{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T09:20:00Z","timestamp":1764148800384,"version":"3.46.0"},"reference-count":82,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"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":["Appl Intell"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s10489-025-06876-6","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T07:37:48Z","timestamp":1762501068000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid multi-output regression with residual correction for smart agriculture: a scalable and interpretable approach"],"prefix":"10.1007","volume":"55","author":[{"given":"Nguyen Minh","family":"Son","sequence":"first","affiliation":[]},{"given":"Do Si","family":"Truong","sequence":"additional","affiliation":[]},{"given":"Thanh Q.","family":"Nguyen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"6876_CR1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3394617","author":"A Naseer","year":"2024","unstructured":"Naseer A, Shmoon M, Shakeel T, Ur Rehman S, Ahmad A, Gruhn V (2024) A systematic literature review of the IoT in agriculture-global adoption, innovations, security privacy challenges. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2024.3394617","journal-title":"IEEE Access"},{"key":"6876_CR2","doi-asserted-by":"publisher","first-page":"100041","DOI":"10.1016\/j.dajour.2022.100041","volume":"3","author":"SKS Durai","year":"2022","unstructured":"Durai SKS, Shamili MD (2022) Smart farming using machine learning and deep learning techniques. Decis Analytics J 3:100041. https:\/\/doi.org\/10.1016\/j.dajour.2022.100041","journal-title":"Decis Analytics J"},{"key":"6876_CR3","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s11119-005-0681-8","volume":"6","author":"A McBratney","year":"2005","unstructured":"McBratney A, Whelan B, Ancev T, Bouma J (2005) Future directions of precision agriculture. Precis Agric 6:7\u201323","journal-title":"Precis Agric"},{"key":"6876_CR4","doi-asserted-by":"publisher","first-page":"129551","DOI":"10.1109\/ACCESS.2019.2932609","volume":"7","author":"M Ayaz","year":"2019","unstructured":"Ayaz M, Ammad-Uddin M, Sharif Z, Mansour A, Aggoune E-HM (2019) Internet-of-Things (IoT)-based smart agriculture: toward making the fields talk. IEEE Access 7:129551\u2013129583","journal-title":"IEEE Access"},{"key":"6876_CR5","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/S0168-1699(02)00096-0","volume":"36","author":"N Zhang","year":"2002","unstructured":"Zhang N, Wang M, Wang N (2002) Precision agriculture\u2014a worldwide overview. Comput Electron Agric 36:2\u20133","journal-title":"Comput Electron Agric"},{"key":"6876_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0168-1699(00)00153-8","volume":"30","author":"H Auernhammer","year":"2001","unstructured":"Auernhammer H (2001) Precision farming\u2014the environmental challenge. Comput Electron Agric 30:1\u20133","journal-title":"Comput Electron Agric"},{"issue":"1","key":"6876_CR7","doi-asserted-by":"publisher","first-page":"57","DOI":"10.13031\/trans.13355","volume":"63","author":"SR Evett","year":"2020","unstructured":"Evett SR, O\u2019Shaughnessy SA, Andrade MA, Kustas WP, Anderson MC, Schomberg H, Thompson A (2020) Precision agriculture and irrigation: current US perspectives. Trans ASABE 63(1):57\u201367","journal-title":"Trans ASABE"},{"issue":"9","key":"6876_CR8","doi-asserted-by":"publisher","first-page":"4883","DOI":"10.3390\/su13094883","volume":"13","author":"N Khan","year":"2021","unstructured":"Khan N, Ray RL, Sargani GR, Ihtisham M, Khayyam M, Ismail S (2021) Current progress and future prospects of agriculture technology: gateway to sustainable