{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T07:28:48Z","timestamp":1774596528903,"version":"3.50.1"},"reference-count":57,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,16]],"date-time":"2026-11-16T00:00:00Z","timestamp":1794787200000},"content-version":"am","delay-in-days":319,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000199","name":"U.S. Department of Agriculture","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019973","name":"Georgia Cotton Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019973","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1934481"],"award-info":[{"award-number":["1934481"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006481","name":"Cotton Incorporated","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006481","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007698","name":"University of Florida","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007698","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["2023-67021-40646"],"award-info":[{"award-number":["2023-67021-40646"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["7009022"],"award-info":[{"award-number":["7009022"]}],"id":[{"id":"10.13039\/100005825","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":[[2026,1]]},"DOI":"10.1016\/j.compag.2025.111214","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:44:58Z","timestamp":1763664298000},"page":"111214","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Dense cotton boll counting with transformer-based video tracking and a customized phenotyping robot for data collection"],"prefix":"10.1016","volume":"240","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6182-6037","authenticated-orcid":false,"given":"Chenjiao","family":"Tan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2590-4797","authenticated-orcid":false,"given":"Changying","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jin","family":"Sun","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"10","key":"10.1016\/j.compag.2025.111214_b0005","doi-asserted-by":"crossref","first-page":"3688","DOI":"10.3390\/s22103688","article-title":"Supervised and weakly supervised deep learning for segmentation and counting of cotton bolls using proximal imagery","volume":"22","author":"Adke","year":"2022","journal-title":"Sensors (basel)"},{"key":"10.1016\/j.compag.2025.111214_b0010","unstructured":"Aharon, N., Orfaig, R., and Bobrovsky, B.-Z. (2022). BoT-SORT: Robust associations multi-pedestrian tracking. arXiv preprint arXiv:2206.14651. doi: 10.48550\/arXiv.2206.14651."},{"key":"10.1016\/j.compag.2025.111214_b0015","article-title":"NTrack: a multiple-object tracker and dataset for infield cotton boll counting","author":"Al Muzaddid","year":"2023","journal-title":"iEEE Trans. Autom. Sci. Eng."},{"issue":"5","key":"10.1016\/j.compag.2025.111214_b0020","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1016\/j.cj.2023.04.005","article-title":"RPNet: rice plant counting after tillering stage based on plant attention and multiple supervision network","volume":"11","author":"Bai","year":"2023","journal-title":"The Crop J."},{"key":"10.1016\/j.compag.2025.111214_b0025","doi-asserted-by":"crossref","first-page":"0020","DOI":"10.34133\/plantphenomics.0020","article-title":"Rice plant counting, locating, and sizing method based on high-throughput UAV RGB images","volume":"5","author":"Bai","year":"2023","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.compag.2025.111214_b0030","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016). Simple online and realtime tracking, in: 2016 IEEE international conference on image processing (ICIP): IEEE, 3464-3468. doi: 10.1109\/ICIP.2016.7533003.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"10.1016\/j.compag.2025.111214_b0035","doi-asserted-by":"crossref","unstructured":"Cao, J., Pang, J., Weng, X., Khirodkar, R., and Kitani, K. (2023). Observation-Centric SORT: Rethinking sort for robust multi-object tracking, in: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 9686-9696. doi: 10.1109\/CVPR52729.2023.00934.","DOI":"10.1109\/CVPR52729.2023.00934"},{"key":"10.1016\/j.compag.2025.111214_b0040","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers, in: European conference on computer vision: Springer, 213-229. doi: 10.1007\/978-3-030-58452-8_13.","DOI":"10.1007\/978-3-030-58452-8_13"},{"issue":"11","key":"10.1016\/j.compag.2025.111214_b0045","doi-asserted-by":"crossref","first-page":"2110","DOI":"10.3390\/agriculture13112110","article-title":"Efficient and lightweight automatic wheat counting method with observation-centric sort for real-time unmanned aerial vehicle surveillance","volume":"13","author":"Chen","year":"2023","journal-title":"Agriculture"},{"key":"10.1016\/j.compag.2025.111214_b0050","doi-asserted-by":"crossref","first-page":"108045","DOI":"10.1016\/j.compag.2023.108045","article-title":"Real-time missing seedling counting in paddy fields based on lightweight network and tracking-by-detection algorithm","volume":"212","author":"Cui","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111214_b0055","unstructured":"Darkpgmr (2020). DarkLabel [Online]. Available: https:\/\/github.com\/darkpgmr\/DarkLabel [Accessed 1 Feb 2023]."