{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T05:41:13Z","timestamp":1776922873698,"version":"3.51.2"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62406327"],"award-info":[{"award-number":["62406327"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012428","name":"Central Public-interest Scientific Institution Basal Research Fund, Chinese Academy of Fishery Sciences","doi-asserted-by":"publisher","award":["JBYW-AII-2025-04"],"award-info":[{"award-number":["JBYW-AII-2025-04"]}],"id":[{"id":"10.13039\/501100012428","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005196","name":"Chinese Academy of Agricultural Sciences","doi-asserted-by":"publisher","award":["CAAS-BRC-SAE-2025-01"],"award-info":[{"award-number":["CAAS-BRC-SAE-2025-01"]}],"id":[{"id":"10.13039\/501100005196","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":[[2026,6]]},"DOI":"10.1016\/j.compag.2026.111729","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T16:18:10Z","timestamp":1775233090000},"page":"111729","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Integrating plant physiology with deep learning for fresh weight prediction across lettuce growth cycles"],"prefix":"10.1016","volume":"247","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2067-2309","authenticated-orcid":false,"given":"Yubin","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yanqi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qixin","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Zhaoxin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2757-9900","authenticated-orcid":false,"given":"Xiujuan","family":"Chai","sequence":"additional","affiliation":[]},{"given":"Tan","family":"Sun","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2026.111729_b0005","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.agwat.2018.04.013","article-title":"Drought response in field grown potatoes and the interactions between canopy growth and yield","volume":"206","author":"Aliche","year":"2018","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.compag.2026.111729_b0010","doi-asserted-by":"crossref","first-page":"127","DOI":"10.2135\/cropsci1986.0011183X002600010030x","article-title":"Leaf photosynthesis and its correlation with leaf area 1","volume":"26","author":"Bhagsari","year":"1986","journal-title":"Crop Sci."},{"key":"10.1016\/j.compag.2026.111729_b0015","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-025-01347-y","article-title":"A method for phenotyping lettuce volume and structure from 3D images","volume":"21","author":"Bloch","year":"2025","journal-title":"Plant Methods"},{"key":"10.1016\/j.compag.2026.111729_b0020","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1038\/s43016-022-00622-8","article-title":"A systematic scoping review of the sustainability of vertical farming, plant-based alternatives, food delivery services and blockchain in food systems","volume":"3","author":"Bunge","year":"2022","journal-title":"Nat. Food"},{"key":"10.1016\/j.compag.2026.111729_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2024.115235","article-title":"Energy consumption of plant factory with artificial light: challenges and opportunities","volume":"210","author":"Cai","year":"2025","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.compag.2026.111729_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110299","article-title":"Advancing biomass estimation in hydroponic lettuce using RGB-depth imaging and morphometric descriptors with machine learning","volume":"234","author":"Cardenas-Gallegos","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111729_b0035","article-title":"Multimodal phenotyping reveals structural\u2013physiological coordination mechanisms underlying light-use efficiency in lettuce","volume":"100560","author":"Chen","year":"2025","journal-title":"Curr. Plant Biol."},{"key":"10.1016\/j.compag.2026.111729_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108876","article-title":"A method for multi-target segmentation of bud-stage apple trees based on improved YOLOv8","volume":"220","author":"Chen","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111729_b0045","series-title":"2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA)","first-page":"237","article-title":"Strawberry Disease and Pest Identification and Control based on SE-ResNeXt50 Model","author":"Gan","year":"2022"},{"key":"10.1016\/j.compag.2026.111729_b0050","doi-asserted-by":"crossref","DOI":"10.3390\/s22155499","article-title":"Estimation of Greenhouse Lettuce Growth Indices based on a Two-Stage CNN using RGB-D Images","volume":"22","author":"Gang","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.compag.2026.111729_b0055","doi-asserted-by":"crossref","first-page":"3097","DOI":"10.1002\/agj2.21680","article-title":"Crop yield estimation uncertainties at the regional scale for Saxony, Germany","volume":"116","author":"Goihl","year":"2024","journal-title":"Agron. J."},{"key":"10.1016\/j.compag.2026.111729_b0060","doi-asserted-by":"crossref","DOI":"10.1002\/ppj2.20110","article-title":"Quantifying leaf symptoms of sorghum charcoal rot in images of field-grown plants using deep neural networks","author":"Gonzalez","year":"2024","journal-title":"Plant Phenome Journal 7."},{"key":"10.1016\/j.compag.2026.111729_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.122334","article-title":"AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory","volume":"356","author":"Hu","year":"2024","journal-title":"Appl. Energy"},{"key":"10.1016\/j.compag.2026.111729_b0070","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2024.1365266","article-title":"Development of a machine vision-based weight prediction system of butterhead lettuce (Lactuca sativa L.) using deep learning models for industrial plant factory","volume":"15","author":"Kim","year":"2024","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111729_b0075","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2022.980581","article-title":"Automatic monitoring of lettuce fresh weight by multi-modal fusion based deep learning","volume":"13","author":"Lin","year":"2022","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111729_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105753","article-title":"Automatic segmentation of overlapped poplar seedling leaves combining Mask R-CNN and DBSCAN","volume":"178","author":"Liu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111729_b0085","doi-asserted-by":"crossref","DOI":"10.1111\/nph.17611","article-title":"Canopy occupation volume as an indicator of canopy photosynthetic capacity","volume":"232","author":"Liu","year":"2021","journal-title":"New Phytol."