agriculture. Sustainability 13(9):4883","journal-title":"Sustainability"},{"issue":"1","key":"6876_CR9","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1007\/s11831-021-09588-5","volume":"29","author":"JA Wani","year":"2022","unstructured":"Wani JA, Sharma S, Muzamil M, Ahmed S, Sharma S, Singh S (2022) Machine learning and deep learning based computational techniques in automatic agricultural diseases detection: methodologies, applications, and challenges. Arch Comput Methods Eng 29(1):641\u2013677","journal-title":"Arch Comput Methods Eng"},{"key":"6876_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3380830","author":"IM Mehedi","year":"2024","unstructured":"Mehedi IM, Hanif MS, Bilal M, Vellingiri MT, Palaniswamy T (2024) Remote sensing and decision support system applications in precision agriculture: challenges and possibilities. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2024.3380830","journal-title":"IEEE Access"},{"issue":"01","key":"6876_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.36548\/jismac.2021.1.001","volume":"3","author":"V Suma","year":"2021","unstructured":"Suma V (2021) Internet-of-things (IoT) based smart agriculture in India-an overview. J ISMAC 3(01):1\u201315","journal-title":"J ISMAC"},{"key":"6876_CR12","unstructured":"Roscher R, Roth L, Stachniss C, Walter A (2023) Data-centric digital agriculture: a perspective, arXiv preprint arXiv:2312.03437"},{"issue":"13","key":"6876_CR13","doi-asserted-by":"publisher","first-page":"7405","DOI":"10.3390\/app13137405","volume":"13","author":"RCd Oliveira","year":"2023","unstructured":"Oliveira RCd, Silva RDdSe (2023) Artificial intelligence in agriculture: benefits, challenges, and trends. Appl Sci 13(13):7405","journal-title":"Appl Sci"},{"key":"6876_CR14","doi-asserted-by":"crossref","unstructured":"Sharma R (2021) Artificial intelligence in agriculture: a review. In: (2021) 5th international conference on intelligent computing and control systems (ICICCS). IEEE, pp 937\u2013942","DOI":"10.1109\/ICICCS51141.2021.9432187"},{"key":"6876_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.indic.2025.100607","author":"V Choudhary","year":"2025","unstructured":"Choudhary V, Guha P, Pau G, Mishra S (2025) An overview of smart agriculture using internet of things (IoT) and web services. Environ Sustain Indic. https:\/\/doi.org\/10.1016\/j.indic.2025.100607","journal-title":"Environ Sustain Indic"},{"key":"6876_CR16","doi-asserted-by":"publisher","first-page":"21219","DOI":"10.1109\/ACCESS.2022.3152544","volume":"10","author":"S Qazi","year":"2022","unstructured":"Qazi S, Khawaja BA, Farooq QU (2022) IoT-equipped and AI-enabled next generation smart agriculture: a critical review, current challenges and future trends. IEEE Access 10:21219\u201321235","journal-title":"IEEE Access"},{"issue":"11","key":"6876_CR17","doi-asserted-by":"publisher","first-page":"3758","DOI":"10.3390\/s21113758","volume":"21","author":"L Benos","year":"2021","unstructured":"Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D (2021) Machine learning in agriculture: a comprehensive updated review. Sensors 21(11):3758","journal-title":"Sensors"},{"issue":"6","key":"6876_CR18","doi-asserted-by":"publisher","first-page":"2053","DOI":"10.1007\/s11119-021-09806-x","volume":"22","author":"MH Saleem","year":"2021","unstructured":"Saleem MH, Potgieter J, Arif KM (2021) Automation in agriculture by machine and deep learning techniques: a review of recent developments. Precis Agric 22(6):2053\u20132091","journal-title":"Precis Agric"},{"key":"6876_CR19","doi-asserted-by":"publisher","first-page":"102986","DOI":"10.1016\/j.aei.2024.102986","volume":"64","author":"Y