{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T09:59:15Z","timestamp":1786355955664,"version":"3.56.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"35","license":[{"start":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:00:00Z","timestamp":1694563200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:00:00Z","timestamp":1694563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s00521-023-08987-y","type":"journal-article","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T15:02:33Z","timestamp":1694617353000},"page":"24843-24857","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A smart IoT-based irrigation system design using AI and prediction model"],"prefix":"10.1007","volume":"35","author":[{"given":"Faeze","family":"Behzadipour","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmod","family":"Ghasemi Nezhad Raeini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4798-8031","authenticated-orcid":false,"given":"Saman","family":"Abdanan Mehdizadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Morteza","family":"Taki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bijan","family":"Khalil Moghadam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Reza","family":"Zare Bavani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaime","family":"Lloret","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"8987_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/15715124.2019.1613415","volume":"19","author":"PA Ashofteh","year":"2019","unstructured":"Ashofteh PA, Bozorg-Haddad O, Lo\u00e1iciga HA (2019) Application of bi-objective genetic programming (BO-GP) for optimizing irrigation rules using two reservoir performance criteria. Int J River Basin Manage 19:1\u201335. https:\/\/doi.org\/10.1080\/15715124.2019.1613415","journal-title":"Int J River Basin Manage"},{"issue":"1","key":"8987_CR2","first-page":"279","volume":"16","author":"M Baaghideh","year":"2019","unstructured":"Baaghideh M, Entezari A, Kordi A (2019) Investigation of the relationship between soil temperature and climate parameters in the Northwest of Iran (1992\u20132015). J Geogr Reg Dev Res 16(1):279\u2013307","journal-title":"J Geogr Reg Dev Res"},{"issue":"1","key":"8987_CR3","first-page":"35","volume":"2","author":"N Bagheri","year":"2019","unstructured":"Bagheri N (2019) Information technology is an infrastructure for the development of intelligent agriculture. Agri Inf Sci Technol 2(1):35\u201349 ((In Farsi))","journal-title":"Agri Inf Sci Technol"},{"issue":"3","key":"8987_CR4","first-page":"715","volume":"31","author":"A Barikloo","year":"2017","unstructured":"Barikloo A, Alamdari P, Moravej K, Servati M (2017) Prediction of irrigated Wheat yield by using hybrid algorithm methods of artificial neural networks and genetic algorithm. J Water Soil 31(3):715\u2013726","journal-title":"J Water Soil"},{"key":"8987_CR5","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.isprsjprs.2014.03.016","volume":"93","author":"J Behmann","year":"2014","unstructured":"Behmann J, Steinr\u00fccken J, Pl\u00fcmer L (2014) Detection of early plant stress responses in hyperspectral images. J Int Soc Photogramm Remote Sensing (ISPRS) 93:98\u2013111. https:\/\/doi.org\/10.1016\/j.isprsjprs.2014.03.016","journal-title":"J Int Soc Photogramm Remote Sensing (ISPRS)"},{"key":"8987_CR6","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.compeleceng.2018.11.013","volume":"73","author":"SR Barkunan","year":"2019","unstructured":"Barkunan SR, Bhanumathi V, Sethuram J (2019) Smart sensor for automatic drip irrigation system for paddy cultivation. Comput Electr Eng 73:180\u2013193. https:\/\/doi.org\/10.1016\/j.compeleceng.2018.11.013","journal-title":"Comput Electr Eng"},{"key":"8987_CR7","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.compag.2019.03.019","volume":"160","author":"H Biabi","year":"2019","unstructured":"Biabi H, Mehdizadeh SA, Salmi MS (2019) Design and implementation of a smart system for water management of Lilium flower using image processing. Comput Electron Agric 160:131\u2013143","journal-title":"Comput Electron Agric"},{"key":"8987_CR8","doi-asserted-by":"publisher","first-page":"100187","DOI":"10.1016\/j.iot.2020.100187","volume":"18","author":"AD Boursianis","year":"2022","unstructured":"Boursianis AD, Papadopoulou MS, Diamantoulakis P, Liopa-Tsakalidi A, Barouchas P, Salahas G, Goudos SK (2022) Internet of Things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: a comprehensive review. Int Things 18:100187","journal-title":"Int Things"},{"key":"8987_CR9","first-page":"497","volume":"9","author":"R Conception II","year":"2022","unstructured":"Conception R II, Lauguico S, Alejandrino