{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T15:32:44Z","timestamp":1779895964312,"version":"3.53.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T00:00:00Z","timestamp":1705881600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T00:00:00Z","timestamp":1705881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100020202","name":"UCD Foundation","doi-asserted-by":"publisher","award":["16\/SPP\/3296"],"award-info":[{"award-number":["16\/SPP\/3296"]}],"id":[{"id":"10.13039\/100020202","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s10994-023-06455-1","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T13:03:02Z","timestamp":1705928582000},"page":"3771-3788","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A neural meta model for predicting winter wheat crop yield"],"prefix":"10.1007","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0756-1455","authenticated-orcid":false,"given":"Yogesh","family":"Bansal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5702-4463","authenticated-orcid":false,"given":"David","family":"Lillis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0176-6281","authenticated-orcid":false,"given":"M.-Tahar","family":"Kechadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"issue":"15","key":"6455_CR1","doi-asserted-by":"publisher","first-page":"1304","DOI":"10.1080\/08839514.2021.1976091","volume":"35","author":"N Bali","year":"2021","unstructured":"Bali, N., & Singla, A. (2021). Deep learning based wheat crop yield prediction model in punjab region of north india. Applied Artificial Intelligence, 35(15), 1304\u20131328.","journal-title":"Applied Artificial Intelligence"},{"issue":"1","key":"6455_CR2","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s11831-021-09569-8","volume":"29","author":"N Bali","year":"2022","unstructured":"Bali, N., & Singla, A. (2022). Emerging trends in machine learning to predict crop yield and study its influential factors: A survey. Archives of Computational Methods in Engineering, 29(1), 95\u2013112.","journal-title":"Archives of Computational Methods in Engineering"},{"key":"6455_CR4","doi-asserted-by":"crossref","unstructured":"Bansal, Y., Lillis, D., & Kechadi, M.-T.  (2023) A deep learning model for heterogeneous dataset analysis - application to winter wheat crop yield prediction. In J. Abawajy, J. Tavares, L. Kharb, D. Chahal, & A. B. Nassif (Eds.), Information, communication and computing technology (pp. 182\u2013194). Springer Nature Switzerland.","DOI":"10.1007\/978-3-031-43838-7_14"},{"key":"6455_CR3","doi-asserted-by":"publisher","unstructured":"Bansal, Y., Lillis, D., Kechadi, & Tahar, M. (2022). Winter wheat crop yield prediction on multiple heterogeneous datasets using machine learning. In 2022 international conference on computational science and computational intelligence (CSCI) (pp. 206\u2013212). https:\/\/doi.org\/10.1109\/CSCI58124.2022.00040","DOI":"10.1109\/CSCI58124.2022.00040"},{"issue":"1","key":"6455_CR5","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"G Chandrashekar","year":"2014","unstructured":"Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16\u201328.","journal-title":"Computers & Electrical Engineering"},{"issue":"1","key":"6455_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-022-00668-2","volume":"9","author":"N Chergui","year":"2022","unstructured":"Chergui, N., & Kechadi, M. T. (2022). Data analytics for crop management: a big data view. Journal of Big Data, 9(1), 1\u201337.","journal-title":"Journal of Big Data"},{"issue":"2","key":"6455_CR7","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1080\/07038992.2020.1833186","volume":"47","author":"C Dang","year":"2021","unstructured":"Dang, C., Liu, Y., Yue, H., Qian, J. X., & Zhu, R. (2021). Autumn crop yield prediction using data-driven approaches:-support vector machines, random forest, and deep neural network methods. Canadian Journal of Remote Sensing, 47(2), 162\u2013181.","journal-title":"Canadian Journal of Remote Sensing"},{"key":"6455_CR8","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.compag.2018.10.024","volume":"155","author":"D