Sun","year":"2025","unstructured":"Sun Y, Tao H, Stojanovic V (2025) Pseudo-label guided dual classifier domain adversarial network for unsupervised cross-domain fault diagnosis with small samples. Adv Eng Inform 64:102986","journal-title":"Adv Eng Inform"},{"issue":"14","key":"6876_CR20","doi-asserted-by":"publisher","first-page":"107070","DOI":"10.1016\/j.jfranklin.2024.107070","volume":"361","author":"D Zheng","year":"2024","unstructured":"Zheng D, Song X, Song S, Stojanovic V (2024) Quantized control for interconnected PDE systems via mobile measurement and control strategies. J Franklin Inst 361(14):107070","journal-title":"J Franklin Inst"},{"issue":"10","key":"6876_CR21","doi-asserted-by":"publisher","first-page":"3473","DOI":"10.1002\/acs.3885","volume":"38","author":"Z Peng","year":"2024","unstructured":"Peng Z, Song X, Song S, Stojanovic V (2024) Spatiotemporal fault estimation for switched nonlinear reaction\u2013diffusion systems via adaptive iterative learning. Int J Adapt Control Signal Process 38(10):3473\u20133483","journal-title":"Int J Adapt Control Signal Process"},{"key":"6876_CR22","doi-asserted-by":"publisher","first-page":"132440","DOI":"10.1016\/j.jhydrol.2024.132440","volume":"649","author":"M He","year":"2025","unstructured":"He M et al (2025) Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning. J Hydrol 649:132440. https:\/\/doi.org\/10.1016\/j.jhydrol.2024.132440","journal-title":"J Hydrol"},{"key":"6876_CR23","doi-asserted-by":"publisher","unstructured":"Lu S, Guo J, Zimmer-Dauphinee JR, Nieusma JM, Wang X, Wernke SA, Huo Y (2025) Vision foundation models in remote sensing: a survey. IEEE Geoscience Remote Sens Magazine https:\/\/doi.org\/10.1109\/MGRS.2025.3541952","DOI":"10.1109\/MGRS.2025.3541952"},{"key":"6876_CR24","doi-asserted-by":"publisher","unstructured":"Xiao A, Xuan W, Wang J, Huang J, Tao D, Lu S, Yokoya N (2025) Foundation models for remote sensing and Earth observation: A survey. IEEE Geoscience Remote Sens Magazine https:\/\/doi.org\/10.1109\/MGRS.2025.3576766","DOI":"10.1109\/MGRS.2025.3576766"},{"key":"6876_CR25","doi-asserted-by":"publisher","unstructured":"Luo Y, Yao T (2024) Remote-sensing foundation model for agriculture: a survey. In: Proceedings of the 6th ACM international conference on multimedia in Asia workshops, pp 1\u20137 https:\/\/doi.org\/10.1145\/3700410.3702133","DOI":"10.1145\/3700410.3702133"},{"key":"6876_CR26","doi-asserted-by":"publisher","first-page":"114825","DOI":"10.1016\/j.rse.2025.114825","volume":"327","author":"MG Hashemi","year":"2025","unstructured":"Hashemi MG, Alemohammad H, Jalilvand E, Tan P-N, Judge J, Cosh M, Das NN (2025) Estimating crop biophysical parameters from satellite-based SAR and optical observations using self-supervised learning with geospatial foundation models. Remote Sens Environ 327:114825","journal-title":"Remote Sens Environ"},{"issue":"8","key":"6876_CR27","first-page":"847","volume":"15","author":"S Yin","year":"2025","unstructured":"Yin S, Xi Y, Zhang X, Sun C, Mao Q (2025) Foundation Models Agriculture: Compr Rev Agriculture 15(8):847","journal-title":"Foundation Models Agriculture: Compr Rev Agriculture"},{"key":"6876_CR28","unstructured":"Deforce B, Baesens B, Asensio ES (2024) Time-Series Foundation Models for Forecasting Soil Moisture Levels in Smart Agriculture, arXiv preprint arXiv:2405.18913"},{"key":"6876_CR29","doi-asserted-by":"publisher","first-page":"125123","DOI":"10.1016\/j.eswa.2024.125123","volume":"259","author":"F Hu","year":"2025","unstructured":"Hu F et al (2025) Transforming agriculture with advanced robotic decision systems via deep recurrent learning. Expert Syst Appl 