J, Dadios E, Sybringco E, Bandala A (2022) Aquaphotomics determination of nutrient biomarker for spectrophotometric parameterization of crop growth primary macronutrients using genetic programming. Inf Process Agri 9:497\u2013513","journal-title":"Inf Process Agri"},{"issue":"5","key":"8987_CR10","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.3390\/s21051693","volume":"21","author":"L Garc\u00eda","year":"2021","unstructured":"Garc\u00eda L, Parra L, Jimenez JM, Parra M, Lloret J, Mauri PV, Lorenz P (2021) Deployment strategies of soil monitoring WSN for precision agriculture irrigation scheduling in rural areas. Sensors 21(5):1693","journal-title":"Sensors"},{"issue":"113972","key":"8987_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.geoderma.2019.113972","volume":"357","author":"A Gholizadeh","year":"2020","unstructured":"Gholizadeh A, Saberioon M, Viscarra Rossel RA, Boruvka L, Klement A (2020) Spectroscopic measurements and imaging of soil color for field-scale estimation of soil organic carbon. Geoderma 357(113972):1\u201310. https:\/\/doi.org\/10.1016\/j.geoderma.2019.113972","journal-title":"Geoderma"},{"key":"8987_CR12","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s00271-010-0225-5","volume":"29","author":"A Guven","year":"2011","unstructured":"Guven A, Kisi O (2011) Daily pan evaporation modeling using linear genetic programming technique. Irrig Sci 29:135\u2013145","journal-title":"Irrig Sci"},{"key":"8987_CR13","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/6089280","author":"A Gonzales-Briones","year":"2018","unstructured":"Gonzales-Briones A, Castellanos-Garzon JA, Mezquita Martin Y, Prieto J, Corchado JM (2018) A framework for knowledge discovery from wireless sensor networks in rural environment: a crop irrigation systems case study. Wirel Commun Mobile Comput. https:\/\/doi.org\/10.1155\/2018\/6089280","journal-title":"Wirel Commun Mobile Comput"},{"issue":"103597","key":"8987_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.micpro.2020.103597","volume":"80","author":"X Guo","year":"2021","unstructured":"Guo X (2021) Application of agricultural IoT technology based on 5 G network and FPGA. Microprocess Microsyst 80(103597):1\u20135. https:\/\/doi.org\/10.1016\/j.micpro.2020.103597","journal-title":"Microprocess Microsyst"},{"key":"8987_CR15","doi-asserted-by":"publisher","first-page":"112706","DOI":"10.1016\/j.rse.2021.112706","volume":"266","author":"L Karthikeyan","year":"2021","unstructured":"Karthikeyan L, Mishra AK (2021) Multi-Layer high-resolution soil moisture estimation using machine learning over the United States. Remot Sensing Environ. 266:112706. https:\/\/doi.org\/10.1016\/j.rse.2021.112706","journal-title":"Remot Sensing Environ."},{"key":"8987_CR16","doi-asserted-by":"publisher","first-page":"120971","DOI":"10.1016\/j.molliq.2022.120971","volume":"369","author":"S Kohzadi","year":"2023","unstructured":"Kohzadi S, Marzban N, Libra JA, Bundschuh M, Maleki A (2023) Removal of RhB from water by Fe-modified hydrochar and biochar-An experimental evaluation supported by genetic programming. J Mol Liq 369:120971","journal-title":"J Mol Liq"},{"issue":"101496","key":"8987_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ecoinf.2021.101496","volume":"67","author":"K Kr\u00edzova","year":"2022","unstructured":"Kr\u00edzova K, Kaderabek J, Novak V, Linda R, Kuresova G, Sarec p, (2022) Using a single-board computer as a low-cost instrument for SPAD value estimation through color images and chlorophyll-related spectral indices. Eco Inform 67(101496):1\u20139. https:\/\/doi.org\/10.1016\/j.ecoinf.2021.101496","journal-title":"Eco Inform"},{"issue":"2","key":"8987_CR18","first-page":"1","volume":"12","author":"G Khoshamoz","year":"2021","unstructured":"Khoshamoz G (2021) Review of integrating IoT and GIS potential to improve agriculturally activities. J GIS RS Appl Plan. 12(2):1\u201321 ((In Farsi))","journal-title":"J GIS RS Appl Plan."},{"key":"8987_CR19","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.scitotenv.2016.01.098","volume":"550","author":"Y Lei","year":"2016","unstructured":"Lei Y, Zhang H, Chen F, Zhang L (2016) How rural land use management facilitates drought risk adaptation in a changing climate- A case study in arid northern China. Sci Total Environ 550:192\u2013199. https:\/\/doi.org\/10.1016\/j.scitotenv.2016.01.098","journal-title":"Sci Total Environ"},{"key":"8987_CR20","doi-asserted-by":"publisher","first-page":"142374","DOI":"10.1109\/ACCESS.2021.3119757","volume":"9","author":"J Lloret","year":"2021","unstructured":"Lloret J, Garc\u00eda L, Jimenez JM, Sendra S, Lorenz P (2021) Cluster-based communication protocol and architecture