{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T10:17:14Z","timestamp":1788257834680,"version":"build-2803163510"},"reference-count":53,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["aBIOTECH"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.abiote.2026.100082","type":"journal-article","created":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T06:28:23Z","timestamp":1785565703000},"page":"100082","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["VT-SCC: Visual-text semantic alignment via multimodal guidance for unified staple crop organ counting"],"prefix":"10.1016","volume":"7","author":[{"given":"Lanying","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengbo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hainan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.abiote.2026.100082_bib1","series-title":"Wheat improvement: food security in a changing climate","first-page":"47","article-title":"Global trends in wheat production, consumption and trade","author":"Erenstein","year":"2022"},{"issue":"7","key":"10.1016\/j.abiote.2026.100082_bib2","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1038\/s41587-019-0152-9","article-title":"Breeding crops to feed 10 billion","volume":"37","author":"Hickey","year":"2019","journal-title":"Nat Biotechnol"},{"issue":"6","key":"10.1016\/j.abiote.2026.100082_bib3","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0066428","article-title":"Yield trends are insufficient to double global crop production by 2050","volume":"8","author":"Ray","year":"2013","journal-title":"PLoS One"},{"issue":"2","key":"10.1016\/j.abiote.2026.100082_bib4","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.molp.2020.01.008","article-title":"Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives","volume":"13","author":"Yang","year":"2020","journal-title":"Mol Plant"},{"issue":"1","key":"10.1016\/j.abiote.2026.100082_bib5","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1109\/MGRS.2020.2998816","article-title":"High-throughput estimation of crop traits: a review of ground and aerial phenotyping platforms","volume":"9","author":"Jin","year":"2021","journal-title":"IEEE Geosci Remote Sens Mag"},{"issue":"5","key":"10.1016\/j.abiote.2026.100082_bib6","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.tplants.2018.02.001","article-title":"Translating high-throughput phenotyping into genetic gain","volume":"23","author":"Araus","year":"2018","journal-title":"Trends Plant Sci"},{"issue":"2","key":"10.1016\/j.abiote.2026.100082_bib7","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.tplants.2018.11.007","article-title":"Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture","volume":"24","author":"Maes","year":"2019","journal-title":"Trends Plant Sci"},{"key":"10.1016\/j.abiote.2026.100082_bib8","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.neucom.2022.03.017","article-title":"WheatNet: a lightweight convolutional neural network for high-throughput image-based wheat head detection and counting","volume":"489","author":"Khaki","year":"2022","journal-title":"Neurocomputing"},{"issue":"5","key":"10.1016\/j.abiote.2026.100082_bib9","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.3390\/rs15051280","article-title":"Sampling survey method of wheat ear number based on UAV images and density map regression algorithm","volume":"15","author":"Wu","year":"2023","journal-title":"Remote Sens"},{"key":"10.1016\/j.abiote.2026.100082_bib10","doi-asserted-by":"crossref","first-page":"236","DOI":"10.34133\/plantphenomics.0236","article-title":"CSNet: a count-supervised network via multiscale MLP-mixer for wheat ear counting","volume":"6","author":"Li","year":"2024","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.abiote.2026.100082_bib11","doi-asserted-by":"crossref","first-page":"85","DOI":"10.34133\/plantphenomics.0085","article-title":"Instance segmentation and berry counting of table grape before thinning based on AS-SwinT","volume":"5","author":"Du","year":"2023","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.abiote.2026.100082_bib12","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2020.571299","article-title":"Tomato fruit detection and counting in greenhouses using deep learning","volume":"11","author":"Afonso","year":"2020","journal-title":"Front Plant Sci"},{"key":"10.1016\/j.abiote.2026.100082_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108425","article-title":"A survey of deep learning-based object detection methods in crop counting","volume":"215","author":"Huang","year":"2023","journal-title":"Comput Electron Agric"},{"key":"10.1016\/j.abiote.2026.100082_bib14","article-title":"TasselLFANet: a novel lightweight multi-branch feature aggregation neural network for high-throughput image-based maize tassels detection and counting","volume":"14","author":"Yu","year":"2023","journal-title":"Front Plant Sci"},{"key":"10.1016\/j.abiote.2026.100082_bib15","doi-asserted-by":"crossref","first-page":"105","DOI":"10.34133\/plantphenomics.0105","article-title":"Panicle-cloud: an open and AI-powered cloud computing platform for quantifying rice panicles from drone-collected imagery to enable the classification of yield production in