259:125123","journal-title":"Expert Syst Appl"},{"issue":"2","key":"6876_CR30","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.aac.2024.07.004","volume":"4","author":"S Bisht","year":"2025","unstructured":"Bisht S, Roy S (2025) Optimizing role assignment for scaling innovations through AI in agricultural frameworks: an effective approach. Adv Agrochem 4(2):106\u2013113","journal-title":"Adv Agrochem"},{"issue":"13","key":"6876_CR31","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.3390\/agriculture15131351","volume":"15","author":"K Zohaib","year":"2025","unstructured":"Zohaib K, Shen Y, Liu H (2025) ObjectDetection in agriculture: a comprehensive review of methods, applications, challenges, and future directions. Agriculture 15(13):1351. https:\/\/doi.org\/10.3390\/agriculture15131351","journal-title":"Agriculture"},{"key":"6876_CR32","doi-asserted-by":"publisher","first-page":"62199","DOI":"10.1109\/ACCESS.2024.3395532","volume":"12","author":"A Holzinger","year":"2024","unstructured":"Holzinger A, Fister I, Kaul H-P, Asseng S (2024) Human-centered AI in smart farming: toward agriculture 5.0. IEEE Access 12:62199\u201362214","journal-title":"IEEE Access"},{"key":"6876_CR33","doi-asserted-by":"publisher","unstructured":"Sharma R, Sharma SK (2025) Optimizing agricultural downstream supply chain: addressing information asymmetry and losses. Bus Process Manage J.\u00a0https:\/\/doi.org\/10.1108\/BPMJ-02-2024-0097","DOI":"10.1108\/BPMJ-02-2024-0097"},{"issue":"1","key":"6876_CR34","first-page":"759","volume":"68","author":"E-SM El-Kenawy","year":"2025","unstructured":"El-Kenawy E-SM, Alhussan AA, Khodadadi N, Mirjalili S, Eid MM (2025) Predicting potato crop yield with machine learning and deep learning for sustainable agriculture. Potato Res 68(1):759\u2013792","journal-title":"Potato Res"},{"issue":"4s","key":"6876_CR35","first-page":"692","volume":"11","author":"VCS Rao","year":"2025","unstructured":"Rao VCS, Voore Subrahmanyam DAM, Radhika P, Shireesha P (2025) Spatio-temporal deep learning models for forecasting agricultural drought in rain-fed regions. Int J Environ Sci 11(4s):692\u2013701","journal-title":"Int J Environ Sci"},{"key":"6876_CR36","doi-asserted-by":"crossref","unstructured":"Ahammad M, Hanif Sikder M, Kawser Rabbi A, Khanom NN, Nazmul Alam SM, Fatema Khatun M (2024) ST-Net: spatio-temporal network for predicting crop yields with unprecedented precision. In: International conference on machine intelligence and emerging technologies. Springer, pp 553\u2013566","DOI":"10.1007\/978-981-96-2721-9_36"},{"issue":"2","key":"6876_CR37","first-page":"41","volume":"7","author":"Y Huan","year":"2025","unstructured":"Huan Y, Beilei F, Chenxue Y, Xian L (2025) Graph neural networks for knowledge graph construction: research progress, agricultural development potential, and future directions. Smart Agric 7(2):41","journal-title":"Smart Agric"},{"issue":"7","key":"6876_CR38","doi-asserted-by":"publisher","first-page":"972","DOI":"10.3390\/plants13070972","volume":"13","author":"Y Lu","year":"2024","unstructured":"Lu Y et al (2024) Application of multimodal transformer model in intelligent agricultural disease detection and question-answering systems. Plants 13(7):972","journal-title":"Plants"},{"key":"6876_CR39","doi-asserted-by":"publisher","DOI":"10.1109\/TAFE.2024.3438330","author":"S Sarkar","year":"2024","unstructured":"Sarkar S, Dey A, Pradhan R, Sarkar UM, Chatterjee C, Mondal A, Mitra P (2024) Crop yield prediction using multimodal meta-transformer and temporal graph neural networks. IEEE Trans Agrifood Electron. https:\/\/doi.org\/10.1109\/TAFE.2024.3438330","journal-title":"IEEE Trans Agrifood