for a wastewater purification system intended for irrigation. IEEE Access 9:142374\u2013142389","journal-title":"IEEE Access"},{"key":"8987_CR21","doi-asserted-by":"publisher","unstructured":"Miloudi L, Rezeg K (2018) Leveraging the power of Integrated solutions of IoT and GIS. In: At the 2018 3rd International conference on pattern analysis and intelligent systems (PAIS). https:\/\/doi.org\/10.1109\/PAIS.2018.8598500.","DOI":"10.1109\/PAIS.2018.8598500"},{"key":"8987_CR22","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.scienta.2017.12.047","volume":"213","author":"M Nadafzadeh","year":"2018","unstructured":"Nadafzadeh M, Abdanan Mehdizadeh S, Soltanikazemi M (2018) Development of computer vision system to predict peroxidase and polyphenol oxidase enzymes to evaluate the process of banana peel browning using genetic programming modeling. Sci Hortic 213:201\u2013209. https:\/\/doi.org\/10.1016\/j.scienta.2017.12.047","journal-title":"Sci Hortic"},{"key":"8987_CR23","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.compag.2015.09.006","volume":"118","author":"Y Osroosh","year":"2015","unstructured":"Osroosh Y, Peters RT, Campbell CS, Zhang Q (2015) Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold. Comput Electron Agric 118:193\u2013203","journal-title":"Comput Electron Agric"},{"key":"8987_CR24","doi-asserted-by":"publisher","DOI":"10.3233\/APC210237","author":"N Patankar","year":"2021","unstructured":"Patankar N, Charmal M, Bhaskar N, Janrao S, Kamble A (2021) A Wi-Fi based smart irrigation monitoring for an agricultural environment. Book Recent Trends Intens Comput. https:\/\/doi.org\/10.3233\/APC210237","journal-title":"Book Recent Trends Intens Comput"},{"issue":"104880","key":"8987_CR25","first-page":"1","volume":"163","author":"F P\u00e9rez-Rodr\u00edguez","year":"2019","unstructured":"P\u00e9rez-Rodr\u00edguez F, G\u00f3mez-Garc\u00eda E (2019) Codelplant: Regression-based processing of RGB images for colour models in plant image segmentation. Comput Electron Agric 163(104880):1\u20133","journal-title":"Comput Electron Agric"},{"issue":"105889","key":"8987_CR26","first-page":"1","volume":"180","author":"M Pincheira","year":"2021","unstructured":"Pincheira M, Vecchio M, Giaffreda R, Kanhere SS (2021) Cost-effective IoT devices as trustworthy data sources for a blockchain-based water management system in precision agriculture. Comput Electron Agric 180(105889):1\u20139","journal-title":"Comput Electron Agric"},{"issue":"82","key":"8987_CR27","first-page":"1","volume":"22","author":"M Pourgholam-Amiji","year":"2021","unstructured":"Pourgholam-Amiji M, Liaghat A, Ahmadaali Kh (2021) Modeling of design and implementation cost of drip irrigation systems. Irrig Drain Struct Eng Res 22(82):1\u201322","journal-title":"Irrig Drain Struct Eng Res"},{"issue":"6","key":"8987_CR28","first-page":"1","volume":"3","author":"M Rahimi Jahangirlou","year":"2021","unstructured":"Rahimi Jahangirlou M (2021) Agronomic and breeding strategies and smart technologies for mitigating drought stress impacts on crop plants. Agri Inf Sci Technol 3(6):1\u201322 ((In Farsi))","journal-title":"Agri Inf Sci Technol"},{"issue":"104404","key":"8987_CR29","first-page":"1","volume":"217","author":"S Sabzi","year":"2021","unstructured":"Sabzi S, Pourdarbani R, Rohban MH, Garc\u00eda-Mateos G, Arribas JI (2021) Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions. Chemo Int Lab Syst. 217(104404):1\u201314","journal-title":"Chemo Int Lab Syst."},{"issue":"2","key":"8987_CR30","first-page":"274","volume":"24","author":"AA Sabziparvar","year":"2010","unstructured":"Sabziparvar AA, Zare Abyaneh H, Bayat Varkeshi M (2010) A model comparison between predicted soil temperatures using ANFIS model and regression methods in three different climates. J Water Soil 24(2):274\u2013285","journal-title":"J Water Soil"},{"key":"8987_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12517-020-05375-x","volume":"13","author":"OA Sanuade","year":"2020","unstructured":"Sanuade OA, Hassan AM, Akanji AO, Olaojo AA, Oladunjoye MA, Abdulraheem A (2020) New empirical equation to estimate the soil moisture content based on thermal properties using machine learning techniques. Arabian J Geosci 13:1\u20134. https:\/\/doi.org\/10.1007\/s12517-020-05375-x","journal-title":"Arabian J Geosci"},{"issue":"17","key":"8987_CR32","doi-asserted-by":"publisher","first-page":"3796","DOI":"10.3390\/s19173796","volume":"19","author":"U Shafi","year":"2019","unstructured":"Shafi U, Mumtaz R, Gracia-Nieto J, Hassan SA, Zaidi SAR, Iqbal N (2019) Precision agriculture techniques and practices: from consideration to applications. Sensors 