rice","volume":"5","author":"Teng","year":"2023","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.abiote.2026.100082_bib16","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109745","article-title":"Robust counting for multi-species plants based on few-shot learning","volume":"229","author":"Zhao","year":"2025","journal-title":"Comput Electron Agric"},{"key":"10.1016\/j.abiote.2026.100082_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107704","article-title":"PlantBiCNet: a new paradigm in plant science with bi-directional cascade neural network for detection and counting","volume":"130","author":"Ye","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.abiote.2026.100082_bib18","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.isprsjprs.2025.11.017","article-title":"TasselNetV4: a vision foundation model for cross-scene, cross-scale, and cross-species plant counting","volume":"231","author":"Hu","year":"2026","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"key":"10.1016\/j.abiote.2026.100082_bib19","first-page":"38","article-title":"Grounding DINO: marrying DINO with grounded pre-training for open-set object detection","volume":"15105","author":"Liu","year":"2025"},{"key":"10.1016\/j.abiote.2026.100082_bib20","doi-asserted-by":"crossref","first-page":"48810","DOI":"10.52202\/079017-1547","article-title":"CountGD: multi-modal open-world counting","volume":"37","author":"Amini-Naieni","year":"2024","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.abiote.2026.100082_bib21","first-page":"91","article-title":"Faster R-CNN: towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.abiote.2026.100082_bib22","first-page":"21","article-title":"SSD: single shot MultiBox detector","volume":"9905","author":"Liu","year":"2016"},{"key":"10.1016\/j.abiote.2026.100082_bib23","series-title":"Proceedings of the IEEE international conference on computer vision","first-page":"2961","article-title":"Mask R-CNN","author":"He","year":"2017"},{"key":"10.1016\/j.abiote.2026.100082_bib24","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"9627","article-title":"FCOS: fully convolutional one-stage object detection","author":"Tian","year":"2019"},{"key":"10.1016\/j.abiote.2026.100082_bib25","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"10781","article-title":"EfficientDet: scalable and efficient object detection","author":"Tan","year":"2020"},{"key":"10.1016\/j.abiote.2026.100082_bib26","series-title":"YOLOv4: optimal speed and accuracy of object detection","author":"Bochkovskiy","year":"2020"},{"key":"10.1016\/j.abiote.2026.100082_bib27","series-title":"YOLOX: exceeding YOLO series in 2021","author":"Ge","year":"2021"},{"key":"10.1016\/j.abiote.2026.100082_bib28","unstructured":"Jocher G, Chaurasia A, Stoken A, Borovec J, Kwon Y, et al., NanoCode012 ultralytics\/yolov5: v6.2\u2014YOLOv5 classification models, Apple M1, reproducibility, ClearML and Deci.ai integrations [software], Zenodo, August 17, 2022."},{"key":"10.1016\/j.abiote.2026.100082_bib29","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops","first-page":"5350","article-title":"Real-time multi-class helmet violation detection using few-shot data sampling technique and YOLOv8","author":"Aboah","year":"2023"},{"key":"10.1016\/j.abiote.2026.100082_bib30","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"589","article-title":"Single-image crowd counting via multi-column convolutional neural network","author":"Zhang","year":"2016"},{"key":"10.1016\/j.abiote.2026.100082_bib31","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1091","article-title":"CSRNet: dilated convolutional neural networks for understanding the highly congested scenes","author":"Li","year":"2018"},{"issue":"1","key":"10.1016\/j.abiote.2026.100082_bib32","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1186\/s13007-023-01079-x","article-title":"WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network","volume":"19","author":"Ye","year":"2023","journal-title":"Plant Methods"},{"issue":"1","key":"10.1016\/j.abiote.2026.100082_bib33","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s13007-023-00985-4","article-title":"YOLO POD: a fast and accurate multi-task model for dense soybean pod counting","volume":"19","author":"Xiang","year":"2023","journal-title":"Plant Methods"},{"key":"10.1016\/j.abiote.2026.100082_bib34","article-title":"Accurate and fast implementation of soybean pod counting and localization from high-resolution image","volume":"15","author":"Yu","year":"2024","journal-title":"Front Plant Sci"},{"issue":"8","key":"10.1016\/j.abiote.2026.100082_bib35","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.3390\/agriculture14081347","article-title":"SPCN: an innovative soybean pod counting network based on HDC strategy and attention mechanism","volume":"14","author":"Li","year":"2024","journal-title":"Agriculture"},{"key":"10.1016\/j.abiote.2026.100082_bib36","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"9377","article-title":"DESTR: object detection with split transformer","author":"He","year":"2022"},{"issue":"6","key":"10.1016\/j.abiote.2026.100082_bib37","doi-asserted-by":"crossref","DOI":"10.1007\/s11432-021-3445-y","article-title":"TransCrowd: weakly-supervised crowd counting with transformers","volume":"65","author":"Liang","year":"2022","journal-title":"Sci