{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T11:42:39Z","timestamp":1736941359949,"version":"3.33.0"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cEugene Kendel\u201d Project for the Development of Precision Drip Irrigation funded via the Ministry of Agriculture and Rural Development in Israel","award":["20-12-0030"]},{"name":"European Union\u2019s Horizon 2020 research and innovation program under Project SHui","award":["773903"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Accurate canopy extraction and temperature calculations are crucial to minimizing inaccuracies in thermal image-based estimation of orchard water status. Currently, no quantitative comparison of canopy extraction methods exists in the context of precision irrigation. The accuracies of four canopy extraction methods were compared, and the effect on water status estimation was explored for these methods: 2-pixel erosion (2PE) where non-canopy pixels were removed by thresholding and morphological erosion; edge detection (ED) where edges were identified and morphologically dilated; vegetation segmentation (VS) using temperature histogram analysis and spatial watershed segmentation; and RGB binary masking (RGB-BM) where a binary canopy layer was statistically extracted from an RGB image for thermal image masking. The field experiments occurred in a four-hectare commercial peach orchard during the primary fruit growth stage (III). The relationship between stem water potential (SWP) and crop water stress index (CWSI) was established in 2018. During 2019, a large dataset of ten thermal infrared and two RGB images was acquired. The canopy extraction methods had different accuracies: on 12 August, the overall accuracy was 83% for the 2PE method, 77% for the ED method, 84% for the VS method, and 90% for the RGB-BM method. Despite the high accuracy of the RGB-BM method, canopy edges and between-row weeds were misidentified as canopy. Canopy temperature and CWSI were calculated using the average of 100% of canopy pixels (CWSI_T100%) and the average of the coolest 33% of canopy pixels (CWSI_T33%). The CWSI_T33% dataset produced similar SWP\u2013CWSI models irrespective of the canopy extraction method used, while the CWSI_T100% yielded different and inferior models. The results highlighted the following: (1) The contribution of the RGB images is not significant for canopy extraction. Canopy pixels can be extracted with high accuracy and reliability solely with thermal images. (2) The T33% approach to canopy temperature calculation is more robust and superior to the simple mean of all canopy pixels. These noteworthy findings are a step forward in implementing thermal imagery in precision irrigation management.<\/jats:p>","DOI":"10.3390\/rs15051448","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T06:35:30Z","timestamp":1678084530000},"page":"1448","source":"Crossref","is-referenced-by-count":3,"title":["How Sensitive Is Thermal Image-Based Orchard Water Status Estimation to Canopy Extraction Quality?"],"prefix":"10.3390","volume":"15","author":[{"given":"Livia","family":"Katz","sequence":"first","affiliation":[{"name":"Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Institute, Rishon-LeZion 7505101, Israel"},{"name":"Department of Soil and Water Sciences, The Robert H. Smith Faculty of Agriculture, Food & Environment, The Hebrew University of Jerusalem, Rehovot 7610001, Israel"},{"name":"Department of Precision Agriculture, MIGAL Galilee Research Institute, Kiryat Shmona 1101602, Israel"},{"name":"Environmental Physics and Irrigation, Agricultural Research Organization, Gilat Research Center, Mobile Post Negev 8528000, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4105-7807","authenticated-orcid":false,"given":"Alon","family":"Ben-Gal","sequence":"additional","affiliation":[{"name":"Environmental Physics and Irrigation, Agricultural Research Organization, Gilat Research Center, Mobile Post Negev 8528000, Israel"}]},{"given":"M. Iggy","family":"Litaor","sequence":"additional","affiliation":[{"name":"Department of Precision Agriculture, MIGAL Galilee Research Institute, Kiryat Shmona 1101602, Israel"},{"name":"Department of Environmental Sciences, Tel Hai College, Upper Galilee, Qiryat Shemona 1220800, Israel"}]},{"given":"Amos","family":"Naor","sequence":"additional","affiliation":[{"name":"Department of Precision Agriculture, MIGAL Galilee Research Institute, Kiryat Shmona 1101602, Israel"}]},{"given":"Aviva","family":"Peeters","sequence":"additional","affiliation":[{"name":"TerraVision Lab, Midreshet Ben-Gurion 8499000, Israel"},{"name":"School of Architecture, SCE Shamoon College of Engineering, Beer Sheva 8410802, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1450-5491","authenticated-orcid":false,"given":"Eitan","family":"Goldshtein","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Institute, Rishon-LeZion 7505101, Israel"}]},{"given":"Guy","family":"Lidor","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Institute, Rishon-LeZion 7505101, Israel"}]},{"given":"Ohaliav","family":"Keisar","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Institute, Rishon-LeZion 