{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T06:21:07Z","timestamp":1776061267979,"version":"3.50.1"},"reference-count":77,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFD2000102"],"award-info":[{"award-number":["2021YFD2000102"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100022963","name":"Key Research and Development Program of Zhejiang Province","doi-asserted-by":"publisher","award":["2022C02013"],"award-info":[{"award-number":["2022C02013"]}],"id":[{"id":"10.13039\/100022963","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.compag.2026.111495","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T02:22:23Z","timestamp":1770344543000},"page":"111495","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Integrating remote sensing data assimilation, deep learning and large language model to interactive yield prediction for wheat breeding"],"prefix":"10.1016","volume":"244","author":[{"given":"Guofeng","family":"Yang","sequence":"first","affiliation":[]},{"given":"Nanfei","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Wenjie","family":"Ai","sequence":"additional","affiliation":[]},{"given":"Zhonghua","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Yuhong","family":"He","sequence":"additional","affiliation":[]},{"given":"Yong","family":"He","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2026.111495_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2021.108773","article-title":"Bayesian multi-modeling of deep neural nets for probabilistic crop yield prediction","author":"Abbaszadeh","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.111495_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.105899","article-title":"Smart farming using artificial intelligence: a review","volume":"120","author":"Akkem","year":"2023","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.compag.2026.111495_b0015","unstructured":"Allard, W., Hendrik, B., 2024. A gentle introduction to WOFOST [WWW Document]. URL https:\/\/backend.wur.nl\/sites\/default\/files\/2025-10\/Gentle-WOFOST-2024.pdf."},{"key":"10.1016\/j.compag.2026.111495_b0020","article-title":"Political-RAG: using generative AI to extract political information from media content","author":"Arslan","year":"2024","journal-title":"J. Inf. Technol. Polit."},{"key":"10.1016\/j.compag.2026.111495_b0025","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"10.1016\/j.compag.2026.111495_b0030","series-title":"Language models are few-shot learners","first-page":"1877","author":"Brown","year":"2020"},{"key":"10.1016\/j.compag.2026.111495_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.fcr.2025.109745","article-title":"Trends in crop yield estimation via data assimilation based on multi-interdisciplinary analysis","author":"Cao","year":"2025","journal-title":"FIELD CROPS Res."},{"key":"10.1016\/j.compag.2026.111495_b0045","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C., 2016. XGBoost: a scalable tree boosting system, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201916. Association for Computing Machinery, New York, NY, USA, pp. 785\u2013794. doi: 10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"key":"10.1016\/j.compag.2026.111495_b0040","series-title":"Findings of the Association for Computational Linguistics: ACL 2024. Presented at the Findings 2024","first-page":"2318","article-title":"M3-embedding: multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation","author":"Chen","year":"2024"},{"key":"10.1016\/j.compag.2026.111495_b0050","series-title":"Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation. Presented at the SSST 2014","first-page":"103","article-title":"On the properties of neural machine translation: encoder\u2013decoder approaches","author":"Cho","year":"2014"},{"key":"10.1016\/j.compag.2026.111495_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.agsy.2023.103749","article-title":"Matching the model to the available data to predict wheat, barley, or canola yield: a review of recently published models and data","volume":"211","author":"Clark","year":"2023","journal-title":"Agric. Syst."},{"key":"10.1016\/j.compag.2026.111495_b0060","doi-asserted-by":"crossref","first-page":"4066","DOI":"10.3390\/rs15164066","article-title":"A global systematic review of improving crop model estimations by assimilating remote sensing data: implications for small-scale agricultural systems","volume":"15","author":"Dlamini","year":"2023","journal-title":"Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0065","series-title":"Support vector regression machines","first-page":"155","author":"Drucker","year":"1996"},{"key":"10.1016\/j.compag.2026.111495_b0070","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1029\/2018RG000608","article-title":"An overview of global leaf area index (LAI): methods, products, validation, and applications","volume":"57","author":"Fang","year":"2019","journal-title":"Rev. Geophys."},{"key":"10.1016\/j.compag.2026.111495_b0075","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1186\/s13007-022-00949-0","article-title":"Combining novel feature selection strategy and hyperspectral vegetation indices to predict crop yield","volume":"18","author":"Fei","year":"2022","journal-title":"Plant Methods"},{"key":"10.1016\/j.compag.2026.111495_b0080","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1038\/s41540-024-00343-7","article-title":"Crop-GPA: an integrated platform of crop gene-phenotype associations","volume":"10","author":"Gao","year":"2024","journal-title":"Npj Syst. Biol. Appl."