China Inf Sci"},{"key":"10.1016\/j.abiote.2026.100082_bib38","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"10012","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.abiote.2026.100082_bib39","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"3365","article-title":"Rethinking counting and localization in crowds: a purely point-based framework","author":"Song","year":"2021"},{"issue":"1","key":"10.1016\/j.abiote.2026.100082_bib40","doi-asserted-by":"crossref","DOI":"10.1016\/j.plaphe.2025.100003","article-title":"LKNet: enhancing rice canopy panicle counting accuracy with an optimized point-based framework","volume":"7","author":"Li","year":"2025","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.abiote.2026.100082_bib41","doi-asserted-by":"crossref","DOI":"10.1109\/TGRS.2021.3058962","article-title":"TasselNetV3: explainable plant counting with guided upsampling and background suppression","volume":"60","author":"Lu","year":"2022","journal-title":"IEEE Trans Geosci Rem Sens"},{"key":"10.1016\/j.abiote.2026.100082_bib42","series-title":"Proceedings of the IEEE international conference on computer vision","first-page":"2980","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"},{"key":"10.1016\/j.abiote.2026.100082_bib43","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"6569","article-title":"CenterNet: keypoint triplets for object detection","author":"Duan","year":"2019"},{"key":"10.1016\/j.abiote.2026.100082_bib44","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"7464","article-title":"YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","author":"Wang","year":"2023"},{"key":"10.1016\/j.abiote.2026.100082_bib45","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1186\/s13007-019-0537-2","article-title":"TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks","volume":"15","author":"Xiong","year":"2019","journal-title":"Plant Methods"},{"issue":"10","key":"10.1016\/j.abiote.2026.100082_bib46","doi-asserted-by":"crossref","first-page":"3513","DOI":"10.1109\/TCSVT.2019.2942970","article-title":"Counting objects by blockwise classification","volume":"30","author":"Liu","year":"2020","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"10.1016\/j.abiote.2026.100082_bib47","doi-asserted-by":"crossref","first-page":"271","DOI":"10.34133\/plantphenomics.0271","article-title":"One to all: toward a unified model for counting cereal crop heads based on few-shot learning","volume":"6","author":"Wang","year":"2024","journal-title":"Plant Phenomics"},{"issue":"6","key":"10.1016\/j.abiote.2026.100082_bib49","first-page":"5832","article-title":"Vision transformer off-the-shelf: a surprising baseline for few-shot class-agnostic counting","volume":"38","author":"Wang","year":"2024","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"10.1016\/j.abiote.2026.100082_bib50","series-title":"Proceedings of the IEEE\/CVF winter conference on applications of computer vision","first-page":"6315","article-title":"Few-shot object counting with similarity-aware feature enhancement","author":"You","year":"2023"},{"issue":"3","key":"10.1016\/j.abiote.2026.100082_bib48","first-page":"2714","article-title":"VLCounter: text-aware visual representation for zero-shot object counting","volume":"38","author":"Kang","year":"2024","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"10.1016\/j.abiote.2026.100082_bib51","series-title":"Proceedings of the 33rd British machine vision conference","article-title":"CounTR: transformer-based generalised visual counting","author":"Liu","year":"2022"},{"key":"10.1016\/j.abiote.2026.100082_bib52","doi-asserted-by":"crossref","DOI":"10.34133\/2021\/9846158","article-title":"Global wheat head detection 2021: an improved dataset for benchmarking wheat head detection methods","volume":"2021","author":"David","year":"2021","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.abiote.2026.100082_bib53","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1186\/s13007-017-0224-0","article-title":"TasselNet: counting maize tassels in the wild via local counts regression network","volume":"13","author":"Lu","year":"2017","journal-title":"Plant Methods"}],"container-title":["aBIOTECH"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2662173826000962?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2662173826000962?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T09:52:03Z","timestamp":1788256323000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2662173826000962"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":53,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["S2662173826000962"],"URL":"https:\/\/doi.org\/10.1016\/j.abiote.2026.100082","relation":{},"ISSN":["2662-1738"],"issn-type":[{"value":"2662-1738","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"VT-SCC: Visual-text semantic alignment via multimodal guidance for unified staple crop organ counting","name":"articletitle","label":"Article Title"},{"value":"aBIOTECH","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.abiote.2026.100082","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V. on behalf of Agricultural Information Institute of CAAS.","name":"copyright","label":"Copyright"}],"article-number":"100082"}}