Electron"},{"key":"6876_CR40","doi-asserted-by":"publisher","first-page":"108690","DOI":"10.1016\/j.compag.2024.108690","volume":"218","author":"Y Jung","year":"2024","unstructured":"Jung Y, Byun S, Kim B, Amin SU, Seo S (2024) Harnessing synthetic data for enhanced detection of pine wilt disease: an image classification approach. Comput Electron Agric 218:108690","journal-title":"Comput Electron Agric"},{"key":"6876_CR41","doi-asserted-by":"publisher","first-page":"107881","DOI":"10.1016\/j.engappai.2024.107881","volume":"131","author":"Y Akkem","year":"2024","unstructured":"Akkem Y, Biswas SK, Varanasi A (2024) A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network. Eng Appl Artif Intell 131:107881","journal-title":"Eng Appl Artif Intell"},{"key":"6876_CR42","doi-asserted-by":"publisher","first-page":"1360113","DOI":"10.3389\/fpls.2024.1360113","volume":"15","author":"J Klein","year":"2024","unstructured":"Klein J, Waller R, Pirk S, Pa\u0142ubicki W, Tester M, Michels DL (2024) Synthetic data at scale: a development model to efficiently leverage machine learning in agriculture. Front Plant Sci 15:1360113","journal-title":"Front Plant Sci"},{"issue":"10","key":"6876_CR43","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1080\/08839514.2021.1922841","volume":"35","author":"I Iqbal","year":"2021","unstructured":"Iqbal I, Odesanmi GA, Wang J, Liu L (2021) Comparative investigation of learning algorithms for image classification with small dataset. Appl Artif Intell 35(10):697\u2013716","journal-title":"Appl Artif Intell"},{"issue":"23","key":"6876_CR44","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1007\/s12665-024-11950-2","volume":"83","author":"C Zhang","year":"2024","unstructured":"Zhang C, Iqbal I, Bhatti UA, Liu J, Awwad EM, Sarhan N (2024) ResNet50 in remote sensing and agriculture: evaluating image captioning performance for high spectral data. Environ Earth Sci 83(23):655","journal-title":"Environ Earth Sci"},{"key":"6876_CR45","unstructured":"Montgomery DC, Peck EA, Vining GG (2021) Introduction to linear regression analysis. John Wiley & Sons"},{"key":"6876_CR46","doi-asserted-by":"crossref","unstructured":"Little RJ, Rubin DB (2019) Statistical analysis with missing data. John Wiley & Sons","DOI":"10.1002\/9781119482260"},{"key":"6876_CR47","doi-asserted-by":"crossref","unstructured":"Montesinos-L\u00f3pez OA et al (2019) A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data. G3: Genes, Genomes, Genetics 9(10):3381\u20133393","DOI":"10.1534\/g3.119.400336"},{"key":"6876_CR48","doi-asserted-by":"publisher","first-page":"105899","DOI":"10.1016\/j.engappai.2023.105899","volume":"120","author":"Y Akkem","year":"2023","unstructured":"Akkem Y, Biswas SK, Varanasi A (2023) Smart farming using artificial intelligence: a review. Eng Appl Artif Intell 120:105899","journal-title":"Eng Appl Artif Intell"},{"key":"6876_CR49","doi-asserted-by":"publisher","first-page":"78686","DOI":"10.1109\/ACCESS.2023.3298215","volume":"11","author":"AA AlZubi","year":"2023","unstructured":"AlZubi AA, Galyna K (2023) Artificial intelligence and internet of things for sustainable farming and smart agriculture. IEEE Access 11:78686\u201378692","journal-title":"IEEE Access"},{"key":"6876_CR50","doi-asserted-by":"publisher","unstructured":"Kucuk C, Birant D, Yildirim Taser P (2021) Conference an intelligent multi-output regression model for soil moisture prediction, presented at the INFUS 2022.