},{"key":"10.1016\/j.compag.2026.111495_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2024.110022","article-title":"Beyond assimilation of leaf area index: leveraging additional spectral information using machine learning for site-specific soybean yield prediction","volume":"351","author":"Gaso","year":"2024","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.111495_b0090","doi-asserted-by":"crossref","first-page":"e503","DOI":"10.1002\/fes3.503","article-title":"Crop models and their use in assessing crop production and food security: a review","volume":"13","author":"Gavasso-Rita","year":"2024","journal-title":"Food Energy Secur"},{"key":"10.1016\/j.compag.2026.111495_b0095","doi-asserted-by":"crossref","DOI":"10.1097\/HEP.0000000000000834","article-title":"Development of a liver disease-specific large language model chat interface using retrieval augmented generation","author":"Ge","year":"2024","journal-title":"Hepatology"},{"key":"10.1016\/j.compag.2026.111495_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109111","article-title":"A novel transformer-based neural network under model interpretability for improving wheat yield estimation using remotely sensed multi-variables","volume":"223","author":"Guo","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111495_b0105","doi-asserted-by":"crossref","first-page":"377","DOI":"10.3390\/agronomy12020377","article-title":"Competitiveness of early vigour wheat (triticum aestivum L.) genotypes is established at early growth stages","volume":"12","author":"Hendriks","year":"2022","journal-title":"Agron.-Basel"},{"key":"10.1016\/j.compag.2026.111495_b0220","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.compag.2026.111495_b0110","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.tplants.2022.12.014","article-title":"Weed-induced crop yield loss: a new paradigm and new challenges","volume":"28","author":"Horvath","year":"2023","journal-title":"Trends Plant Sci."},{"key":"10.1016\/j.compag.2026.111495_b0115","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.3390\/rs15082014","article-title":"Remote-sensing data and deep-learning techniques in crop mapping and yield prediction: a systematic review","volume":"15","author":"Joshi","year":"2023","journal-title":"Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0120","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1080\/22797254.2020.1839359","article-title":"Evaluation of sentinel-2 vegetation indices for prediction of LAI, fAPAR and fCover of winter wheat in bulgaria","volume":"54","author":"Kamenova","year":"2021","journal-title":"Eur. J. Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0125","series-title":"LightGBM: a highly efficient gradient boosting decision tree","first-page":"3149","author":"Ke","year":"2017"},{"key":"10.1016\/j.compag.2026.111495_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2024.110071","article-title":"Responses of spring wheat yield and growth period to different future climate change models in the yellow river irrigation area based on CMIP6 and WOFOST models","volume":"353","author":"Li","year":"2024","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.111495_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2023.108663","article-title":"Climate-smart irrigation strategy can mitigate agricultural water consumption while ensuring food security under a changing climate","volume":"292","author":"Li","year":"2024","journal-title":"Agric. Water Manag."},{"key":"10.1016\/j.compag.2026.111495_b0130","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.molp.2024.03.002","article-title":"Smart Breeding Platform: a web-based tool for high-throughput population genetics, phenomics, and genomic selection","volume":"17","author":"Li","year":"2024","journal-title":"Mol. PLANT"},{"key":"10.1016\/j.compag.2026.111495_b0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109032","article-title":"Foundation models in smart agriculture: basics, opportunities, and challenges","volume":"222","author":"Li","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111495_b0150","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","article-title":"Temporal fusion transformers for interpretable multi-horizon time series forecasting","volume":"37","author":"Lim","year":"2021","journal-title":"Int. J. Forecast."},{"key":"10.1016\/j.compag.2026.111495_b0155","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.molp.2023.12.014","article-title":"Cultivating potential: Harnessing plant stem cells for agricultural crop improvement","volume":"17","author":"Lindsay","year":"2024","journal-title":"Mol. Plant"},{"key":"10.1016\/j.compag.2026.111495_b0160","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.isprsjprs.2024.03.005","article-title":"WPS:a whole phenology-based spectral feature selection method for mapping winter crop from time-series images","volume":"210","author":"Liu","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0165","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1186\/s13007-024-01272-6","article-title":"Harnessing UAVs and deep learning for accurate grass weed detection in wheat fields: a study on biomass and yield implications","volume":"20","author":"Liu","year":"2024","journal-title":"Plant Methods"},{"key":"10.1016\/j.compag.2026.111495_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.agsy.2023.103711","article-title":"Crop yield estimation based on assimilation of crop models and remote sensing data: a systematic evaluation","volume":"210","author":"Luo","year":"2023","journal-title":"Agric. Syst."