},{"key":"10.1016\/j.compag.2026.111729_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110721","article-title":"Dual cross-modality fusion boosts the RGBD-based lettuce fresh weight estimation","volume":"237","author":"Ma","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111729_b0095","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.compag.2018.10.011","article-title":"Semantic labeling and reconstruction of grape bunches from 3D range data using a new RGB-D feature descriptor","volume":"155","author":"Mack","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111729_b0100","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.compag.2018.11.026","article-title":"In-field high throughput grapevine phenotyping with a consumer-grade depth camera","volume":"156","author":"Milella","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111729_b0105","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.compag.2018.09.010","article-title":"Segmentation of lettuce in coloured 3D point clouds for fresh weight estimation","volume":"154","author":"Mortensen","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111729_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108642","article-title":"Estimating hydroponic lettuce phenotypic parameters for efficient resource allocation","volume":"218","author":"Ojo","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111729_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130521","article-title":"Informed Machine Learning: Excess risk and generalization","volume":"646","author":"Oneto","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compag.2026.111729_b0120","first-page":"443","article-title":"Classification of Mango Plant Leaf Diseases using Optimized ConvNeXt","volume":"2024","author":"Rohman","year":"2024","journal-title":"International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA)"},{"key":"10.1016\/j.compag.2026.111729_b0125","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1111\/j.1365-3040.2005.01490.x","article-title":"Functional dynamics of plant growth and photosynthesis\u2013from steady\u2010state to dynamics\u2013from homogeneity to heterogeneity","volume":"29","author":"Schurr","year":"2006","journal-title":"Plant Cell Environ."},{"key":"10.1016\/j.compag.2026.111729_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108263","article-title":"PosNet: estimating lettuce fresh weight in plant factory based on oblique image","volume":"213","author":"Tan","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111729_b0135","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.inpa.2021.06.003","article-title":"Application status and challenges of machine vision in plant factory\u2014A review","volume":"9","author":"Tian","year":"2022","journal-title":"Information Processing in Agriculture"},{"key":"10.1016\/j.compag.2026.111729_b0140","unstructured":"Wang W. 2023. Advanced auto labeling solution with added features. Github Repository in press."},{"key":"10.1016\/j.compag.2026.111729_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109681","article-title":"A high-throughput method for monitoring growth of lettuce seedlings in greenhouses based on enhanced Mask2Former","volume":"227","author":"Wei","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111729_b0150","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1111\/nph.20129","article-title":"An allometry perspective on crops","volume":"244","author":"Westgeest","year":"2024","journal-title":"New Phytol."},{"key":"10.1016\/j.compag.2026.111729_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110726","article-title":"TinySeg: a deep learning model for small target segmentation of grape pedicels with multi-attention and multi-scale feature fusion","volume":"237","author":"Wu","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111729_b0160","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.tplants.2022.08.018","article-title":"Machine learning bridges omics sciences and plant breeding","volume":"28","author":"Yan","year":"2023","journal-title":"Trends Plant Sci."},{"key":"10.1016\/j.compag.2026.111729_b0165","article-title":"A review on full-, zero-, and partial-knowledge based predictive models for industrial applications","volume":"102996","author":"Zampini","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.compag.2026.111729_b0170","doi-asserted-by":"crossref","first-page":"13670","DOI":"10.1109\/TNNLS.2025.3539314","article-title":"A Systematic Review on Long-Tailed Learning","volume":"36","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10.1016\/j.compag.2026.111729_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.112094","article-title":"Multi-phenotypic parameters extraction and biomass estimation for lettuce based on point clouds","volume":"204","author":"Zhang","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.compag.2026.111729_b0180","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1038\/s41438-020-00345-6","article-title":"Growth monitoring of greenhouse lettuce based on a convolutional neural network","volume":"7","author":"Zhang","year":"2020","journal-title":"Hortic. Res."},{"key":"10.1016\/j.compag.2026.111729_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109868","article-title":"YOMASK: an instance segmentation method for high-throughput phenotypic platform lettuce images","volume":"230","author":"Zhao","year":"2025","journal-title":"Comput. Electron. Agric."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926003248?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926003248?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T04:49:46Z","timestamp":1776919786000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926003248"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":37,"alternative-id":["S0168169926003248"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.111729","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Integrating plant physiology with deep learning for fresh weight prediction across lettuce growth cycles","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.111729","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":"111729"}}