{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T08:39:56Z","timestamp":1769243996351,"version":"3.49.0"},"reference-count":102,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"internal funding in the context of Ph.D. studies"},{"name":"momerandum of understanding between the Hellenic Mediterranean University (HMU)"},{"name":"WES TRADE LTD"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study proposes a three-stage, flexible and adaptable protocol for the establishment of field-scale agricultural management zones (AMZs) using remote sensing, ground truthing (apparent electrical conductivity and soil sampling), the IRRIGOPTIMAL\u00ae system and machine learning. The methodology to develop this protocol was applied to olive and alfalfa plots in Heraklion (Crete, Greece) to monitor soil and plant responses for the period 2022\u20132024. However, the actual time for the implementation of this protocol varies between 3 and 6 months. The first step of this protocol involves the use of soil and vegetation reflectance mapping (moisture, photosynthetic activity) by satellites and unmanned aerial systems, together with geophysical electromagnetic induction mapping (apparent electrical conductivity) to verify soil variability, which is strongly linked to the delineation of management zones. In the second step, a machine learning-based prediction of the spatial distribution of soil electrical conductivity is made, considering the data obtained in the first step. Furthermore, in the second step, the IRRIGOPTIMAL\u00ae system provides real-time monitoring of a variety of weather (such as air temperature, dew point, solar radiation, relative humidity, precipitation) and soil (temperature, moisture) parameters to support the optimal cultivation strategy for the plants. Once the data have been analysed, the soil variability of the plot and the presence or absence of cultivation zones are determined and the decision on the cultivation strategy is made based on targeted soil sampling and further soil analyses. This protocol could contribute significantly to the rational use of inputs (water, seeds, fertilizers and pesticides) and support variable rate technology in the agricultural sector of Crete.<\/jats:p>","DOI":"10.3390\/rs16234486","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T08:24:14Z","timestamp":1732868654000},"page":"4486","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Delineation Protocol of Agricultural Management Zones (Olive Trees and Alfalfa) at Field Scale (Crete, Greece)"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5637-7558","authenticated-orcid":false,"given":"David","family":"Chatzidavid","sequence":"first","affiliation":[{"name":"Department of Agriculture, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6836-5143","authenticated-orcid":false,"given":"Eleni","family":"Kokinou","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Greece"}]},{"given":"Nikolaos","family":"Gerarchakis","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1959-350X","authenticated-orcid":false,"given":"Ioannis","family":"Kontogiorgakis","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2834-9700","authenticated-orcid":false,"given":"Alessio","family":"Bucaioni","sequence":"additional","affiliation":[{"name":"WES Trade Ltd., Blue Bay Level 1, 154 the Strand, GZR 3010 Gzira, Malta"}]},{"given":"Milos","family":"Bogdanovic","sequence":"additional","affiliation":[{"name":"WES Trade Ltd., Blue Bay Level 1, 154 the Strand, GZR 3010 Gzira, Malta"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"ref_1","first-page":"431","article-title":"Precision Farming and Precision Pest Management: The Power of New Crop Production Technologies","volume":"30","author":"Strickland","year":"1998","journal-title":"J. Nematol."},{"key":"ref_2","unstructured":"Singh, A.K. (2010). Precision Farming, Water Technology Centre, IARI."