{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T05:56:44Z","timestamp":1772863004328,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"32","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-025-20747-9","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T04:49:16Z","timestamp":1742273356000},"page":"39787-39811","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["M-Bi-GRU-CNN: a hybrid deep learning model with optimized feature selection for enhanced crop yield prediction"],"prefix":"10.1007","volume":"84","author":[{"given":"Madhuri","family":"J","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Indiramma","family":"M","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nagarathna","family":"N","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"20747_CR1","doi-asserted-by":"publisher","first-page":"105709","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. Comput Electron Agric 177:105709","journal-title":"Comput Electron Agric"},{"key":"20747_CR2","doi-asserted-by":"publisher","first-page":"86886","DOI":"10.1109\/ACCESS.2020.2992480","volume":"8","author":"D Elavarasan","year":"2020","unstructured":"Elavarasan D, Vincent PD (2020) Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE access 8:86886\u201386901","journal-title":"IEEE access"},{"key":"20747_CR3","doi-asserted-by":"crossref","unstructured":"Nishant PS, Venkat PS, Avinash BL, Jabber B (2020) Crop yield prediction based on Indian agriculture using machine learning. In2020 International Conference for Emerging Technology (INCET) 1\u20134. IEEE.","DOI":"10.1109\/INCET49848.2020.9154036"},{"key":"20747_CR4","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.compag.2018.05.012","volume":"151","author":"A Chlingaryan","year":"2018","unstructured":"Chlingaryan A, Sukkarieh S, Whelan B (2018) Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Comput Electron Agric 151:61\u201369","journal-title":"Comput Electron Agric"},{"issue":"7","key":"20747_CR5","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.3390\/agronomy10071046","volume":"10","author":"F Abbas","year":"2020","unstructured":"Abbas F, Afzaal H, Farooque AA, Tang S (2020) Crop yield prediction through proximal sensing and machine learning algorithms. Agronomy 10(7):1046","journal-title":"Agronomy"},{"key":"20747_CR6","doi-asserted-by":"publisher","first-page":"63406","DOI":"10.1109\/ACCESS.2021.3075159","volume":"9","author":"M Rashid","year":"2021","unstructured":"Rashid M, Bari BS, Yusup Y, Kamaruddin MA, Khan N (2021) A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction. IEEE Access 9:63406\u201363439","journal-title":"IEEE Access"},{"issue":"9","key":"20747_CR7","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 NH, 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"},{"issue":"10","key":"20747_CR8","doi-asserted-by":"publisher","first-page":"2460","DOI":"10.3390\/agronomy12102460","volume":"12","author":"A Ny\u00e9ki","year":"2022","unstructured":"Ny\u00e9ki A, Nem\u00e9nyi M (2022) Crop Yield Prediction in Precision Agriculture. Agronomy 12(10):2460","journal-title":"Agronomy"},{"key":"20747_CR9","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.3389\/fpls.2019.01750","volume":"10","author":"S Khaki","year":"2020","unstructured":"Khaki S, Wang L, Archontoulis SV (2020) a cnn-rnn framework for crop yield prediction. Front Plant Sci 10:1750","journal-title":"Front Plant Sci"},{"issue":"20","key":"20747_CR10","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":"20747_CR11","doi-asserted-by":"crossref","unstructured":"Kumar YJ, Spandana V, Vaishnavi VS, Neha K, Devi VG (2020) Supervised machine learning approach for crop yield prediction in agriculture sector. In2020 5th International Conference on Communication and Electronics Systems (ICCES) 736\u2013741. IEEE","DOI":"10.1109\/ICCES48766.2020.9137868"},{"issue":"5","key":"20747_CR12","first-page":"371","volume":"4","author":"DA Bondre","year":"2019","unstructured":"Bondre DA, Mahagaonkar S (2019) Prediction of crop yield and fertilizer recommendation using machine learning algorithms. Int J Eng Appl Sci Technol 4(5):371\u2013376","journal-title":"Int J Eng Appl Sci Technol"},{"issue":"9","key":"20747_CR13","doi-asserted-by":"publisher","first-page":"400","DOI":"10.3390\/agriculture10090400","volume":"10","author":"D Elavarasan","year":"2020","unstructured":"Elavarasan D, Vincent PMDR, Srinivasan K, Chang CY (2020) a hybrid CFS filter and RF-RFE wrapper-based feature extraction for enhanced agricultural crop yield prediction modeling. Agriculture 10(9):400","journal-title":"Agriculture"},{"key":"20747_CR14","doi-asserted-by":"crossref","unstructured":"Bhanumathi S, Vineeth M, Rohit N (2019) Crop yield prediction and efficient use of fertilizers. In2019 International Conference on Communication and Signal Processing (ICCSP) 