Elavarasan","year":"2018","unstructured":"Elavarasan, D., Vincent, D. R., Sharma, V., Zomaya, A. Y., & Srinivasan, K. (2018). Forecasting yield by integrating agrarian factors and machine learning models: A survey. Computers and Electronics in Agriculture, 155, 257\u2013282.","journal-title":"Computers and Electronics in Agriculture"},{"key":"6455_CR9","doi-asserted-by":"publisher","first-page":"103326","DOI":"10.1016\/j.advengsoft.2022.103326","volume":"175","author":"S Iniyan","year":"2023","unstructured":"Iniyan, S., Varma, V. A., & Naidu, C. T. (2023). Crop yield prediction using machine learning techniques. Advances in Engineering Software, 175, 103326.","journal-title":"Advances in Engineering Software"},{"key":"6455_CR10","doi-asserted-by":"crossref","unstructured":"Saeed Khaki, Lizhi Wang, and Sotirios V Archontoulis. A cnn-rnn framework for crop yield prediction. Frontiers in Plant Science, 10:1750, 2020.","DOI":"10.3389\/fpls.2019.01750"},{"key":"6455_CR11","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.agrformet.2018.01.031","volume":"253","author":"JA Mathieu","year":"2018","unstructured":"Mathieu, J. A., & Aires, F. (2018). Assessment of the agro-climatic indices to improve crop yield forecasting. Agricultural and Forest Meteorology, 253, 15\u201330.","journal-title":"Agricultural and Forest Meteorology"},{"issue":"9","key":"6455_CR12","doi-asserted-by":"publisher","first-page":"1990","DOI":"10.3390\/rs14091990","volume":"14","author":"P Muruganantham","year":"2022","unstructured":"Muruganantham, P., Wibowo, S., Grandhi, S., Samrat, N. H., & Islam, N. (2022). A systematic literature review on crop yield prediction with deep learning and remote sensing. Remote Sensing, 14(9), 1990.","journal-title":"Remote Sensing"},{"key":"6455_CR13","doi-asserted-by":"crossref","unstructured":"Oikonomidis, A., Catal, C., & Kassahun. A. (2022) Deep learning for crop yield prediction: a systematic literature review. New Zealand Journal of Crop and Horticultural Science (pp. 1\u201326).","DOI":"10.1080\/01140671.2022.2032213"},{"issue":"1","key":"6455_CR14","doi-asserted-by":"publisher","first-page":"2031822","DOI":"10.1080\/08839514.2022.2031823","volume":"36","author":"A Oikonomidis","year":"2022","unstructured":"Oikonomidis, A., Catal, C., & Kassahun, A. (2022b). Hybrid deep learning-based models for crop yield prediction. Applied Artificial Intelligence, 36(1), 2031822.","journal-title":"Applied Artificial Intelligence"},{"key":"6455_CR15","doi-asserted-by":"crossref","unstructured":"Pravallika, K,. Karuna, G., Anuradha, K., Srilakshmi, V. (2021). Deep neural network model for proficient crop yield prediction. In E3S web of conferences (Vol. 309, p. 01031. EDP Sciences).","DOI":"10.1051\/e3sconf\/202130901031"},{"key":"6455_CR16","first-page":"256","volume":"58","author":"M Sarith Divakar","year":"2022","unstructured":"Sarith Divakar, M., Sudheep Elayidom, M., & Rajesh, R. (2022). Forecasting crop yield with deep learning based ensemble model. Materials Today: Proceedings, 58, 256\u2013259.","journal-title":"Materials Today: Proceedings"},{"key":"6455_CR17","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.3389\/fpls.2020.01120","volume":"11","author":"M Shahhosseini","year":"2020","unstructured":"Shahhosseini, M., Hu, G., & Archontoulis, S. V. (2020). Forecasting corn yield with machine learning ensembles. Frontiers in Plant Science, 11, 1120.","journal-title":"Frontiers in Plant Science"},{"key":"6455_CR18","doi-asserted-by":"publisher","first-page":"709008","DOI":"10.3389\/fpls.2021.709008","volume":"12","author":"M Shahhosseini","year":"2021","unstructured":"Shahhosseini, M., Hu, G., Khaki, S., & Archontoulis, S. V. (2021). Corn yield prediction with ensemble cnn-dnn. Frontiers in Plant Science, 12, 709008.","journal-title":"Frontiers in Plant Science"},{"key":"6455_CR19","unstructured":"Sharma, S., Rai, S. & Krishnan, N. C. (2020). Wheat crop yield prediction using deep lstm model. arXiv preprint arXiv:2011.01498."