{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T12:24:34Z","timestamp":1781094274320,"version":"3.54.1"},"reference-count":65,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T00:00:00Z","timestamp":1674518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001807","name":"FAPESP","doi-asserted-by":"publisher","award":["19\/06078-8"],"award-info":[{"award-number":["19\/06078-8"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"FAPESP","doi-asserted-by":"publisher","award":["27192.03.01\/2020.13-00"],"award-info":[{"award-number":["27192.03.01\/2020.13-00"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"name":"FUNDEP-Rota 2030","award":["19\/06078-8"],"award-info":[{"award-number":["19\/06078-8"]}]},{"name":"FUNDEP-Rota 2030","award":["27192.03.01\/2020.13-00"],"award-info":[{"award-number":["27192.03.01\/2020.13-00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Precision Irrigation (PI) is a promising technique for monitoring and controlling water use that allows for meeting crop water requirements based on site-specific data. However, implementing the PI needs precise data on water evapotranspiration. The detection and monitoring of crop water stress can be achieved by several methods, one of the most interesting being the use of infra-red (IR) thermometry combined with the estimate of the Crop Water Stress Index (CWSI). However, conventional IR equipment is expensive, so the objective of this paper is to present the development of a new low-cost water stress detection system using TL indices obtained by crossing the responses of infrared sensors with image processing. The results demonstrated that it is possible to use low-cost IR sensors with a directional Field of Vision (FoV) to measure plant temperature, generate thermal maps, and identify water stress conditions. The Leaf Temperature Maps, generated by the IR sensor readings of the plant segmentation in the RGB image, were validated by thermal images. Furthermore, the estimated CWSI is consistent with the literature results.<\/jats:p>","DOI":"10.3390\/s23031318","type":"journal-article","created":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T03:23:49Z","timestamp":1674617029000},"page":"1318","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Water Stress Index Detection Using a Low-Cost Infrared Sensor and Excess Green Image Processing"],"prefix":"10.3390","volume":"23","author":[{"given":"Rodrigo Leme de","family":"Paulo","sequence":"first","affiliation":[{"name":"School of Agricultural Engineering, University of Campinas, Campinas 13083-875, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8163-6638","authenticated-orcid":false,"given":"Angel Pontin","family":"Garcia","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, University of Campinas, Campinas 13083-875, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudio Kiyoshi","family":"Umezu","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, University of Campinas, Campinas 13083-875, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5164-2634","authenticated-orcid":false,"given":"Antonio Pires de","family":"Camargo","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, University of Campinas, Campinas 13083-875, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabr\u00edcio Theodoro","family":"Soares","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, University of Campinas, Campinas 13083-875, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6877-8618","authenticated-orcid":false,"given":"Daniel","family":"Albiero","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, University of Campinas, Campinas 13083-875, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,24]]},"reference":[{"key":"ref_1","unstructured":"Rassini, J.B. (2011). Irriga\u00e7\u00e3o e Fertiliza\u00e7\u00e3o em Fruteiras e Hortali\u00e7as, Embrapa Informa\u00e7\u00e3o Tecnol\u00f3gica."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.12944\/CARJ.7.1.01","article-title":"Agricultural Robotics: A Promising Challenge","volume":"7","author":"Albiero","year":"2019","journal-title":"Curr. Agric. Res. J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Albiero, D. (2022). Robots and AI: Illusions and Social Dilemmas, Springer International Publishing.","DOI":"10.1007\/978-3-030-95790-2"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105441","DOI":"10.1016\/j.compag.2020.105441","article-title":"A Review on Monitoring and Advanced Control Strategies for Precision Irrigation","volume":"173","author":"Abioye","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1590\/1678-992x-2020-0249","article-title":"Mechanical Properties of Lettuce (Lactuca Sativa L.) for Horticultural Machinery Design","volume":"79","author":"Xavier","year":"2022","journal-title":"Sci. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"775","DOI":"10.13031\/2013.8844","article-title":"Establishing crop water stress index (cwsi) threshold values for early, non\u2013contact detection of plant water stress","volume":"45","author":"Kacira","year":"2002","journal-title":"Trans. ASAE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/0002-1571(82)90020-6","article-title":"Non-Water-Stressed Baselines: A Key to Measuring and Interpreting Plant Water Stress","volume":"27","author":"Idso","year":"1982","journal-title":"Agric. Meteorol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Waller, P., and Yitayew, M. (2016). Irrigation and Drainage Engineering, Springer.","DOI":"10.1007\/978-3-319-05699-9"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.agwat.2018.06.002","article-title":"Thermal Imaging at Plant Level to Assess the Crop-Water Status in Almond Trees (Cv. Guara) under Deficit Irrigation Strategies","volume":"208","author":"Rubio","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Alsalam, B.H.Y., Morton, K., Campbell, D., and Gonzalez, F. (2017, January 4\u201311). Autonomous UAV with Vision Based On-Board Decision Making for Remote Sensing and Precision Agriculture. Proceedings of the 2017 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2017.7943593"},{"key":"ref_11","first-page":"372","article-title":"Evaluation of Crop Water Stress Index (CWSI) for Eggplant under Varying Irrigation Regimes Using Surface and Subsurface Drip Systems","volume":"4","author":"Yazar","year":"2015","journal-title":"Agric. Agric. Sci. Procedia"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1080\/17686733.2017.1331008","article-title":"Use of Infrared Thermography and Hyperspectral Data to Detect Effects of Water Stress on Pepper","volume":"15","author":"Camoglu","year":"2018","journal-title":"Quant. Infrared Thermogr. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s00271-009-0150-7","article-title":"Evaluating Water Stress in Irrigated Olives: Correlation of Soil Water Status, Tree Water Status, and Thermal Imagery","volume":"27","author":"Agam","year":"2009","journal-title":"Irrig. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1590\/1807-1929\/agriambi.v22n2p95-100","article-title":"Tomato Water Stress Index as a Function of Irrigation Depths","volume":"22","author":"Golynski","year":"2018","journal-title":"Rev. Bras. Eng. Agric. Ambient."},{"key":"ref_15","first-page":"449","article-title":"Irrigation Scheduling for Watermelon with Crop Water Stress Index (Cwsi)","volume":"6","author":"Erdem","year":"2006","journal-title":"J. Cent. Eur. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7535","DOI":"10.15666\/aeer\/1606_75357549","article-title":"Scheduling Maize Irrigation Based on Crop Water Stress Index (CWSI)","volume":"16","author":"Fattahi","year":"2018","journal-title":"Appl. Ecol. Environ. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.agrformet.2012.08.005","article-title":"Usefulness of Thermography for Plant Water Stress Detection in Citrus and Persimmon Trees","volume":"168","author":"Ballester","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_18","unstructured":"Pantano, A.P., Camparotto, L.B., and Meireles, E.J.L. (2021). Monitoramento Agrometeorol\u00f3gico para Regi\u00f5es Cafeeiras do Estado de S\u00e3o Paulo: Janeiro\/2010\u2014Dezembro\/2019 (Boletim t\u00e9cnico 224\u2014IAC), IAC."},{"key":"ref_19","first-page":"449","article-title":"Desempenho Agron\u00f4mico de R\u00facula sob Diferentes Espa\u00e7amentos","volume":"40","author":"Kalliany","year":"2009","journal-title":"Rev. Ci\u00eancia Agron\u00f4mica"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.compag.2018.08.009","article-title":"Dynamic Modelling of the Baseline Temperatures for Computation of the Crop Water Stress Index (CWSI) of a Greenhouse Cultivated Lettuce Crop","volume":"153","author":"Adeyemi","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","first-page":"23","article-title":"Use of Crop Water Stress Index (CWSI) for Evaluation of Water Status and Irrigation Scheduling of Saffron","volume":"7","author":"Shirmohammadi","year":"2006","journal-title":"Iran. J. Hortic. Sci. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Monteith, J.L., and Unsworth, M.H. (2013). Principles of Environmental Physics, Academic Press. [4th ed.].","DOI":"10.1016\/B978-0-12-386910-4.00001-9"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.biosystemseng.2017.09.012","article-title":"A Practical Method Using a Network of Fixed Infrared Sensors for Estimating Crop Canopy Conductance and Evaporation Rate","volume":"165","author":"Jones","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s12355-022-01115-5","article-title":"Sensor-Based Technologies in Sugarcane Agriculture","volume":"24","author":"Garcia","year":"2022","journal-title":"Sugar Tech."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.compag.2018.12.011","article-title":"IoT and Agriculture Data Analysis for Smart Farm","volume":"156","author":"Muangprathub","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.biosystemseng.2015.07.005","article-title":"Open Source Hardware to Monitor Environmental Parameters in Precision Agriculture","volume":"137","year":"2015","journal-title":"Biosyst. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Flores, K.O., Butaslac, I.M., Gonzales, J.E.M., Dumlao, S.M.G., and Reyes, R.S.J. (2016, January 22\u201325). Precision Agriculture Monitoring System Using Wireless Sensor Network and Raspberry Pi Local Server. Proceedings of the IEEE Region 10 Annual International Conference, Proceedings\/TENCON, Singapore.","DOI":"10.1109\/TENCON.2016.7848600"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s11119-013-9334-5","article-title":"Mapping Crop Water Stress Index in a \u2018Pinot-Noir\u2019 Vineyard: Comparing Ground Measurements with Thermal Remote Sensing Imagery from an Unmanned Aerial Vehicle","volume":"15","author":"Bellvert","year":"2013","journal-title":"Precis. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"259","DOI":"10.13031\/2013.27838","article-title":"Color Indices for Weed Identification under Various Soil, Residue, and Lighting Conditions","volume":"38","author":"Woebbecke","year":"1995","journal-title":"Trans. Am. Soc. Agric. Eng."},{"key":"ref_30","unstructured":"Perissini, I.C. (2018). An\u00e1lise Experimental de Algoritmos de Const\u00e2ncia de Cor e Segmenta\u00e7\u00e3o Para Detec\u00e7\u00e3o de Mudas de Plantas. [Master\u2019s Thesis, S\u00e3o Paulo University]."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bailly, S., Giordano, S., Landrieu, L., and Chehata, N. (2018, January 22\u201327). Crop-Rotation Structured Classification Using Multi-Source Sentinel Images and LPIS for Crop Type Mapping. Proceedings of the 2018 International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518427"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.compag.2010.04.007","article-title":"Automated Canopy Temperature Estimation via Infrared Thermography: A First Step towards Automated Plant Water Stress Monitoring","volume":"73","author":"Wang","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s00271-008-0104-5","article-title":"Crop Water Stress Index Is a Sensitive Water Stress Indicator in Pistachio Trees","volume":"26","author":"Testi","year":"2008","journal-title":"Irrig. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/0002-1571(81)90032-7","article-title":"Normalizing the Stress-Degree-Day Parameter for Environmental Variability","volume":"24","author":"Idso","year":"1981","journal-title":"Agric. Meteorol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1590\/S1806-66902012000100011","article-title":"Avalia\u00e7\u00e3o Da Distribui\u00e7\u00e3o de Sementes Por Uma Semeadora de Anel Interno Rotativo Utilizando M\u00e9dia M\u00f3vel Exponencial","volume":"43","author":"Albiero","year":"2012","journal-title":"Rev. Ci\u00eancia Agron\u00f4mica"},{"key":"ref_36","unstructured":"Montgomery, D.C. (2008). Design and Analysis of Experiments, John Wiley & Sons. [8th ed.]."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"102744","DOI":"10.1016\/j.est.2021.102744","article-title":"Electric Tractor System for Family Farming: Increased Autonomy and Economic Feasibility for an Energy Transition","volume":"40","author":"Vogt","year":"2021","journal-title":"J. Energy Storage"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1111\/jac.12371","article-title":"Crop Water Stress Index for Scheduling Irrigation of Indian Mustard (Brassica Juncea) Based on Water Use Efficiency Considerations","volume":"206","author":"Kumar","year":"2020","journal-title":"J. Agron. Crop. Sci"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2380","DOI":"10.1016\/j.rse.2009.06.018","article-title":"Mapping Canopy Conductance and CWSI in Olive Orchards Using High Resolution Thermal Remote Sensing Imagery","volume":"113","author":"Berni","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.agwat.2012.12.004","article-title":"An Insight to the Performance of Crop Water Stress Index for Olive Trees","volume":"118","author":"Agam","year":"2013","journal-title":"Agric. Water Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/BF00296705","article-title":"A Reexamination of the Crop Water Stress Index","volume":"9","author":"Jackson","year":"1988","journal-title":"Irrig. Sci."},{"key":"ref_42","unstructured":"Gilman, K.L. (2021). Pistachio Yields and Nut Quality Determination and the Relationship Between Soil Characteristics. [Master\u2019s Thesis, California State University]."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"10","DOI":"10.37934\/aram.72.1.1024","article-title":"Smart Temperature Measurement System for Milling Process Application Based on MLX90614 Infrared Thermometer Sensor with Arduino","volume":"72","author":"Sudianto","year":"2020","journal-title":"J. Adv. Res. Appl. Mech."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s11119-016-9470-9","article-title":"A Cost-Effective Canopy Temperature Measurement System for Precision Agriculture: A Case Study on Sugar Beet","volume":"18","author":"Egea","year":"2017","journal-title":"Precis. Agric."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.compag.2010.07.006","article-title":"A Low-Cost Microcontroller-Based System to Monitor Crop Temperature and Water Status","volume":"74","author":"Fisher","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_46","first-page":"574","article-title":"Diurnal Variations in Leaf\u2014Air Temperature and Vapor Pressure Deficit of Sunlit and Shaded Kenaf Leaves","volume":"2761","author":"Gintsioudis","year":"2020","journal-title":"CEUR Workshop Proc."