\"}]","Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation | PDF",1785894004,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-models-to-predict-daily-actual-evapotranspiration-of-citrus-orchards-under-regulated-deficit-irrigation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-models-to-predict-daily-actual-evapotranspiration-of-citrus-orchards-under-regulated-deficit-irrigation/124703/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting daily actual evapotranspiration (ETa) important for citrus orchards?","Question",{"text":75,"@type":76},"ETa estimation supports agricultural ecosystem sustainability and water management. It helps meet crop water requirements more carefully, particularly during increasing drought events.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the study, and what is the main goal?",{"text":80,"@type":76},"The research compares Multi-Layer Perceptron (MLP) and Random Forest (RF). The goal is to predict daily ETa using different feature combinations and to identify the most representative inputs.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the results evaluate irrigation water savings under regulated deficit irrigation (RDI)?",{"text":84,"@type":76},"The models are calibrated under RDI to estimate ETa and reduce irrigation water. 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