},{"key":"10.1016\/j.compag.2025.111214_b0060","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"10061","article-title":"TAPIR: tracking any point with per-frame initialization and temporal refinement","author":"Doersch","year":"2023"},{"key":"10.1016\/j.compag.2025.111214_b0070","series-title":"Cotton Fiber: Physics, Chemistry and Biology","author":"Fang","year":"2018"},{"key":"10.1016\/j.compag.2025.111214_b0075","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2022.1003243","article-title":"LettuceTrack: detection and tracking of lettuce for robotic precision spray in agriculture","volume":"13","author":"Hu","year":"2022","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2025.111214_b0065","unstructured":"ERS USDA (2022). Cotton and Wool [Online]. Available: https:\/\/www.ers.usda.gov\/topics\/crops\/cotton-and-wool\/ [Accessed March 1 2024]."},{"key":"10.1016\/j.compag.2025.111214_b0080","doi-asserted-by":"crossref","unstructured":"Huang, Z., Shi, X., Zhang, C., Wang, Q., Cheung, K.C., Qin, H., et al. (2022). FlowFormer: A transformer architecture for optical flow, in: European conference on computer vision: Springer, 668-685. doi: 10.1007\/978-3-031-19790-1_40.","DOI":"10.1007\/978-3-031-19790-1_40"},{"key":"10.1016\/j.compag.2025.111214_b0085","first-page":"597","article-title":"On the computational complexity of self-attention","author":"Keles","year":"2023","journal-title":"Int. Conf. ALT PMLR"},{"key":"10.1016\/j.compag.2025.111214_b0090","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Wahid, A., Ahmad, M., Tahir, M.T., Ahmed, M., Ahmad, S., et al. (2020). World cotton production and consumption: An overview. Cotton production and uses: Agronomy, crop protection, and postharvest technologies, 1-7. doi: 10.1007\/978-981-15-1472-2_1.","DOI":"10.1007\/978-981-15-1472-2_1"},{"issue":"1\u20132","key":"10.1016\/j.compag.2025.111214_b0095","first-page":"83","article-title":"The Hungarian method for the assignment problem","volume":"2","author":"Kuhn","year":"2006","journal-title":"Nav. Res. Logist. Q."},{"issue":"4","key":"10.1016\/j.compag.2025.111214_b0100","doi-asserted-by":"crossref","DOI":"10.1093\/pnasnexus\/pgad076","article-title":"Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale","volume":"2","author":"Li","year":"2023","journal-title":"PNAS Nexus"},{"key":"10.1016\/j.compag.2025.111214_b0105","first-page":"1","article-title":"TasselNetV3: explainable plant counting with guided upsampling and background suppression","volume":"60","author":"Lu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"15","key":"10.1016\/j.compag.2025.111214_b0110","doi-asserted-by":"crossref","first-page":"6650","DOI":"10.3390\/app14156650","article-title":"COTTON-YOLO: enhancing cotton boll detection and counting in complex environmental conditions using an advanced yolo model","volume":"14","author":"Lu","year":"2024","journal-title":"Appl. Sci."},{"key":"10.1016\/j.compag.2025.111214_b0115","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1007\/s11263-020-01375-2","article-title":"HOTA: a higher order metric for evaluating multi-object tracking","volume":"129","author":"Luiten","year":"2021","journal-title":"Int. J. Comput. Vision"},{"key":"10.1016\/j.compag.2025.111214_b0120","doi-asserted-by":"crossref","unstructured":"Lv, W., Xu, S., Zhao, Y., Wang, G., Wei, J., Cui, C., et al. (2023). DETRs beat YOLOs on real-time object detection. arXiv preprint arXiv:2304.08069. doi: 10.1109\/CVPR52733.2024.01605.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"10.1016\/j.compag.2025.111214_b0125","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4040","article-title":"A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation","author":"Mayer","year":"2016"},{"key":"10.1016\/j.compag.2025.111214_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106533","article-title":"Fruit detection and load estimation of an orange orchard using the YOLO models through simple approaches in different imaging and illumination conditions","volume":"191","author":"Mirhaji","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111214_b0135","doi-asserted-by":"crossref","unstructured":"Osco, L.P., De Arruda, M.d.S., Junior, J.M., Da Silva, N.B., Ramos, A.P.M., Moryia, \u00c9.A.S., et al. (2020). A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing 160, 97-106. doi: 10.1016\/j.isprsjprs.2019.12.010.","DOI":"10.1016\/j.isprsjprs.2019.12.010"},{"key":"10.1016\/j.compag.2025.111214_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.106734","article-title":"Weakly-supervised learning to automatically count cotton flowers from aerial imagery","volume":"194","author":"Petti","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111214_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109294","article-title":"Graph Neural Networks for lightweight plant organ tracking","volume":"225","author":"Petti","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111214_b0150","doi-asserted-by":"crossref","unstructured":"Petti, D.J., and