},{"key":"10.1016\/j.compag.2026.111495_b0175","article-title":"Developing a land continuous variable estimator to generate daily land products from landsat data","volume":"60","author":"Ma","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0180","article-title":"Field-scale yield prediction of winter wheat under different irrigation regimes based on dynamic fusion of multimodal UAV imagery","volume":"118","author":"Ma","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinformation"},{"key":"10.1016\/j.compag.2026.111495_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2024.114427","article-title":"Subfield-level crop yield mapping without ground truth data: a scale transfer framework","volume":"315","author":"Ma","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.111495_b0190","doi-asserted-by":"crossref","DOI":"10.1038\/s41477-024-01639-6","article-title":"A scoping review on tools and methods for trait prioritization in crop breeding programmes","volume":"10","author":"Occelli","year":"2024","journal-title":"Nat. Plants"},{"key":"10.1016\/j.compag.2026.111495_b0195","article-title":"Combination of UAV and deep learning to estimate wheat yield at ripening stage: the potential of phenotypic features","volume":"124","author":"Peng","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinformation"},{"key":"10.1016\/j.compag.2026.111495_b0200","doi-asserted-by":"crossref","first-page":"0029","DOI":"10.34133\/plantphenomics.0029","article-title":"BreedingEIS: an efficient evaluation information system for crop breeding","volume":"5","author":"Qi","year":"2023","journal-title":"Plant Phenom."},{"key":"10.1016\/j.compag.2026.111495_b0205","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1186\/s12859-022-04755-2","article-title":"GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization","volume":"23","author":"Raubach","year":"2022","journal-title":"BMC Bioinform."},{"key":"10.1016\/j.compag.2026.111495_b0210","doi-asserted-by":"crossref","first-page":"774","DOI":"10.3390\/rs17050774","article-title":"Improving wheat yield prediction with multi-source remote sensing data and machine learning in arid regions","volume":"17","author":"Raza","year":"2025","journal-title":"Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108822","article-title":"Based on historical weather data to predict summer field-scale maize yield: assimilation of remote sensing data to WOFOST model by ensemble kalman filter algorithm","volume":"219","author":"Ren","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111495_b0225","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.tplants.2023.08.007","article-title":"Strategies for breeding crops for future environments","volume":"29","author":"Salse","year":"2024","journal-title":"Trends Plant Sci."},{"key":"10.1016\/j.compag.2026.111495_b0230","doi-asserted-by":"crossref","first-page":"4043","DOI":"10.3390\/rs16214043","article-title":"Multimodal deep learning integration of image, weather, and phenotypic data under temporal effects for early prediction of maize yield","volume":"16","author":"Shamsuddin","year":"2024","journal-title":"Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0235","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/s41586-023-06647-8","article-title":"Role play with large language models","volume":"623","author":"Shanahan","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.compag.2026.111495_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.xplc.2024.100894","article-title":"BreedingAIDB: a database integrating crop genome-to-phenotype paired data with machine learning tools applicable to breeding","volume":"5","author":"Shen","year":"2024","journal-title":"Plant Commun."},{"key":"10.1016\/j.compag.2026.111495_b0240","doi-asserted-by":"crossref","first-page":"255","DOI":"10.34133\/plantphenomics.0255","article-title":"GSP-AI: an AI-powered platform for identifying key growth stages and the vegetative-to-reproductive transition in wheat using trilateral drone imagery and meteorological data","volume":"6","author":"Shen","year":"2024","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.compag.2026.111495_b0250","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.isprsjprs.2024.03.015","article-title":"Bridging the gap between crop breeding and GeoAI: soybean yield prediction from multispectral UAV images with transfer learning","volume":"210","author":"Skobalski","year":"2024","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0255","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1038\/s43016-020-0072-3","article-title":"Crop biotechnology and the future of food","volume":"1","author":"Steinwand","year":"2020","journal-title":"Nat. Food"},{"key":"10.1016\/j.compag.2026.111495_b0260","doi-asserted-by":"crossref","DOI":"10.1016\/j.eja.2022.126500","article-title":"Designing wheat cultivar adaptation to future climate change across China by coupling biophysical modelling and machine learning","volume":"136","author":"Tao","year":"2022","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.compag.2026.111495_b0265","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2024.110183","article-title":"Attention mechanism-based deep learning approach for wheat yield estimation and uncertainty analysis from remotely sensed variables","volume":"356","author":"Tian","year":"2024","journal-title":"Agric. for. Meteorol."},{"key":"10.1016\/j.compag.2026.111495_b0270","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1038\/s43016-023-00867-x","article-title":"Large language models and agricultural extension services","volume":"4","author":"Tzachor","year":"2023","journal-title":"Nat. Food"},{"key":"10.1016\/j.compag.2026.111495_b0275","unstructured":"United Nations, Department of Economic and Social Affairs, Population Division, 2024. World population prospects 2024: Methodology of the united nations population estimates and projections, Advance unedited version. ed, UN DESA\/POP\/2024\/DC\/NO. 10."},{"key":"10.1016\/j.compag.2026.111495_b0280","series-title":"Attention is all you need","first-page":"6000","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.compag.2026.111495_b0285","doi-asserted-by":"crossref","DOI":"10.1016\/j.fcr.2024.109588","article-title":"Digital evolution and twin miracle of sugarcane breeding","volume":"318","author":"Wang","year":"2024","journal-title":"Field Crops Res."