\u00a0https:\/\/doi.org\/10.1007\/978-3-030-85577-2_56","DOI":"10.1007\/978-3-030-85577-2_56"},{"key":"6876_CR51","doi-asserted-by":"publisher","first-page":"102000","DOI":"10.1016\/j.ecoinf.2023.102000","volume":"75","author":"O Mzoughi","year":"2023","unstructured":"Mzoughi O, Yahiaoui I (2023) Deep learning-based segmentation for disease identification. Ecol Informatics 75:102000","journal-title":"Ecol Informatics"},{"issue":"1","key":"6876_CR52","doi-asserted-by":"publisher","first-page":"15997","DOI":"10.1038\/s41598-023-42356-y","volume":"13","author":"S Rani","year":"2023","unstructured":"Rani S (2023) Machine learning-based optimal crop selection system in smart agriculture. Sci Rep 13(1):15997","journal-title":"Sci Rep"},{"key":"6876_CR53","doi-asserted-by":"publisher","first-page":"1158933","DOI":"10.3389\/fpls.2023.1158933","volume":"14","author":"M Shoaib","year":"2023","unstructured":"Shoaib M (2023) An advanced deep learning models-based plant disease detection: a review of recent research. Front Plant Sci 14:1158933","journal-title":"Front Plant Sci"},{"key":"6876_CR54","doi-asserted-by":"crossref","unstructured":"Ghosh P, Kumpatla SP (2022) GIS applications in agriculture. In: Geographic information systems and applications in coastal studies. IntechOpen","DOI":"10.5772\/intechopen.104786"},{"key":"6876_CR55","doi-asserted-by":"publisher","first-page":"107936","DOI":"10.1016\/j.cie.2022.107936","volume":"165","author":"A Sharma","year":"2022","unstructured":"Sharma A, Georgi M, Tregubenko M, Tselykh A, Tselykh A (2022) Enabling smart agriculture by implementing artificial intelligence and embedded sensing. Comput Ind Eng 165:107936","journal-title":"Comput Ind Eng"},{"key":"6876_CR56","doi-asserted-by":"crossref","unstructured":"Kucuk C, Birant D, Yildirim Taser P (2021) An intelligent multi-output regression model for soil moisture prediction. In: Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS held August 24\u201326, 2021. Volume 2, 2022: Springer, pp 474\u2013481","DOI":"10.1007\/978-3-030-85577-2_56"},{"issue":"3","key":"6876_CR57","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2(3):160","journal-title":"SN Comput Sci"},{"key":"6876_CR58","first-page":"102959","volume":"113","author":"A Tripathi","year":"2022","unstructured":"Tripathi A, Tiwari RK, Tiwari SP (2022) A deep learning multi-layer perceptron and remote sensing approach for soil health based crop yield estimation. Int J Appl Earth Obs Geoinf 113:102959","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"6876_CR59","doi-asserted-by":"publisher","first-page":"1158933","DOI":"10.3389\/fpls.2023.1158933","volume":"14","author":"M Shoaib","year":"2023","unstructured":"Shoaib M et al (2023) An advanced deep learning models-based plant disease detection: a review of recent research. Front Plant Sci 14:1158933","journal-title":"Front Plant Sci"},{"issue":"14","key":"6876_CR60","doi-asserted-by":"publisher","first-page":"8561","DOI":"10.3390\/su14148561","volume":"14","author":"SK Behera","year":"2022","unstructured":"Behera SK et al (2022) The scope for using proximal soil sensing by the farmers of India. Sustainability 14(14):8561","journal-title":"Sustainability"},{"issue":"1","key":"6876_CR61","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"issue":"3","key":"6876_CR62","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.jksuci.2016.10.003","volume":"30","author":"PP Ray","year":"2018","unstructured":"Ray PP (2018) A survey on internet of things architectures. Journal of King Saud University-Computer and Information Sciences 30(3):291\u2013319. https:\/\/doi.org\/10.1016\/j.jksuci.2016.10.003","journal-title":"Journal of King Saud University-Computer and Information Sciences"},{"key":"6876_CR63","unstructured":"Montgomery DC (2021) Introduction to linear regression analysis. John Wiley & Sons"},{"issue":"11","key":"6876_CR64","doi-asserted-by":"publisher","first-page":"3758","DOI":"10.3390\/s21113758","volume":"21","author":"L Benos","year":"2021","unstructured":"Benos L (2021) Machine learning in agriculture: a comprehensive updated review. Sensors 21(11):3758. https:\/\/doi.org\/10.3390\/s21113758","journal-title":"Sensors"},{"key":"6876_CR65","doi-asserted-by":"crossref","unstructured":"Kucuk C, Birant D, Yildirim Taser P (2022) An intelligent multi-output regression model for soil moisture prediction. In: INFUS 2021 Conference","DOI":"10.1007\/978-3-030-85577-2_56"},{"key":"6876_CR66","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3394617","author":"A Naseer","year":"2024","unstructured":"Naseer A (2024) A systematic literature review of the IoT in agriculture: global adoption, innovations, security and privacy challenges. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2024.3394617","journal-title":"IEEE Access"},{"key":"6876_CR67","doi-asserted-by":"publisher","unstructured":"Choudhary V (2025) An overview of smart agriculture using internet of things (IoT) and web services. Environ Sustain Indic. https:\/\/doi.org\/10.1016\/j.indic.2025.100607","DOI":"10.1016\/j.indic.2025.100607"},{"key":"6876_CR68","doi-asserted-by":"crossref","unstructured":"Oliveira RC, Silva RDS (2023) Artificial intelligence in agriculture: benefits, challenges, and trends. Appl Sci","DOI":"10.3390\/app13137405"},{"key":"6876_CR69","doi-asserted-by":"publisher","unstructured":"Ray R (2018) Applications of machine learning in agriculture. Agric Syst. https:\/\/doi.org\/10.4018\/978-1-6684-6418-2.ch006","DOI":"10.4018\/978-1-6684-6418-2.ch006"},{"key":"6876_CR70","doi-asserted-by":"publisher","first-page":"129551","DOI":"10.1109\/ACCESS.2019.2932609","volume":"7","author":"M Ayaz","year":"2019","unstructured":"Ayaz M (2019) Internet-of-Things (IoT)-based smart agriculture: toward making the fields talk. IEEE Access 7:129551","journal-title":"IEEE Access"},{"issue":"1","key":"6876_CR71","doi-asserted-by":"publisher","first-page":"57","DOI":"10.13031\/trans.13355","volume":"63","author":"SR Evett","year":"2020","unstructured":"Evett SR (2020) Precision agriculture and irrigation: current US perspectives. Trans ASABE 63(1):57","journal-title":"Trans ASABE"},{"key":"6876_CR72","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2024.3451412","author":"Z Xia","year":"2024","unstructured":"Xia Z, Hu Z, He Q, Wang C (2024) Real-time transfer active learning for functional regression and prediction based on multi-output Gaussian process. IEEE Trans Signal Process. https:\/\/doi.org\/10.1109\/TSP.2024.3451412","journal-title":"IEEE Trans Signal Process"},{"issue":"2","key":"6876_CR73","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1109\/TSIPN.2018.2885925","volume":"5","author":"L Yang","year":"2018","unstructured":"Yang L, Wang K, Mihaylova L (2018) Online sparse multi-output Gaussian process regression and learning. IEEE Trans Signal Inform Process Over Networks 5(2):258\u2013272","journal-title":"IEEE Trans Signal Inform Process Over Networks"},{"key":"6876_CR74","doi-asserted-by":"crossref","unstructured":"Alvarez MA, Rosasco L, Lawrence ND (2012) Kernels for vector-valued functions: a review. Found Trends\u00ae Mach Learn 4(3):195\u2013266","DOI":"10.1561\/2200000036"},{"key":"6876_CR75","doi-asserted-by":"publisher","first-page":"17760","DOI":"10.1109\/ACCESS.2024.3359115","volume":"12","author":"S Emami","year":"2024","unstructured":"Emami S, Mart\u00ednez-Mu\u00f1oz G (2024) Deep learning for multi-output regression using gradient boosting. IEEE Access 12:17760\u201317772","journal-title":"IEEE Access"},{"issue":"7","key":"6876_CR76","first-page":"2409","volume":"31","author":"D