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/S1474-6670(17)34099-5","article-title":"Precision Farming Approaches for Small Scale Farms","volume":"34","author":"Shibusawa","year":"2001","journal-title":"IFAC Proc. Vol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1023\/A:1021171514148","article-title":"Precision Agriculture: A Challenge for Crop Nutrition Management","volume":"247","author":"Robert","year":"2002","journal-title":"Plant Soil"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0168-1699(02)00096-0","article-title":"Precision Agriculture\u2014A Worldwide Overview","volume":"36","author":"Zhang","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","first-page":"1407","article-title":"Yield Mapping In Precision Farming Development Of Hardware","volume":"259","author":"Zhang","year":"2008","journal-title":"Springer"},{"key":"ref_7","first-page":"50","article-title":"A Review: The Application of Remote Sensing, GIS and GPS in Precision Agriculture","volume":"2","author":"Goswami","year":"2012","journal-title":"Int. J. Adv. Technol. Eng. Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Heege, H.J. (2013). Precision in Crop Farming: Site Specific Concepts and Sensing Methods: Applications and Results, Springer.","DOI":"10.1007\/978-94-007-6760-7"},{"key":"ref_9","first-page":"200","article-title":"Precision Farming for Small Agricultural Farm: Indian Scenario","volume":"3","author":"Mandal","year":"2013","journal-title":"Am. J. Exp. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"78","DOI":"10.17660\/eJHS.2016\/81.2.2","article-title":"Applications of Precision Agriculture in Horticultural Crops","volume":"81","author":"Fountas","year":"2016","journal-title":"Eur. J. Hortic. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Balafoutis, A., Beck, B., Fountas, S., Vangeyte, J., Wal, T.V.d., Soto, I., G\u00f3mez-Barbero, M., Barnes, A., and Eory, V. (2017). Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics. Sustainability, 9.","DOI":"10.3390\/su9081339"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1007\/s11119-016-9482-5","article-title":"Adoption of Precision Agriculture Technologies by German Crop Farmers","volume":"18","author":"Paustian","year":"2017","journal-title":"Precis. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1007\/s11119-018-9569-2","article-title":"Science Mapping Approach to Analyze the Research Evolution on Precision Agriculture: World, EU and Italian Situation","volume":"19","author":"Pallottino","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shannon, D.K., Clay, D.E., and Kitchen, N.R. (2018). Yield Monitoring and Mapping. Precision Agriculture Basics, ASA, CSSA, SSSA.","DOI":"10.2134\/precisionagbasics"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shafi, U., Mumtaz, R., Garc\u00eda-nieto, J., and Hassan, S.A. (2019). Precision Agriculture Techniques and Practices: From Considerations to Applications. Sensors, 19.","DOI":"10.3390\/s19173796"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1146\/annurev-resource-100518-093929","article-title":"Precision Farming at the Nexus of Agricultural Production and the Environment","volume":"11","author":"Finger","year":"2019","journal-title":"Annu. Rev. Resour. Econ."},{"key":"ref_17","first-page":"1","article-title":"Precision Farming; Their Tools and Techniques","volume":"2","author":"Meena","year":"2021","journal-title":"Just Agric. Multidiscip. e-Newsl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"441","DOI":"10.9734\/ijecc\/2023\/v13i123701","article-title":"An Overview of Precision Farming","volume":"13","author":"Manasa","year":"2023","journal-title":"Int. J. Environ. Clim. Change"},{"key":"ref_19","first-page":"1","article-title":"Potential Application of Electrical Conductivity (EC) Map for Variable Rate Seeding","volume":"7","author":"Ehsani","year":"2005","journal-title":"Agric. Eng. Int. CIGR Ejournal"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1007\/s11119-013-9308-7","article-title":"Optimization of Corn Plant Population According to Management Zones in Southern Brazil","volume":"14","author":"Amado","year":"2013","journal-title":"Precis. Agric."},{"key":"ref_21","unstructured":"Fridgen, J., Fraisse, C., Kitchen, N., and Sudduth, K. (2000, January 10\u201312). Delineation and Analysis of Site-Specific Management Zones. Proceedings of the Second International Conference on Geospatial Information in Agriculture and Forestry, Lake Buena Vista, FL, USA. Available online: https:\/\/citeseerx.ist.psu.edu\/document?repid=rep1&type=pdf&doi=be11cfd475e9196d6c3bca0f2f565ecac715ef89."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.compag.2014.10.017","article-title":"Delineation of Management Zones to Improve Nitrogen Management of Wheat","volume":"110","author":"Peralta","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"957","DOI":"10.2134\/agronj2015.0381","article-title":"Validating a Digital Soil Map with Corn Yield Data for Precision Agriculture Decision Support","volume":"108","author":"Bobryk","year":"2016","journal-title":"Agron. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/bs.agron.2017.01.003","article-title":"Delineation of Soil Management Zones for Variable-Rate Fertilization: A Review","volume":"143","author":"Nawar","year":"2017","journal-title":"Adv. Agron."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.compag.2017.07.025","article-title":"A New Approach for Zoning Irregularly-Spaced, within-Field Data","volume":"141","author":"Leroux","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.geoderma.2018.02.034","article-title":"A Pedometric Technique to Delimitate Soil-Specific Zones at Field Scale","volume":"322","author":"Balzarini","year":"2018","journal-title":"Geoderma"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lajili, A., Cambouris, A.N., Chokmani, K., Duchemin, M., Perron, I., Zebarth, B.J., Biswas, A., and Adamchuk, V.I. (2021). Analysis of Four Delineation Methods to Identify Potential Management Zones in a Commercial Potato Field in Eastern Canada. Agronomy, 11.","DOI":"10.3390\/agronomy11030432"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ali, A., Rondelli, V., Martelli, R., Falsone, G., Lupia, F., and Barbanti, L. (2022). Management Zones Delineation through Clustering Techniques Based on Soils Traits, NDVI Data, and Multiple Year Crop Yields. Agriculture, 12.","DOI":"10.3390\/agriculture12020231"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chatzidavid, D., Kokinou, E., Kokolakis, S., and Karagiannidou, M. (2023). Integrating Earth Observation with Stream Health and Agricultural Activity. Remote Sens., 15.","DOI":"10.3390\/rs15235485"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1007\/s11119-019-09696-0","article-title":"Delineation of Management Zones with Spatial Data Fusion and Belief Theory","volume":"21","author":"Vallentin","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote Sensing for Agricultural Applications: A Meta-Review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"647","DOI":"10.14358\/PERS.69.6.647","article-title":"Remote Sensing for Crop Management Management","volume":"69","author":"Pinter","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gomarasca, M.A. (2009). Basic of Geomatics, Springer. [1st ed.].","DOI":"10.1007\/978-1-4020-9014-1"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Stigter, K. (2010). Applied Agrometeorology, Springer.","DOI":"10.1007\/978-3-540-74698-0"},{"key":"ref_35","first-page":"123","article-title":"Visualisation, Imagery, and the Development of Geometrical Reasoning","volume":"18","author":"Jones","year":"1998","journal-title":"Proc. Br. Res. Learn. Math."},{"key":"ref_36","first-page":"295","article-title":"Application of Remote Sensing and Gis in Agriculture and Natural Resource Management Under Changing Climatic Conditions","volume":"53","author":"Kingra","year":"2016","journal-title":"Agric. Res. J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mani, J.K., and Varghese, A.O. (2018). Remote Sensing and GIS in Agriculture and Forest Resource Monitoring. Geospatial Technologies in Land Resources Mapping, Monitoring and Management, Springer International Publishing.","DOI":"10.1007\/978-3-319-78711-4_19"},{"key":"ref_38","first-page":"2270","article-title":"Applications of Remote Sensing in Agriculture\u2014A Review","volume":"8","author":"Shanmugapriya","year":"1990","journal-title":"Appl. Remote Sens. Agric."},{"key":"ref_39","first-page":"229","article-title":"Remote Sensing of Soil Properties in Precision Agriculture: A Review","volume":"5","author":"Ge","year":"2011","journal-title":"Front. Earth Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"472","DOI":"10.2134\/agronj2003.4720","article-title":"Comparison of Electromagnetic Induction and Direct Sensing of Soil Electrical Conductivity","volume":"95","author":"Sudduth","year":"2003","journal-title":"Agron. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s11119-005-1033-4","article-title":"Mapping Potential Crop Management Zones within Fields: Use of Yield-Map Series and Patterns of Soil Physical Properties Identified by Electromagnetic Induction Sensing","volume":"6","author":"King","year":"2005","journal-title":"Precis. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/B:PRAG.0000040807.18932.80","article-title":"Estimation of Soil Textural Features from Soil Electrical Conductivity Recorded Using the EM38","volume":"5","author":"Domsch","year":"2004","journal-title":"Precis. Agric."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.compag.2004.11.002","article-title":"Characterizing Soil Spatial Variability with Apparent Soil Electrical Conductivity: I. Survey Protocols","volume":"46","author":"Corwin","year":"2005","journal-title":"Comput. Electron. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/BF02872015","article-title":"Soil Management Zones Delineated by Electrical Conductivity to Characterize Spatial and Temporal Variations in Potato Yield and in Soil Properties","volume":"83","author":"Cambouris","year":"2006","journal-title":"Am. J. Potato Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.still.2009.12.002","article-title":"Delineation of Management Zones Using Mobile Measurements of Soil Electrical Conductivity and Multivariate Geostatistical Techniques","volume":"106","year":"2010","journal-title":"Soil Tillage Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"10024","DOI":"10.3390\/s140610024","article-title":"Spatial and Temporal Patterns of Apparent Electrical Conductivity: DUALEM vs. Veris Sensors for Monitoring Soil Properties","volume":"14","author":"Serrano","year":"2014","journal-title":"Sensors"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"150","DOI":"10.14393\/BJ-v32n1a2016-26287","article-title":"The Temporal Stability of the Variability in Apparent Soil Electrical Conductivity","volume":"32","author":"Medeiros","year":"2016","journal-title":"Biosci. J."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Serrano, J., Shahidian, S., Paix\u00e3o, L., Marques da Silva, J., and Moral, F. (2022). Management Zones in Pastures Based on Soil Apparent Electrical Conductivity and Altitude: NDVI, Soil and Biomass Sampling Validation. Agronomy, 12.","DOI":"10.3390\/agronomy12040778"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Serrano, J., Mau, V., Rodrigues, R., Paix\u00e3o, L., Shahidian, S., Marques da Silva, J., Paniagua, L.L., and Moral, F.J. (2023). Definition and Validation of Vineyard Management Zones Based on Soil Apparent Electrical Conductivity and Altimetric Survey. Environments, 10.","DOI":"10.3390\/environments10070117"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"460","DOI":"10.3390\/agriengineering5010030","article-title":"Soil Density Characterization in Management Zones Based on Apparent Soil Electrical Conductivity in Two Field Systems: Rainfeed and Center-Pivot Irrigation","volume":"5","author":"Bottega","year":"2023","journal-title":"AgriEngineering"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kourgialas, N.N., Psarras, G., Morianou, G., Pisinaras, V., Koubouris, G., Digalaki, N., Malliaraki, S., Aggelaki, K., Motakis, G., and Arampatzis, G. (2022). Good Agricultural Practices Related to Water and Soil as a Means of Adaptation of Mediterranean Olive Growing to Extreme Climate-Water Conditions. Sustainability, 14.","DOI":"10.3390\/su142013673"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Kourgialas, N.N., Anastopoulos, I., and Stefanakis, A. (2024). Adapting Water and Soil Management to Climate Change. Sustainability, 16.","DOI":"10.3390\/su16062416"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Tapoglou, E., Vozinaki, A.-E.E., and Tsanis, I. (2019). Climate Change Impact on the Frequency of Hydrometeorological Extremes in the Island of Crete. Water, 11.","DOI":"10.3390\/w11030587"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Carter, M.R., and Gregorich, E.G. (2007). Soil Sampling and Methods of Analysis, CRC Press. [2nd ed.].","DOI":"10.1201\/9781420005271"},{"key":"ref_55","unstructured":"Ray, S.S., National, M., Forecast, C., and Panigrahy, S. (2004, January 12\u201323). Use of high resolution remote sensing data for generating site-specific soil management plan. Proceedings of the ISPRS Congress Technical Commission VII Symposioum, Instanbul, Turkey."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.geoderma.2005.10.009","article-title":"Detecting Salinity Hazards within a Semiarid Context by Means of Combining Soil and Remote-Sensing Data","volume":"134","author":"Douaoui","year":"2006","journal-title":"Geoderma"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"373","DOI":"10.4236\/ars.2013.24040","article-title":"Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review","volume":"2","author":"Allbed","year":"2013","journal-title":"Adv. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1016\/j.ecolind.2015.03.021","article-title":"Site-Based and Remote Sensing Methods for Monitoring Indicators of Vegetation Condition: An Australian Review","volume":"60","author":"Lawley","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.eja.2015.11.026","article-title":"Are Vegetation Indices Derived from Consumer-Grade Cameras Mounted on UAVs Sufficiently Reliable for Assessing Experimental Plots?","volume":"74","author":"Rasmussen","year":"2016","journal-title":"Eur. J. Agron."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2326","DOI":"10.3390\/agriengineering5040143","article-title":"Remote Sensing and Kriging with External Drift to Improve Sparse Proximal Soil Sensing Data and Define Management Zones in Precision Agriculture","volume":"5","author":"Rodrigues","year":"2023","journal-title":"AgriEngineering"},{"key":"ref_61","unstructured":"Ratcliff, C., Gobbett, D., and Bramley, R.G.V. PAT\u2014Precision Agriculture Tools. CSIRO.V3 Softw., 2020."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3172","DOI":"10.21105\/joss.03172","article-title":"Semi-Automatic Classification Plugin: A Python Tool for the Download and Processing of Remote Sensing Images in QGIS","volume":"6","author":"Congedo","year":"2021","journal-title":"J. Open Source Softw."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI?A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1080\/01431169308904370","article-title":"In Vivo Spectroscopy and Internal Optics of Leaves as Basis for Remote Sensing of Vegetation","volume":"14","author":"Buschmann","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1190\/1.1441097","article-title":"A Wide-Bank Electromagnetic Exploration Method\u2014Some Theoretical and Experimental Results","volume":"45","author":"Won","year":"1980","journal-title":"Geophysics"},{"key":"ref_66","unstructured":"Fraisse, C.W., Sudduth, K.A., Kitchen, N.R., and Fridgen, J.J. (1999, January 18\u201321). Use of Unsupervised Clustering Algorithms for Delineating Within-Field Management Zones. Proceedings of the 1999 ASAE\/CSAE-SCGR Annual International Meeting: Emerging Technologies for the 21st Century, Toronto, ON, Canada. Paper No. 993043."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.geoderma.2019.05.031","article-title":"Digital Soil Mapping Algorithms and Covariates for Soil Organic Carbon Mapping and Their Implications: A Review","volume":"352","author":"Lamichhane","year":"2019","journal-title":"Geoderma"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zeraatpisheh, M., Bakhshandeh, E., Emadi, M., Li, T., and Xu, M. (2020). Integration of PCA and Fuzzy Clustering for Delineation of Soil Management Zones and Cost-Efficiency Analysis in a Citrus Plantation. Sustainability, 12.","DOI":"10.3390\/su12145809"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Taghizadeh-Mehrjardi, R., Nabiollahi, K., Rasoli, L., Kerry, R., and Scholten, T. (2020). Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models. Agronomy, 10.","DOI":"10.3390\/agronomy10040573"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Emadi, M., Taghizadeh-Mehrjardi, R., Cherati, A., Danesh, M., Mosavi, A., and Scholten, T. (2020). Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran. Remote Sens., 12.","DOI":"10.3390\/rs12142234"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.apm.2019.12.016","article-title":"Selecting Appropriate Machine Learning Methods for Digital Soil Mapping","volume":"81","author":"Khaledian","year":"2020","journal-title":"Appl. Math. Model."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"115079","DOI":"10.1016\/j.geoderma.2021.115079","article-title":"Performance of Linear Mixed Models and Random Forests for Spatial Prediction of Soil PH","volume":"397","author":"Makungwe","year":"2021","journal-title":"Geoderma"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"106925","DOI":"10.1016\/j.ecolind.2020.106925","article-title":"Comparison of Random Forest and Multiple Linear Regression Models for Estimation of Soil Extracellular Enzyme Activities in Agricultural Reclaimed Coastal Saline Land","volume":"120","author":"Xie","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"8539","DOI":"10.1007\/s10668-022-02411-6","article-title":"Soil Variability Mapping and Delineation of Site-Specific Management Zones Using Fuzzy Clustering Analysis in a Mid-Himalayan Watershed, India","volume":"25","author":"Shashikumar","year":"2023","journal-title":"Environ. Dev. Sustain."