},{"issue":"6","key":"6455_CR20","doi-asserted-by":"publisher","first-page":"892","DOI":"10.3390\/agriculture12060892","volume":"12","author":"Y Shen","year":"2022","unstructured":"Shen, Y., Mercatoris, Beno\u02c6\u0131t, Cao, Z., Kwan, P., Guo, L., Yao, H., & Cheng, Q. (2022). Improving wheat yield prediction accuracy using lstm-rf framework based on uav thermal infrared and multispectral imagery. Agriculture, 12(6), 892.","journal-title":"Agriculture"},{"key":"6455_CR21","doi-asserted-by":"crossref","unstructured":"Srivastava, A. K., Safaei, N., Khaki, S., Lopez, G., Zeng, W., Ewert, F., Gaiser, T., & Rahimi. J. (2022) Winter wheat yield prediction using convolutional neural networks from environmental and phenological data. Scientific Reports, 12(1):3215","DOI":"10.1038\/s41598-022-06249-w"},{"issue":"20","key":"6455_CR22","doi-asserted-by":"publisher","first-page":"4363","DOI":"10.3390\/s19204363","volume":"19","author":"J Sun","year":"2019","unstructured":"Sun, J., Di, L., Sun, Z., Shen, Y., & Lai, Z. (2019). County-level soybean yield prediction using deep cnn-lstm model. Sensors, 19(20), 4363.","journal-title":"Sensors"},{"key":"6455_CR23","doi-asserted-by":"crossref","unstructured":"Toomula, S., & Pelluri. S. (2022) An extensive survey of deep learning-based crop yield prediction models for precision agriculture. In Proceedings of the international conference on cognitive and intelligent computing: ICCIC 2021 (Vol. 1, pp. 1\u201312. Springer).","DOI":"10.1007\/978-981-19-2350-0_1"},{"key":"6455_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105709","volume":"177","author":"T Van Klompenburg","year":"2020","unstructured":"Van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709.","journal-title":"Computers and Electronics in Agriculture"},{"issue":"11","key":"6455_CR25","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.3390\/rs12111744","volume":"12","author":"X Wang","year":"2020","unstructured":"Wang, X., Huang, J., Feng, Q., & Yin, D. (2020). Winter wheat yield prediction at county level and uncertainty analysis in main wheat-producing regions of china with deep learning approaches. Remote Sensing, 12(11), 1744.","journal-title":"Remote Sensing"},{"issue":"17","key":"6455_CR26","doi-asserted-by":"publisher","first-page":"8654","DOI":"10.3390\/app12178654","volume":"12","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., Liu, J., & Shen, W. (2022b). A review of ensemble learning algorithms used in remote sensing applications. Applied Sciences, 12(17), 8654.","journal-title":"Applied Sciences"},{"issue":"5","key":"6455_CR27","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1177\/03091333221088018","volume":"46","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., Wang, L., Chen, X., Liu, Y., Wang, S., & Wang, L. (2022a). Prediction of winter wheat yield at county level in china using ensemble learning. Progress in Physical Geography: Earth and Environment, 46(5), 676\u2013696.","journal-title":"Progress in Physical Geography: Earth and Environment"}],"updated-by":[{"DOI":"10.1007\/s10994-024-06535-w","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000}}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06455-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-023-06455-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06455-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T18:11:35Z","timestamp":1764267095000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-023-06455-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,22]]},"references-count":27,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["6455"],"URL":"https:\/\/doi.org\/10.1007\/s10994-023-06455-1","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,22]]},"assertion":[{"value":"12 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2024","order":6,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":7,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s10994-024-06535-w","URL":"https:\/\/doi.org\/10.1007\/s10994-024-06535-w","order":9,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There are no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Yes, we will participate","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors give consent for publication of research findings.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}