Xu","year":"2019","unstructured":"Xu D, Shi Y, Tsang IW, Ong Y-S, Gong C, Shen X (2019) Survey on multi-output learning. IEEE Trans Neural Networks Learn Syst 31(7):2409\u20132429","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"5","key":"6876_CR77","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1002\/widm.1157","volume":"5","author":"H Borchani","year":"2015","unstructured":"Borchani H, Varando G, Bielza C, Larranaga P (2015) A survey on multi-output regression. Wiley Interdiscip Rev Data Min Knowl Discov 5(5):216\u2013233","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"issue":"10","key":"6876_CR78","doi-asserted-by":"publisher","first-page":"6232","DOI":"10.1109\/TSMC.2023.3281973","volume":"53","author":"H Liu","year":"2023","unstructured":"Liu H, Wu K, Ong Y-S, Bian C, Jiang X, Wang X (2023) Learning multitask Gaussian process over heterogeneous input domains. IEEE Trans Syst Man Cybernetics: Syst 53(10):6232\u20136244","journal-title":"IEEE Trans Syst Man Cybernetics: Syst"},{"issue":"1","key":"6876_CR79","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1186\/s12911-020-1069-4","volume":"20","author":"L-F Cheng","year":"2020","unstructured":"Cheng L-F, Dumitrascu B, Darnell G, Chivers C, Draugelis M, Li K, Engelhardt BE (2020) Sparse multi-output Gaussian processes for online medical time series prediction. BMC Med Inform Decis Mak 20(1):152","journal-title":"BMC Med Inform Decis Mak"},{"key":"6876_CR80","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.jclepro.2018.10.243","volume":"209","author":"Y Zhou","year":"2019","unstructured":"Zhou Y, Chang F-J, Chang L-C, Kao I-F, Wang Y-S (2019) Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J Clean Prod 209:134\u2013145","journal-title":"J Clean Prod"},{"issue":"3","key":"6876_CR81","doi-asserted-by":"publisher","first-page":"7914","DOI":"10.1007\/s11356-022-22588-7","volume":"30","author":"X Zhang","year":"2023","unstructured":"Zhang X, Li D (2023) Multi-input multi-output temporal convolutional network for predicting the long-term water quality of ocean ranches. Environ Sci Pollut Res 30(3):7914\u20137929","journal-title":"Environ Sci Pollut Res"},{"key":"6876_CR82","doi-asserted-by":"crossref","unstructured":"Samal KKR, Babu KS, Das SK (2021) Time series forecasting of air pollution using deep neural network with multi-output learning. In: 2021 IEEE 18th India Council International Conference (INDICON). IEEE, pp 1\u20135","DOI":"10.1109\/INDICON52576.2021.9691552"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06876-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06876-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06876-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T09:07:34Z","timestamp":1764148054000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06876-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":82,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["6876"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06876-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"19 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study does not involve human participants, animals, or identifiable personal data. Therefore, ethical approval and informed consent were not required. The dataset used (LH_Data) consists solely of anonymized environmental sensor measurements collected as part of a smart agriculture pilot initiative, with no human subject involvement.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Ethical Approval and Informed Consent"}},{"value":"The authors declare no competing interests.The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1081"}}