{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:24:00Z","timestamp":1760059440749,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:00:00Z","timestamp":1749772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hellenic Ministry of Agriculture","award":["M16SYN2-00073\/PRECISION RICE","M16SYN2-00258\/ILYS","CPV: 77100000-1","\u0394\u0395\u03a16-0019641"],"award-info":[{"award-number":["M16SYN2-00073\/PRECISION RICE","M16SYN2-00258\/ILYS","CPV: 77100000-1","\u0394\u0395\u03a16-0019641"]}]},{"name":"National Center for Quality Control, Classification and Standardization of Cotton of the Institute of Industrial &amp; Livestock Plants","award":["M16SYN2-00073\/PRECISION RICE","M16SYN2-00258\/ILYS","CPV: 77100000-1","\u0394\u0395\u03a16-0019641"],"award-info":[{"award-number":["M16SYN2-00073\/PRECISION RICE","M16SYN2-00258\/ILYS","CPV: 77100000-1","\u0394\u0395\u03a16-0019641"]}]},{"name":"Opora","award":["M16SYN2-00073\/PRECISION RICE","M16SYN2-00258\/ILYS","CPV: 77100000-1","\u0394\u0395\u03a16-0019641"],"award-info":[{"award-number":["M16SYN2-00073\/PRECISION RICE","M16SYN2-00258\/ILYS","CPV: 77100000-1","\u0394\u0395\u03a16-0019641"]}]},{"name":"Greek Secretary of Research-04341, CheRemote","award":["M16SYN2-00073\/PRECISION RICE","M16SYN2-00258\/ILYS","CPV: 77100000-1","\u0394\u0395\u03a16-0019641"],"award-info":[{"award-number":["M16SYN2-00073\/PRECISION RICE","M16SYN2-00258\/ILYS","CPV: 77100000-1","\u0394\u0395\u03a16-0019641"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>In this work, an innovative process chain is set up for the regular provision of fertilization consultation services to farmers for a variety of crops, within a precision agriculture framework. The central hub of this mechanism is a geographic information system (GIS), while a 5 \u00d7 5 m point grid is the information carrier. Potential data sources include soil samples, satellite imagery, meteorological parameters, yield maps, and agronomic information. Whenever big data are available per crop, decision-making is supported by machine learning systems (MLSs). All the map data are uploaded to a farm management information system (FMIS) for visualization and storage. The recipe maps are transmitted wirelessly to variable rate technologies (VRTs) for applications in the field. To a large degree, the process chain has been automated with programming at many levels. Currently, four different service modules based on the new process chain are available in the market.<\/jats:p>","DOI":"10.3390\/computers14060234","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T09:51:24Z","timestamp":1749808284000},"page":"234","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Innovative Process Chain for Precision Agriculture Services"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3074-1670","authenticated-orcid":false,"given":"Christos","family":"Karydas","sequence":"first","affiliation":[{"name":"Ecodevelopment S.A., Filyro, P.O. Box 2420, 57010 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8965-3053","authenticated-orcid":false,"given":"Miltiadis","family":"Iatrou","sequence":"additional","affiliation":[{"name":"Soil and Water Resources Institute, Hellenic Agricultural Organization DIMITRA, P.O. Box 60435, 57001 Thessaloniki, Greece"}]},{"given":"Spiros","family":"Mourelatos","sequence":"additional","affiliation":[{"name":"Ecodevelopment S.A., Filyro, P.O. Box 2420, 57010 Thessaloniki, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,13]]},"reference":[{"key":"ref_1","unstructured":"(2025, June 12). Grand View Research. Available online: https:\/\/www.grandviewresearch.com\/industry-analysis\/precision-farming-market."},{"key":"ref_2","unstructured":"PrecisionAG (2021, March 26). ISPA Forms Official Definition of \u2018Precision Agriculture\u2019. Available online: https:\/\/www.precisionag.com\/market-watch\/ispa-forms-official-definition-of-precision-agriculture\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1111\/1477-9552.12440","article-title":"Precision agriculture technology adoption and technical efficiency","volume":"73","author":"DeLay","year":"2022","journal-title":"J. Agric. Econ."},{"key":"ref_4","first-page":"1","article-title":"Sequential Adoption and Cost Savings from Precision Agriculture","volume":"41","author":"David","year":"2016","journal-title":"J. Agric. Resour. Econ. West. Agric. Econ. Assoc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1111\/1746-692X.12354","article-title":"CAP and Advisory Services: From Farm Advisory Systems to Innovation Support","volume":"21","author":"Labarthe","year":"2022","journal-title":"EuroChoices"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Iatrou, M., Karydas, C., Iatrou, G., Pitsiorlas, I., Aschonitis, V., Raptis, I., Mpetas, S., Kravvas, K., and Mourelatos, S. (2021). Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems. Agriculture, 11.","DOI":"10.3390\/agriculture11040312"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"100175","DOI":"10.1016\/j.atech.2023.100175","article-title":"Embedding a new precision agriculture service into a farm management information system\u2014Points of innovation","volume":"4","author":"Karydas","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"ref_8","unstructured":"(2025, June 12). SAP Documentation. Available online: https:\/\/help.sap.com\/doc\/saphelp_nw75\/7.5.5\/en-US\/8f\/c08b3baaa59649e10000000a11402f\/frameset.htm."},{"key":"ref_9","unstructured":"Burrough, P.A., and McDonnell, R.A. (1998). Principles of Geographical Information Systems, Oxford University Press."