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.compag.2018.02.018","article-title":"Econimical thermal-RGB imaging system for monitoring agricultural crops","volume":"147","author":"Osroosh","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_48","first-page":"106319","article-title":"Intelligent thermal image-based sensor for affordable measurement of crop canopy temperature","volume":"188","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"106019","DOI":"10.1016\/j.compag.2021.106019","article-title":"Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications","volume":"182","author":"Zhou","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"100021","DOI":"10.1016\/j.atech.2021.100021","article-title":"Application of infrared thermography for irrigation scheduling of horticulture plants","volume":"1","author":"Parihar","year":"2021","journal-title":"Smart Agric. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"106699","DOI":"10.1016\/j.agwat.2020.106699","article-title":"Improving the performance in crop water deficit diagnosis with canopy temperature spatial distribution information measured by thermal imaging","volume":"246","author":"Luan","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"107575","DOI":"10.1016\/j.agwat.2022.107575","article-title":"Crop water stress index computation approaches and their sensitivity to soil water dynamics","volume":"266","author":"Katimbo","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.jaridenv.2005.01.017","article-title":"The CWSI Variations of a Cotton Crop in a Semi-Arid Region of Northeast Brazil","volume":"62","year":"2005","journal-title":"J. Arid Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.agwat.2015.03.023","article-title":"Comparison of canopy temperature-based water stress indices for maize","volume":"156","author":"DeJonge","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s40725-019-00096-1","article-title":"Early Diagnosis of Vegetation Health from High-Resolution Hyperspectral and Thermal Imagery: Lessons Learned from Empirical Relationships and Radiative Transfer Modelling","volume":"5","author":"Hornero","year":"2019","journal-title":"Curr. For. Rep."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.biosystemseng.2017.08.013","article-title":"Linking Thermal Imaging and Soil Remote Sensing to Enhance Irrigation Management of Sugar Beet","volume":"165","author":"Quebrajo","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s00271-020-00681-4","article-title":"Evaluation of crop water stress index and leaf water potential for differentially irrigated quinoa with surface and subsurface drip systems","volume":"39","author":"Yazar","year":"2021","journal-title":"Irrig. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1093\/jxb\/eri174","article-title":"Estimation of leaf water potential by thermal imagery and spatial analysis","volume":"56","author":"Cohen","year":"2005","journal-title":"J. Exp. Bot."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"359","DOI":"10.17660\/ActaHortic.1993.335.43","article-title":"Comparison of water stress indicators for soybean","volume":"335","author":"Mastrorilli","year":"1993","journal-title":"Acta Hortic."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"e09010","DOI":"10.1016\/j.heliyon.2022.e09010","article-title":"Quantifying water stress of safflower (Carthamus tinctorius L.) cultivars by crop water stress index under different irrigation regimes","volume":"8","author":"Bijanzadeh","year":"2022","journal-title":"Heliyon"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1029\/WR017i004p01133","article-title":"Canopy Temperature as a Crop Water Stress Indicator","volume":"17","author":"Jackson","year":"1981","journal-title":"Water Resour. Res."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ciezkowski, W., Szporak-Wasilewska, S., Kleniewska, M.L., J\u00f3\u017awiak, J., Gnatowski, T., Dabrowski, P., G\u00f3raj, M., SzatyLowicz, J., Ignar, S., and Chorma\u0144ski, J.L. (2020). Remotely Sensed Land Surface Temperature-Based Water Stress Index for Wetland Habitats. Remote Sens., 12.","DOI":"10.3390\/rs12040631"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1016\/j.agwat.2008.04.017","article-title":"Development of Crop Water Stress Index of Wheat Crop for Scheduling Irrigation Using Infrared Thermometry","volume":"95","author":"Gontia","year":"2008","journal-title":"Agric. Water Manag."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s11119-009-9111-7","article-title":"Evaluation of Different Approaches for Estimating and Mapping Crop Water Status in Cotton with Thermal Imaging","volume":"11","author":"Alchanatis","year":"2010","journal-title":"Precis. Agric."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.ijforecast.2003.09.015","article-title":"Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages","volume":"20","author":"Holt","year":"2004","journal-title":"Int. J. Forecast."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1318\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:14:38Z","timestamp":1760120078000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,24]]},"references-count":65,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031318"],"URL":"https:\/\/doi.org\/10.3390\/s23031318","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202209.0368.v1","asserted-by":"object"}]},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,24]]}}}