},{"key":"10.1016\/j.compag.2026.111495_b0290","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.3390\/rs16111995","article-title":"Time phase selection and accuracy analysis for predicting winter wheat yield based on time series vegetation index","volume":"16","author":"Wang","year":"2024","journal-title":"Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0295","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.3390\/plants13111520","article-title":"Tools and techniques to accelerate crop breeding","volume":"13","author":"Williams","year":"2024","journal-title":"Plants-Basel"},{"key":"10.1016\/j.compag.2026.111495_b0300","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2020.112276","article-title":"Regional winter wheat yield estimation based on the WOFOST model and a novel VW-4DEnSRF assimilation algorithm","volume":"255","author":"Wu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.111495_b0305","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108555","article-title":"Winter wheat yield estimation at the field scale using sentinel-2 data and deep learning","volume":"216","author":"Xiao","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111495_b0310","article-title":"A global systematic review of the remote sensing vegetation indices","volume":"139","author":"Yan","year":"2025","journal-title":"Int. J. Appl. Earth Obs. Geoinformation"},{"key":"10.1016\/j.compag.2026.111495_b0315","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.isprsjprs.2025.03.027","article-title":"Multimodal large language model for wheat breeding: a new exploration of smart breeding","volume":"225","author":"Yang","year":"2025","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0320","series-title":"Biostatistical analysis","author":"Zar","year":"1999"},{"key":"10.1016\/j.compag.2026.111495_b0325","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.isprsjprs.2023.09.025","article-title":"A phenology-guided bayesian-CNN (PB-CNN) framework for soybean yield estimation and uncertainty analysis","volume":"205","author":"Zhang","year":"2023","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0335","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106138","article-title":"Combining texture, color, and vegetation indices from fixed-wing UAS imagery to estimate wheat growth parameters using multivariate regression methods","volume":"185","author":"Zhang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111495_b0360","doi-asserted-by":"crossref","first-page":"5591","DOI":"10.1038\/s41467-022-33265-1","article-title":"Climate change may outpace current wheat breeding yield improvements in north America","volume":"13","author":"Zhang","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compag.2026.111495_b0355","article-title":"Comparison of attention mechanism-based deep learning and transfer strategies for wheat yield estimation using multisource temporal drone imagery","volume":"62","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0365","doi-asserted-by":"crossref","first-page":"2883","DOI":"10.1016\/j.csbj.2024.07.004","article-title":"PidTools: Algorithm and web tools for crop pedigree identification analysis","volume":"23","author":"Zhang","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.compag.2026.111495_b0330","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1145\/3617680","article-title":"A survey of controllable text generation using transformer-based pre-trained language models","volume":"56","author":"Zhang","year":"2024","journal-title":"Acm Comput. Surv."},{"key":"10.1016\/j.compag.2026.111495_b0340","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.111014","article-title":"Winter wheat yield estimation based on multisource remote sensing data: a dual-branch TCN-transformer model and analysis of growth-stage feature transition mechanisms","author":"Zhang","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111495_b0350","article-title":"PlantGPT: an arabidopsis-based intelligent agent that answers questions about plant functional genomics","author":"Zhang","year":"2025","journal-title":"Adv. Sci."},{"key":"10.1016\/j.compag.2026.111495_b0345","article-title":"TKSF-KAN: transformer-enhanced oat yield modeling and transferability across major oat-producing regions in China using UAV multisource data","author":"Zhang","year":"2025","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0375","doi-asserted-by":"crossref","first-page":"5474","DOI":"10.3390\/rs14215474","article-title":"Transfer-learning-based approach for yield prediction of winter wheat from planet data and SAFY model","volume":"14","author":"Zhao","year":"2022","journal-title":"Remote Sens."},{"key":"10.1016\/j.compag.2026.111495_b0370","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106672","article-title":"Intelligent upgrading of plant breeding: Decision support tools in the golden seed breeding cloud platform","volume":"194","author":"Zhao","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111495_b0380","article-title":"A prediction model of maize field yield based on the fusion of multitemporal and multimodal UAV data: a case study in northeast China","author":"Zhou","year":"2023","journal-title":"Remote Sens"},{"key":"10.1016\/j.compag.2026.111495_b0385","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2024.109909","article-title":"Integrating data assimilation, crop model, and machine learning for winter wheat yield forecasting in the north China plain","volume":"347","author":"Zhuang","year":"2024","journal-title":"Agric. for. Meteorol."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926000906?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926000906?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T18:04:46Z","timestamp":1771437886000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926000906"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":77,"alternative-id":["S0168169926000906"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.111495","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Integrating remote sensing data assimilation, deep learning and large language model to interactive yield prediction for wheat breeding","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.111495","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111495"}}