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Maleki, S., Karimi, A., Mousavi, A., Kerry, R., and Taghizadeh-Mehrjardi, R. (2023). Delineation of Soil Management Zone Maps at the Regional Scale Using Machine Learning. Agronomy, 13.","DOI":"10.3390\/agronomy13020445"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Karunathilake, E.M.B.M., Le, A.T., Heo, S., Chung, Y.S., and Mansoor, S. (2023). The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture. Agriculture, 13.","DOI":"10.3390\/agriculture13081593"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"219","DOI":"10.33545\/26180723.2024.v7.i4c.536","article-title":"The Role of Precision Farming in Sustainable Agriculture: An Overview","volume":"7","author":"Biswas","year":"2024","journal-title":"Int. J. Agric. Ext. Soc. Dev."},{"key":"ref_78","first-page":"101048","article-title":"Application of Precision Agriculture Technologies in Central Europe-Review","volume":"15","author":"Zoubek","year":"2024","journal-title":"J. Agric. Food Res."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Zain, M., Ma, H., Ur Rahman, S., Nuruzzaman, M., Chaudhary, S., Azeem, I., Mehmood, F., Duan, A., and Sun, C. (2024). Nanotechnology in Precision Agriculture: Advancing towards Sustainable Crop Production. Plant Physiol. Biochem., 206.","DOI":"10.1016\/j.plaphy.2023.108244"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Moomen, A.W., Yevugah, L.L., Boakye, L., Osei, J.D., and Muthoni, F. (2024). Review of Applications of Remote Sensing towards Sustainable Agriculture in the Northern Savannah Regions of Ghana. Agriculture, 14.","DOI":"10.3390\/agriculture14040546"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Ahmed, Z., Gui, D., Murtaza, G., Yunfei, L., and Ali, S. (2023). An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands. Agronomy, 13.","DOI":"10.3390\/agronomy13082113"},{"key":"ref_82","first-page":"102004","article-title":"Delineation of Management Zones in Agricultural Fields Using Cover\u2013Crop Biomass Estimates from PlanetScope Data","volume":"85","author":"Breunig","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"120677","DOI":"10.1016\/j.jenvman.2024.120677","article-title":"Management Zones in Transboundary Aquifers: A Review of Delineation Methods under a New Framework of Cross-Border Groundwater Impacts","volume":"357","author":"Stigter","year":"2024","journal-title":"J. Environ. Manage."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"195","DOI":"10.2134\/agronj2004.1950","article-title":"Appropriateness of Management Zones for Characterizing Spatial Variability of Soil Properties and Irrigated Corn Yields across Years","volume":"96","author":"Schepers","year":"2004","journal-title":"Agron. J."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.geoderma.2007.11.003","article-title":"Key Soil and Topographic Properties to Delineate Potential Management Classes for Precision Agriculture in the European Loess Area","volume":"143","author":"Vitharana","year":"2008","journal-title":"Geoderma"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.scitotenv.2019.06.214","article-title":"Site-Specific Management of Salt Affected Soils: A Case Study from Egypt","volume":"688","author":"Shaddad","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Loures, L., Chamizo, A., Ferreira, P., Loures, A., Castanho, R., and Panagopoulos, T. (2020). Assessing the Effectiveness of Precision Agriculture Management Systems in Mediterranean Small Farms. Sustainability, 12.","DOI":"10.3390\/su12093765"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"438","DOI":"10.3390\/agriengineering3020029","article-title":"From Conventional to Precision Fertilization: A Case Study on the Transition for a Small-Medium Farm","volume":"3","author":"Brambilla","year":"2021","journal-title":"AgriEngineering"},{"key":"ref_89","unstructured":"Kerry, R., Ingram, B., and Oliver, M. (2021, January 19\u201322). Sampling Needs to Establish Effective Management Zones for Plant Nutrients in Precision Agriculture. Proceedings of the 13th European Conference on Precision Agriculture (ECPA), Budapest, Hungary."