7505101, Israel"}]},{"given":"Stav","family":"Marzuk","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Institute, Rishon-LeZion 7505101, Israel"},{"name":"Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4974-7186","authenticated-orcid":false,"given":"Victor","family":"Alchanatis","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Institute, Rishon-LeZion 7505101, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5095-4353","authenticated-orcid":false,"given":"Yafit","family":"Cohen","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Institute, Rishon-LeZion 7505101, Israel"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,4]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Gonzalez-Dugo, V., and Zarco-Tejada, P.J. (2022). Assessing the Impact of Measurement Errors in the Calculation of CWSI for Characterizing the Water Status of Several Crop Species. Irrig. Sci., 1\u201313.","key":"ref_1","DOI":"10.1007\/s00271-022-00819-6"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/S0168-1923(99)00030-1","article-title":"Use of Infrared Thermometry for Estimation of Stomatal Conductance as a Possible Aid to Irrigation Scheduling","volume":"95","author":"Jones","year":"1999","journal-title":"Agric. For. Meteorol."},{"key":"ref_4","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_5","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_6","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1007\/s11119-009-9153-x","article-title":"Crop Water Stress Mapping for Site-Specific Irrigation by Thermal Imagery and Artificial Reference Surfaces","volume":"11","author":"Meron","year":"2010","journal-title":"Precis. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1007\/s11119-013-9322-9","article-title":"Using High Resolution UAV Thermal Imagery to Assess the Variability in the Water Status of Five Fruit Tree Species within a Commercial Orchard","volume":"14","author":"Nortes","year":"2013","journal-title":"Precis. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1007\/s11119-016-9484-3","article-title":"Mapping Water Status Based on Aerial Thermal Imagery: Comparison of Methodologies for Upscaling from a Single Leaf to Commercial Fields","volume":"18","author":"Cohen","year":"2017","journal-title":"Precis. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s11119-014-9351-z","article-title":"Crop Water Stress Index Derived from Multi-Year Ground and Aerial Thermal Images as an Indicator of Potato Water Status","volume":"15","author":"Rud","year":"2014","journal-title":"Precis. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s00271-014-0447-z","article-title":"Improving the Precision of Irrigation in a Pistachio Farm Using an Unmanned Airborne Thermal System","volume":"33","author":"Goldhamer","year":"2015","journal-title":"Irrig. Sci."},{"unstructured":"Stafford, J.V. Characterization of Salinity-Induced Efects in Olive Trees Based on Thermal Imagery. Proceedings of the 10th European Conference on Precision Agriculture.","key":"ref_11"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.agwat.2017.03.030","article-title":"Assessing a Crop Water Stress Index Derived from Aerial Thermal Imaging and Infrared Thermometry in Super-High Density Olive Orchards","volume":"187","author":"Egea","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s11119-011-9232-7","article-title":"Use of Aerial Thermal Imaging to Estimate Water Status of Palm Trees","volume":"13","author":"Cohen","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s00271-012-0382-9","article-title":"Assessment of Vineyard Water Status Variability by Thermal and Multispectral Imagery Using an Unmanned Aerial Vehicle (UAV)","volume":"30","author":"Baluja","year":"2012","journal-title":"Irrig. Sci."},{"doi-asserted-by":"crossref","unstructured":"Camino, C., Zarco-Tejada, P.J., and Gonzalez-Dugo, V. (2018). Effects of Heterogeneity within Tree Crowns on Airborne-Quantified SIF and the CWSI as Indicators of Water Stress in the Context of Precision Agriculture. Remote Sens., 10.","key":"ref_16","DOI":"10.3390\/rs10040604"},{"doi-asserted-by":"crossref","unstructured":"Nixon, M.S., and Aguado, A.S. (2020). Feature Extraction and Image Processing for Computer Vision, Academic Press. [4th ed.].","key":"ref_17","DOI":"10.1016\/B978-0-12-814976-8.00003-8"},{"key":"ref_18","first-page":"1","article-title":"Individual Tree Crown Segmentation Based on Aerial Image Using Superpixel and Topological Features","volume":"14","author":"Zhou","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"684328","DOI":"10.3389\/fpls.2021.684328","article-title":"Intelligent Fruit Yield Estimation for Orchards Using Deep Learning Based Semantic Segmentation Techniques\u2014A Review","volume":"12","author":"Maheswari","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1007\/s11119-022-09877-4","article-title":"Spatiotemporal Normalized Ratio Methodology to Evaluate the Impact of Field-Scale Variable Rate Application","volume":"23","author":"Katz","year":"2022","journal-title":"Precis. Agric."