0769\u20130773. IEEE.","DOI":"10.1109\/ICCSP.2019.8698087"},{"issue":"12","key":"20747_CR15","first-page":"1460","volume":"5","author":"B Devika","year":"2018","unstructured":"Devika B, Ananthi B (2018) Analysis of crop yield prediction using data mining technique to predict annual yield of major crops. Int Res J Eng Technol 5(12):1460\u20131465","journal-title":"Int Res J Eng Technol"},{"key":"20747_CR16","doi-asserted-by":"publisher","first-page":"621","DOI":"10.3389\/fpls.2019.00621","volume":"10","author":"S Khaki","year":"2019","unstructured":"Khaki S, Wang L (2019) Crop yield prediction using deep neural networks. Front Plant Sci 10:621","journal-title":"Front Plant Sci"},{"issue":"1","key":"20747_CR17","first-page":"012012","volume":"1714","author":"S Agarwal","year":"2021","unstructured":"Agarwal S, Tarar S (2021) a hybrid approach for crop yield prediction using machine learning and deep learning algorithms. InJ Phys: Conf Series 1714(1):012012 (IOP Publishing)","journal-title":"InJ Phys: Conf Series"},{"key":"20747_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2020.106649","volume":"245","author":"J Yu","year":"2021","unstructured":"Yu J, Zhang X, Xu L, Dong J, Zhangzhong L (2021) A hybrid CNN-GRU model for predicting soil moisture in maize root zone. Agric Water Manag 245:106649","journal-title":"Agric Water Manag"},{"issue":"4","key":"20747_CR19","doi-asserted-by":"publisher","first-page":"1976","DOI":"10.3390\/s23041976","volume":"23","author":"SH Park","year":"2023","unstructured":"Park SH, Lee BY, Kim MJ, Sang W, Seo MC, Baek JK, Yang JE, Mo C (2023) Development of a soil moisture prediction model based on recurrent neural network long short-term memory (RNN-LSTM) in soybean cultivation. Sensors 23(4):1976","journal-title":"Sensors"},{"issue":"4","key":"20747_CR20","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1080\/15427528.2019.1610534","volume":"33","author":"JO Ajetomobi","year":"2019","unstructured":"Ajetomobi JO, Olaleye AO (2019) Auto-regressive integrated moving average (ARIMA) modeling of cocoa production in Nigeria: 1900\u20132025. J Crop Improv 33(4):445\u2013455","journal-title":"J Crop Improv"},{"issue":"15","key":"20747_CR21","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.3390\/plants11151925","volume":"11","author":"D Batool","year":"2022","unstructured":"Batool D, Shahbaz M, ShahzadAsif H, Shaukat K, Alam TM, Hameed IA, Ramzan Z, Waheed A, Aljuaid H, Luo S (2022) A hybrid approach to tea crop yield prediction using simulation models and machine learning. Plants 11(15):1925","journal-title":"Plants"},{"key":"20747_CR22","doi-asserted-by":"publisher","first-page":"108377","DOI":"10.1016\/j.fcr.2021.108377","volume":"276","author":"D Paudel","year":"2022","unstructured":"Paudel D, Boogaard H, de Wit A, van der Velde M, Claverie M, Nisini L, Janssen S, Osinga S, Athanasiadis IN (2022) Machine learning for regional crop yield forecasting in Europe. Field Crop Res 276:108377","journal-title":"Field Crop Res"},{"issue":"1","key":"20747_CR23","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 (2022) Hybrid deep learning-based models for crop yield prediction. Appl Artif Intell 36(1):2031822","journal-title":"Appl Artif Intell"},{"issue":"3","key":"20747_CR24","doi-asserted-by":"publisher","first-page":"719","DOI":"10.3390\/s22030719","volume":"22","author":"HT Pham","year":"2022","unstructured":"Pham HT, Awange J, Kuhn M, Nguyen BV, Bui LK (2022) Enhancing crop yield prediction utilizing machine learning on satellite-based vegetation health indices. Sensors 22(3):719","journal-title":"Sensors"},{"issue":"6","key":"20747_CR25","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.3390\/rs14061474","volume":"14","author":"C Bian","year":"2022","unstructured":"Bian C, Shi H, Wu S, Zhang K, Wei M, Zhao Y, Sun Y, Zhuang H, Zhang X, Chen S (2022) Prediction of field-scale wheat yield using machine learning method and multi-spectral UAV data. Remote Sensing 14(6):1474","journal-title":"Remote Sensing"},{"key":"20747_CR26","doi-asserted-by":"publisher","first-page":"64671","DOI":"10.1109\/ACCESS.2022.3181970","volume":"10","author":"HR Seireg","year":"2022","unstructured":"Seireg HR, Omar YM, Abd El-Samie FE, El-Fishawy AS, Elmahalawy A (2022) Ensemble machine learning techniques using computer simulation data for wild blueberry yield prediction. IEEE Access 10:64671\u201364687","journal-title":"IEEE Access"},{"issue":"1","key":"20747_CR27","doi-asserted-by":"publisher","first-page":"5488","DOI":"10.1038\/s41598-022-09482-5","volume":"12","author":"M Ali","year":"2022","unstructured":"Ali M, Deo RC, Xiang Y, Prasad R, Li J, Farooque A, Yaseen ZM (2022) Coupled online sequential extreme learning machine model with ant colony optimization algorithm for wheat yield prediction. Sci Rep 12(1):5488","journal-title":"Sci