19(17):3796","journal-title":"Sensors"},{"key":"8987_CR33","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.compag.2018.10.001","volume":"155","author":"R Sharma","year":"2018","unstructured":"Sharma R, Kamble SS, Gunasekaran A (2018) Big GIS analytics framework for agriculture review identifying the current trends and future perspectives. Comput Electron Agric 155:103\u2013120","journal-title":"Comput Electron Agric"},{"key":"8987_CR34","first-page":"490","volume-title":"International Conference on Applied Human Factors and Ergonomics","author":"JA Sheikh","year":"2020","unstructured":"Sheikh JA, Cheema SM, Ali M, Amjad Z, Tariq JZ, Naz A (2020) IoT and AI in precision agriculture: designing smart system to support illiterate farmers. International Conference on Applied Human Factors and Ergonomics. Springer, Cham, pp 490\u2013496"},{"key":"8987_CR35","doi-asserted-by":"publisher","first-page":"105860","DOI":"10.1016\/j.compag.2020.105860","volume":"180","author":"P Shi","year":"2020","unstructured":"Shi P, Wang Y, Xu J, Zhao Y, Yang B, Yuan Z, Sun Q (2020) Rice nitrogen nutrition estimation with RGB images and machine learning methods. Comput Electr Agri 180:105860. https:\/\/doi.org\/10.1016\/j.compag.2020.105860","journal-title":"Comput Electr Agri"},{"issue":"107912","key":"8987_CR36","first-page":"1","volume":"100","author":"PK Singh","year":"2022","unstructured":"Singh PK, Sharma A (2022) An intelligent WSN-UAV-based IoT framework for precision agriculture application. Comput Electr Eng 100(107912):1\u201317","journal-title":"Comput Electr Eng"},{"issue":"1","key":"8987_CR37","first-page":"71","volume":"3","author":"M Taki","year":"2016","unstructured":"Taki M, Ajabshirchi Y, Ranjbar SF, Rohani A, Matloobi M (2016) Prediction of soil temperature and inside air humidity in a semisolar greenhouse equipped with cement north wall by artificial neural network; case study: Tabria city. Agri Mech 3(1):71\u201383 ((In Farsi))","journal-title":"Agri Mech"},{"key":"8987_CR38","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.biosystemseng.2017.09.007","volume":"164","author":"A Tzounis","year":"2017","unstructured":"Tzounis A, Katsoulas N, Bartzanas T, Kittas C (2017) Internet of things in agriculture, recent advances and future challenges. Biosys Eng 164:31\u201348","journal-title":"Biosys Eng"},{"issue":"103822","key":"8987_CR39","first-page":"1","volume":"82","author":"P Wang","year":"2021","unstructured":"Wang P, Hafshejani BA, Wang D (2021) An improved multilayer perceptron approach for detecting sugarcane yield production in IoT-based smart agriculture. Microprocess Microsyst 82(103822):1\u20137","journal-title":"Microprocess Microsyst"},{"issue":"1275","key":"8987_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs14051275","volume":"14","author":"C Wen","year":"2022","unstructured":"Wen C, Lu M, Bi Y, Zhang Sh, Xue B, Zhang M, Zhou Q, Wu W (2022) An object-based genetic programming approach for cropland field extraction. Remote Sensing 14(1275):1\u201317. https:\/\/doi.org\/10.3390\/rs14051275","journal-title":"Remote Sensing"},{"key":"8987_CR41","doi-asserted-by":"publisher","first-page":"100209","DOI":"10.1016\/j.prime.2023.100209","volume":"5","author":"P Wong","year":"2023","unstructured":"Wong P, Wong WK, Juwono FH, Lease BA, Gopal L, Chew IM (2023) Sensor abnormality detection in multistage compressor units: a white box approach using tree-based genetic programming. e-Prime-Adv Electr Eng Energy 5:100209\u2013100298","journal-title":"e-Prime-Adv Electr Eng Energy"},{"key":"8987_CR42","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1016\/j.jhydrol.2018.06.081","volume":"563","author":"W Zhao","year":"2018","unstructured":"Zhao W, Sanchez N, Lu H, Li A (2018) A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression. J Hydrol 563:1009\u20131024","journal-title":"J Hydrol"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08987-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08987-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08987-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T12:06:12Z","timestamp":1700136372000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08987-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,13]]},"references-count":42,"journal-issue":{"issue":"35","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["8987"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08987-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,13]]},"assertion":[{"value":"3 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This work was supported by the Agricultural Sciences and Natural Resources University of Khuzestan.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}