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Shi, B., Yost, R., Liu, X., Tian, Y., Zhu, Y., Cao, W., and Cao, Q. (2022). Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System. Plants, 11.","DOI":"10.3390\/plants11192611"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"105835","DOI":"10.1016\/j.catena.2021.105835","article-title":"Spatial Variability of Soil Quality within Management Zones: Homogeneity and Purity of Delineated Zones","volume":"209","author":"Zeraatpisheh","year":"2022","journal-title":"Catena"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.3390\/agriengineering5030092","article-title":"Delineating Management Zones with Different Yield Potentials in Soybean\u2013Corn and Soybean\u2013Cotton Production Systems","volume":"5","author":"Speranza","year":"2023","journal-title":"AgriEngineering"},{"key":"ref_93","unstructured":"Nikoforids, G., Kokinou, E., Chatzidavid, D., and Tzanakakis, V. (November, January 29). Precision Agriculture: Olive trees management zones by non-invasive methods. Proceedings of the 31st Conference of the Hellenic Society of Horticultural Science, Heraklion, Greece. Available online: https:\/\/31eeeo.gr\/#page."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Kritikakis, G., Kokkinou, E., Economou, N., Andronikidis, N., Brintakis, J., Daliakopoulos, I.N., Kourgialas, N., Pavlaki, A., Fasarakis, G., and Markakis, N. (2022). Estimating Soil Clay Content Using an Agrogeophysical and Agrogeological Approach: A Case Study in Chania Plain, Greece. Water, 14.","DOI":"10.3390\/w14172625"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Economou, N., Brintakis, J., Andronikidis, N., Kritikakis, G., Kokkinou, E., Papadopoulos, N., Kourgialas, N., and Vafidis, A. (2021, January 10\u201314). GPR Data Migration Velocity Estimation Using a Local Diffraction Multi-Focusing Criterion. Proceedings of the 11th Congress of the Balkan Geophysical Society, Online.","DOI":"10.3997\/2214-4609.202149BGS13"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"3086","DOI":"10.1007\/s11119-024-10191-4","article-title":"Relevance of NDVI, Soil Apparent Electrical Conductivity and Topography for Variable Rate Irrigation Zoning in an Olive","volume":"25","author":"Vanderlinden","year":"2024","journal-title":"Precis. Agric."},{"key":"ref_97","first-page":"50","article-title":"Prediction Model of Soil Electrical Conductivity Based on ELM Optimized by Bald Eagle Search Algorithm","volume":"25","author":"Huang","year":"2021","journal-title":"Electronics"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"106639","DOI":"10.1016\/j.gexplo.2020.106639","article-title":"Predicting Soil Electrical Conductivity Using Multi-Layer Perceptron Integrated with Grey Wolf Optimizer","volume":"220","author":"Mosavi","year":"2021","journal-title":"J. Geochem. Explor."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.35940\/ijitee.L3609.119119","article-title":"Random Forest Algorithm for Soil Fertility Prediction and Grading Using Machine Learning","volume":"9","author":"Kumar","year":"2019","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1080\/01431161.2016.1259681","article-title":"Quantitative Remote Sensing of Soil Electrical Conductivity Using ETM+ and Ground Measured Data","volume":"38","author":"Rahmati","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Jia, P., Du, Y., Zhao, Z., Zhao, C., Wu, Y., Guo, J., and Peng, Y. (2022). Combination of Hyperspectral and Machine Learning to Invert Soil Electrical Conductivity. Remote Sens., 14.","DOI":"10.3390\/rs14112602"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Wang, J., Peng, J., Li, H., Yin, C., Liu, W., Wang, T., and Zhang, H. (2021). Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China. Remote Sens., 13.","DOI":"10.3390\/rs13020305"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4486\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:42:43Z","timestamp":1760114563000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4486"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"references-count":102,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16234486"],"URL":"https:\/\/doi.org\/10.3390\/rs16234486","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,29]]}}}