Rep"},{"key":"20747_CR28","doi-asserted-by":"publisher","first-page":"107705","DOI":"10.1016\/j.compag.2023.107705","volume":"206","author":"J Wang","year":"2023","unstructured":"Wang J, Wang P, Tian H, Tansey K, Liu J, Quan W (2023) A deep learning framework combining CNN and GRU for improving wheat yield estimates using time series remotely sensed multi-variables. Comput Electron Agric 206:107705","journal-title":"Comput Electron Agric"},{"issue":"23","key":"20747_CR29","doi-asserted-by":"publisher","first-page":"15522","DOI":"10.3390\/su142315522","volume":"14","author":"D Liu","year":"2022","unstructured":"Liu D, Tang Z, Cai Y (2022) A hybrid model for China\u2019s soybean spot price prediction by integrating CEEMDAN with fuzzy entropy clustering and CNN-GRU-attention. Sustainability 14(23):15522","journal-title":"Sustainability"},{"issue":"1","key":"20747_CR30","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3390\/computers12010010","volume":"12","author":"U Bhimavarapu","year":"2023","unstructured":"Bhimavarapu U, Battineni G, Chintalapudi N (2023) Improved optimization algorithm in LSTM to predict crop yield. Computers 12(1):10","journal-title":"Computers"},{"key":"20747_CR31","doi-asserted-by":"crossref","unstructured":"Gajera V, Gupta R, Jana PK (2016) an effective multi-objective task scheduling algorithm using min-max normalization in cloud computing. In: 2016 2nd International conference on applied and theoretical computing and communication technology (iCATccT).\u00a0IEEE, pp 812\u2013816","DOI":"10.1109\/ICATCCT.2016.7912111"},{"key":"20747_CR32","doi-asserted-by":"publisher","first-page":"116158","DOI":"10.1016\/j.eswa.2021.116158","volume":"191","author":"L Abualigah","year":"2022","unstructured":"Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH (2022) Reptile Search Algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158","journal-title":"Expert Syst Appl"},{"key":"20747_CR33","doi-asserted-by":"crossref","unstructured":"Liu J, Yang Y, Lv S, Wang J, Chen H (2019) Attention-based BiGRU-CNN for Chinese question classification.\u00a0J Ambient Intell Humaniz Comput 1\u201312","DOI":"10.1007\/s12652-019-01344-9"},{"key":"20747_CR34","doi-asserted-by":"publisher","first-page":"102282","DOI":"10.1016\/j.artmed.2022.102282","volume":"127","author":"Y An","year":"2022","unstructured":"An Y, Xia X, Chen X, Wu FX, Wang J (2022) Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF. Artif Intell Med 127:102282","journal-title":"Artif Intell Med"},{"key":"20747_CR35","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354\u2013377","journal-title":"Pattern Recogn"},{"issue":"10","key":"20747_CR36","doi-asserted-by":"publisher","first-page":"28969","DOI":"10.1007\/s11042-023-16612-2","volume":"83","author":"VRR Kolipaka","year":"2024","unstructured":"Kolipaka VRR, Namburu A (2024) An automatic crop yield prediction framework designed with two-stage classifiers: a meta-heuristic approach. Multimed Tools Appl 83(10):28969\u201328992","journal-title":"Multimed Tools Appl"},{"issue":"7","key":"20747_CR37","doi-asserted-by":"publisher","first-page":"19161","DOI":"10.1007\/s11042-023-16235-7","volume":"83","author":"BS Devi","year":"2024","unstructured":"Devi BS, Sandhya N, Chatrapati KS (2024) Hybrid deep WaveNet-LSTM architecture for crop yield prediction. Multimed Tools Appl 83(7):19161\u201319179","journal-title":"Multimed Tools Appl"},{"key":"20747_CR38","doi-asserted-by":"publisher","first-page":"106412","DOI":"10.1016\/j.ecolind.2020.106412","volume":"115","author":"D Sarkar","year":"2020","unstructured":"Sarkar D, Kar SK, Chattopadhyay A, Rakshit A, Tripathi VK, Dubey PK, Abhilash PC (2020) Low input sustainable agriculture: A viable climate-smart option for boosting food production in a warming world. Ecol Ind 115:106412","journal-title":"Ecol Ind"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20747-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-025-20747-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20747-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T15:12:31Z","timestamp":1758899551000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-025-20747-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,18]]},"references-count":38,"journal-issue":{"issue":"32","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["20747"],"URL":"https:\/\/doi.org\/10.1007\/s11042-025-20747-9","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,18]]},"assertion":[{"value":"18 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All the authors involved have agreed to participate in this submitted article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All the authors involved in this manuscript give full consent for publication of this submitted article.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"Authors declare that they have no conflict of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}