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2018.05.012","article-title":"Machine Learning Approaches for Crop Yield Prediction and Nitrogen Status Estimation in Precision Agriculture: A Review","volume":"151","author":"Chlingaryan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_12","unstructured":"Dorogush, A.V., Gulin, A., Gusev, G., Kazeev, N., Prokhorenkova, L.O., and Vorobev, A. (2017). Fighting Biases with Dynamic Boosting. arXiv, Available online: http:\/\/arxiv.org\/abs\/1706.09516."},{"key":"ref_13","unstructured":"Guyon, I., von Luxburg, U., Bengio, S., Wallach, H., Fergus, R., and Vishwanathan, S. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Iatrou, M., Karydas, C., Tseni, X., and Mourelatos, S. (2022). Representation Learning with a Variational Autoencoder for Predicting Nitrogen Requirement in Rice. Remote Sens., 14.","DOI":"10.3390\/rs14235978"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D Graphics Environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_16","unstructured":"Waskom, M., Botvinnik, O., O\u2019Kane, D., Hobson, P., Lukauskas, S., Gemperline, D.C., Augspurger, A., Halchenko, Y., Cole, J.B., and Warmenhoven, J. (2022, March 16). mwaskom\/seaborn: V0.8.1 (September 2017). Available online: https:\/\/zenodo.org\/record\/883859."},{"key":"ref_17","unstructured":"Van Rossum, G., and Drake, F.L. (2010). The Python Tutorial, Python Software Foundation. Available online: http:\/\/docs.python.org\/tutorial\/."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.compag.2015.05.011","article-title":"Farm management information systems: Current situation and future perspectives","volume":"115","author":"Fountas","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.compag.2017.11.022","article-title":"Multi-level automation of farm management information systems, Comput","volume":"14","author":"Paraforos","year":"2017","journal-title":"Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fulton, J., Hawkins, E., Taylor, R., and Franzen, A. (2019). Yield Monitoring and Mapping. Precision Agriculture Basics, Soil Science Society of America, Inc.","DOI":"10.2134\/precisionagbasics.2016.0089"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Aschonitis, V.G., Karydas, C.G., Iatrou, \u039c., Mourelatos, S., Metaxa, I., Tziachris, P., and Iatrou, G. (2019). An Integrated Approach to Assessing the Soil Quality and Nutritional Status of Large and Long-Term Cultivated Rice Agro-Ecosystems. Agriculture, 9.","DOI":"10.3390\/agriculture9040080"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Karydas, C., Iatrou, M., Iatrou, G., and Mourelatos, S. (2020). Management Zone Delineation for Site-Specific Fertilization in Rice Crop Using Multi-Temporal RapidEye Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12162604"},{"key":"ref_23","unstructured":"(2025, June 12). Index Database. Available online: https:\/\/www.indexdatabase.de\/."},{"key":"ref_24","unstructured":"(2025, June 12). Google Earth Engine (GEE). Available online: https:\/\/console.cloud.google.com\/earth-engine\/configuration\/register?inv=1&invt=AbyDxQ&project=ecodev-pa&supportedpurview=project."},{"key":"ref_25","unstructured":"(2025, June 12). Copernicus Climate Data Store (CCDS). Available online: https:\/\/cds.climate.copernicus.eu\/#!\/home."},{"key":"ref_26","unstructured":"(2025, June 12). AUAV. Available online: https:\/\/www.auav.com.au\/articles\/drone-types\/#1."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1175\/BAMS-D-15-00306.1","article-title":"The Global Precipitation Measurement (GPM) Mission for Science and Society","volume":"98","author":"Petersen","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_28","unstructured":"(2025, June 12). Copernicus Data Space Ecosystem\/Sentinel Hub. Available online: https:\/\/www.sentinel-hub.com\/explore\/copernicus-data-space-ecosystem."},{"key":"ref_29","unstructured":"(2025, May 29). European Centre for Medium-Range Weather Forecasts (ECMWF). Available online: https:\/\/www.ecmwf.int\/."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Karydas, C., Chatziantoniou, M., Tremma, O., Milios, A., Stamkopoulos, K., Vassiliadis, V., and Mourelatos, S. (2023). Profitability Assessment of Precision Agriculture Applications\u2014A Step Forward in Farm Management. Appl. Sci., 13.","DOI":"10.20944\/preprints202307.0289.v1"},{"key":"ref_31","unstructured":"(2025, June 12). Cambridge University Press & Assessment. Available online: https:\/\/dictionary.cambridge.org\/us\/dictionary\/english\/descriptor."},{"key":"ref_32","unstructured":"(2025, June 12). ML Journey. Available online: https:\/\/mljourney.com\/information-retrieval-system-examples\/."},{"key":"ref_33","first-page":"64","article-title":"Optimization of fertilization recommendation in Greek rice fields using precision agriculture","volume":"19","author":"Iatrou","year":"2018","journal-title":"Agric. Econ. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1126\/science.1183899","article-title":"Precision Agriculture and Food Security","volume":"327","author":"Gebbers","year":"2010","journal-title":"Science"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/6\/234\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:51:28Z","timestamp":1760032288000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/6\/234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,13]]},"references-count":34,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["computers14060234"],"URL":"https:\/\/doi.org\/10.3390\/computers14060234","relation":{},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2025,6,13]]}}}