[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126813-en":3,"doc-seo-126813-105":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},126813,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Prediction of Stem Water Potential in Olive Orchards Using High-Resolution Planet Satellite Images and Machine Learning Techniques","Accurate assessment of plant water status across time and space underpins stable crop yield and quality, especially under climate change pressures. This study measures stem water potential (Ψstem) in an irrigated olive orchard in southern Italy during 2021 and 2022. Midday measurements from 24 trees (June–October) are matched with reflectance data collected at sampling times to compute vegetation indices. Machine learning models use vegetation indices and spectral bands to predict Ψstem, with random forest delivering the strongest performance (R2=0.78) and supporting irrigation management without extensive fieldwork.","agronomy  \nArticle  \nPrediction of Stem Water Potential in Olive Orchards Using High-Resolution Planet Satellite Images and Machine Learning Techniques  \nSimone Pietro Garofalo 1, *, Vincenzo Giannico 1, Leonardo Costanza 1, Salem Alhajj Ali 1,  \nSalvatore Camposeo 1, Giuseppe Lopriore 1, Francisco Pedrero Salcedo 2 and Gaetano Alessandro Vivaldi 1  \nCitation: Garofalo, S.P.; Giannico, V.; Costanza, L.; Alhajj Ali, S.; Camposeo, S.; Lopriore, G.; Pedrero Salcedo, F.; Vivaldi, G.A. Prediction of Stem Water Potential in Olive Orchards Using High-Resolution Planet Satellite Images and Machine Learning Techniques. Agronomy 2024, 14, 1 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)agronomy14010001  \nAcademic Editor: Valerio Cristofori  \nReceived: 23 November 2023  \nRevised: 15 December 2023  \nAccepted: 17 December 2023  \nPublished: 19 December 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Soil, Plant and Food Science, University of Bari Aldo Moro, Via Amendola 165/A,  \n70126 Bari, Italy; [vincenzo.giannico@uniba.it](vincenzo.giannico@uniba.it) (V.G.); [leonardo.costanza@uniba.it](leonardo.costanza@uniba.it) (L.C.);  \n[salem.alhajj@uniba.it](salem.alhajj@uniba.it) (S.A.A.); [salvatore.camposeo@uniba.it](salvatore.camposeo@uniba.it) (S.C.); [giuseppe.lopriore@uniba.it](giuseppe.lopriore@uniba.it) (G.L.);  \n[gaetano.vivaldi@uniba.it](gaetano.vivaldi@uniba.it) (G.A.V.)  \n2 Department of Irrigation, CEBAS-CSIC, Campus Universitario de Espinardo, 30100 Murcia, Spain; [fpedrero@cebas.csic.es](fpedrero@cebas.csic.es)  \n* Correspondence: [simone.garofalo@uniba.it](simone.garofalo@uniba.it)  \nAbstract: Assessing plant water status accurately in both time and space is crucial for maintaining satisfactory crop yield and quality standards, especially in the face of a changing climate. Remote sensing technology offers a promising alternative to traditional in situ measurements for estimating stem water potential (Ψstem) . In this study, we carried out field measurements of Ψstem in an irrigated olive orchard in southern Italy during the 2021 and 2022 seasons. Water status data were acquired at midday from 24 olive trees between June and October in both years. Reflectance data collected at the time of Ψstem measurements were utilized to calculate vegetation indices (VIs) . Employing machine learning techniques, various prediction models were developed by considering VIs and spectral bands as predictors. Before the analyses, both datasets were randomly split into training and testing datasets. Our findings reveal that the random forest model outperformed other models, providing a more accurate prediction of olive water status (R2 = 0 .78) . This is the first study in the literature integrating remote sensing and machine learning techniques for the prediction of olive water status in order to improve olive orchard irrigation management, offering a practical solution for estimating Ψstem avoiding time-consuming and resource-intensive fieldwork.  \nKeywords: vegetation indices; spectral bands; satellite; irrigation management; olive; modeling  \n1. Introduction  \nThe Mediterranean basin is famous for its rich history of olive tree cultivation since the Roman and Greek civilizations [1], both for table olives and olive oil production, which represent essential components of the Mediterranean diet [2] . The climate condition in these areas is generally characterized by hot summers and mild winters, which are wellsuited for olive tree growth [3] . According to the International Olive Oil Council [4], Mediterranean countries accounted for approximately 97% of the world’s olive cultivation,","cbCailaoc5de1dlr","https://ap.wps.com/l/cbCailaoc5de1dlr","pdf",6011323,1,18,"English","en",105,"# Abstract\n# Introduction\n## Study context and motivation\n# Materials and Methods\n## Field measurements\n## Remote sensing features and indices\n## Machine learning modeling\n# Results\n## Model performance and comparison\n# Discussion\n## Implications for irrigation management\n# Conclusions","[{\"question\":\"What plant variable does the study aim to predict in olive orchards?\",\"answer\":\"The study predicts stem water potential (Ψstem) to characterize olive water status for irrigation decision-making.\"},{\"question\":\"How are vegetation indices and spectral bands used in the prediction models?\",\"answer\":\"Reflectance measured during Ψstem sampling is converted into vegetation indices (VIs), and both VIs and spectral bands are used as predictors for machine learning models.\"},{\"question\":\"Which machine learning method performed best and what was its accuracy?\",\"answer\":\"The random forest model outperformed the others, achieving an R2 value of 0.78 for predicting olive water status.\"}]","Prediction of Stem Water Potential in Olive Orchards Using High-Resolution Planet Satellite Images and Machine Learning Techniques | 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plant variable does the study aim to predict in olive orchards?","Question",{"text":75,"@type":76},"The study predicts stem water potential (Ψstem) to characterize olive water status for irrigation decision-making.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are vegetation indices and spectral bands used in the prediction models?",{"text":80,"@type":76},"Reflectance measured during Ψstem sampling is converted into vegetation indices (VIs), and both VIs and spectral bands are used as predictors for machine learning models.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning method performed best and what was its accuracy?",{"text":84,"@type":76},"The random forest model outperformed the others, achieving an R2 value of 0.78 for predicting olive water 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