},{"doi-asserted-by":"crossref","unstructured":"Park, S., Ryu, D., Fuentes, S., Chung, H., Hern\u00e1ndez-Montes, E., and O\u2019Connell, M. (2017). Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV). Remote Sens., 9.","key":"ref_21","DOI":"10.3390\/rs9080828"},{"doi-asserted-by":"crossref","unstructured":"Bian, J., Zhang, Z., Chen, J., Chen, H., Cui, C., Li, X., Chen, S., and Fu, Q. (2019). Simplified Evaluation of Cotton Water Stress Using High Resolution Unmanned Aerial Vehicle Thermal Imagery. Remote Sens., 11.","key":"ref_22","DOI":"10.3390\/rs11030267"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.compag.2018.02.018","article-title":"Economical Thermal-RGB Imaging System for Monitoring Agricultural Crops","volume":"147","author":"Osroosh","year":"2018","journal-title":"Comput. Electron. Agric."},{"doi-asserted-by":"crossref","unstructured":"Zhou, Z., Diverres, G., Kang, C., Thapa, S., Karkee, M., Zhang, Q., and Keller, M. (2022). Ground-Based Thermal Imaging for Assessing Crop Water Status in Grapevines over a Growing Season. Agronomy, 12.","key":"ref_25","DOI":"10.3390\/agronomy12020322"},{"doi-asserted-by":"crossref","unstructured":"Bahat, I., Netzer, Y., Gr\u00fcnzweig, J.M., Alchanatis, V., Peeters, A., Goldshtein, E., Ohana-Levi, N., Ben-Gal, A., and Cohen, Y. (2021). In-Season Interactions between Vine Vigor, Water Status and Wine Quality in Terrain-Based Management-Zones in a \u2018Cabernet Sauvignon\u2019 Vineyard. Remote Sens., 13.","key":"ref_26","DOI":"10.3390\/rs13091636"},{"key":"ref_27","first-page":"1118","article-title":"A Spatiotemporal Decision Support Protocol Based on Thermal Imagery for Variable Rate Drip Irrigation of a Peach Orchard","volume":"42","author":"Katz","year":"2022","journal-title":"Irrig. Sci."},{"unstructured":"Dag, A., Alchanatis, V., Zipori, I., Sprinstin, M., Cohen, A., Maravi, T., and Naor, A. (2015). Precision Agriculture \u201915, Wageningen Academic Publishers.","key":"ref_28"},{"unstructured":"Kalo, N., Edan, Y., and Alchanatis, V. Detection of Irrigation Malfunctions Based on Thermal Imaging. Proceedings of the Precision Agriculture\u201921.","key":"ref_29"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.compag.2016.04.024","article-title":"A Survey of Image Processing Techniques for Plant Extraction and Segmentation in the Field","volume":"125","author":"Hamuda","year":"2016","journal-title":"Comput. Electron. Agric."},{"unstructured":"Steduto, P., Hsiao, T.C., Fereres, E., and Raes, D. (2012). Crop Yield Response to Water, Food and Agriculture Organization of the United Nations.","key":"ref_32"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.rse.2018.11.032","article-title":"Deep Learning Based Multi-Temporal Crop Classification","volume":"221","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"unstructured":"Hijmans, R.J. (2022, January 10). Raster: Geographic Data Analysis and Modeling. Available online: https:\/\/rspatial.org\/raster.","key":"ref_34"},{"unstructured":"Bivand, R., Keitt, T., and Rowlingson, B. (2022, January 10). Rgdal: Bindings for the \u201cGeospatial\u201d Data Abstraction Library. Available online: http:\/\/rgdal.r-forge.r-project.org\/.","key":"ref_35"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v021.i12","article-title":"Reshaping Data with the {reshape} Package","volume":"21","author":"Wickham","year":"2007","journal-title":"J. Stat. Softw."},{"unstructured":"(2019). R Core Team R: A Language and Environment for Statistical Computing, R Core Team.","key":"ref_37"},{"doi-asserted-by":"crossref","unstructured":"Wickham, H. (2016). Ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag.","key":"ref_38","DOI":"10.1007\/978-3-319-24277-4"},{"unstructured":"Shimshowitz, E. (2018). The Effect of Irrigation and Crop Load on Crop Yield Anf Fruit Size Distribution in Nectarine Cv. Arctic Mist. [Master\u2019s Thesis, Tel Hai Academic College].","key":"ref_39"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/s11119-013-9310-0","article-title":"Foliage Temperature Extraction from Thermal Imagery for Crop Water Stress Determination","volume":"14","author":"Meron","year":"2013","journal-title":"Precis. Agric."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s11119-020-09735-1","article-title":"A New Color Index for Vegetation Segmentation and Classification","volume":"22","author":"Lee","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_42","first-page":"156","article-title":"Almond Tree Canopy Temperature Reveals Intra-Crown Variability That Is Water Stress-Dependent","volume":"154\u2013155","author":"Berni","year":"2012","journal-title":"Agric. For. Meteorol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1448\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T10:33:20Z","timestamp":1736937200000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1448"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,4]]},"references-count":42,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051448"],"URL":"https:\/\/doi.org\/10.3390\/rs15051448","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,3,4]]}}}