Li, C. (2024). Active Learning for Real-Time Flower Counting with a Ground Mobile Robot, in: 2024 ASABE Annual International Meeting: American Society of Agricultural and Biological Engineers, 1. doi: 10.13031\/aim.202400607.","DOI":"10.13031\/aim.202400607"},{"key":"10.1016\/j.compag.2025.111214_b0155","doi-asserted-by":"crossref","unstructured":"Saraceni, L., Motoi, I.M., Nardi, D., and Ciarfuglia, T.A. (2024). AgriSORT: A Simple Online Real-time Tracking-by-Detection framework for robotics in precision agriculture, in: 2024 IEEE International Conference on Robotics and Automation (ICRA): IEEE, 2675-2682. doi: 10.1109\/ICRA57147.2024.10610231.","DOI":"10.1109\/ICRA57147.2024.10610231"},{"key":"10.1016\/j.compag.2025.111214_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108557","article-title":"Aerial imagery-based tobacco plant counting framework for efficient crop emergence estimation","volume":"217","author":"Shahid","year":"2024","journal-title":"Comput. Electron. Agric."},{"issue":"5","key":"10.1016\/j.compag.2025.111214_b0165","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.2134\/agronj15.0024","article-title":"Contribution of boll mass and boll number to irrigated cotton yield","volume":"107","author":"Sharma","year":"2015","journal-title":"Agron. J."},{"key":"10.1016\/j.compag.2025.111214_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.104976","article-title":"Image processing algorithms for infield single cotton boll counting and yield prediction","volume":"166","author":"Sun","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111214_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106683","article-title":"Towards real-time tracking and counting of seedlings with a one-stage detector and optical flow","volume":"193","author":"Tan","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111214_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108359","article-title":"Anchor-free deep convolutional neural network for tracking and counting cotton seedlings and flowers","volume":"215","author":"Tan","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111214_b0185","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.biosystemseng.2024.08.010","article-title":"Three-view cotton flower counting through multi-object tracking and RGB-D imagery","volume":"246","author":"Tan","year":"2024","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.compag.2025.111214_b0190","doi-asserted-by":"crossref","first-page":"110065","DOI":"10.1016\/j.compag.2025.110065","article-title":"A customized density map model and segment anything model for cotton boll number, size, and yield prediction in aerial images","volume":"232","author":"Tan","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111214_b0195","unstructured":"Tzutalin (2015). LabelImg [Online]. Available: https:\/\/github.com\/HumanSignal\/labelImg [Accessed May 1 2023]."},{"key":"10.1016\/j.compag.2025.111214_b0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107513","article-title":"Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms","volume":"204","author":"Villacr\u00e9s","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111214_b0205","doi-asserted-by":"crossref","DOI":"10.1002\/rob.22567","article-title":"Geometry\u2010aware 3D point cloud learning for precise cutting\u2010point detection in unstructured field environments","author":"Wang","year":"2025","journal-title":"Journal of Field Robotics"},{"key":"10.1016\/j.compag.2025.111214_b0210","series-title":"Simple Online and Realtime Tracking with a Deep Association Metric","first-page":"3645","author":"Wojke","year":"2017"},{"key":"10.1016\/j.compag.2025.111214_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107827","article-title":"Detection and counting of banana bunches by integrating deep learning and classic image-processing algorithms","volume":"209","author":"Wu","year":"2023","journal-title":"Comput. Electron. Agric."},{"issue":"12","key":"10.1016\/j.compag.2025.111214_b0220","doi-asserted-by":"crossref","first-page":"3002","DOI":"10.3390\/agronomy14123002","article-title":"An enhanced cycle generative adversarial network approach for nighttime pineapple detection of automated harvesting robots","volume":"14","author":"Wu","year":"2024","journal-title":"Agronomy"},{"key":"10.1016\/j.compag.2025.111214_b0225","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.biosystemseng.2023.09.005","article-title":"Twice matched fruit counting system: an automatic fruit counting pipeline in modern apple orchard using mutual and secondary matches","volume":"234","author":"Wu","year":"2023","journal-title":"Biosyst. Eng."},{"issue":"1","key":"10.1016\/j.compag.2025.111214_b0230","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"},{"issue":"4","key":"10.1016\/j.compag.2025.111214_b0235","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1002\/rob.22056","article-title":"A modular agricultural robotic system (MARS) for precision farming: Concept and implementation","volume":"39","author":"Xu","year":"2022","journal-title":"J. Field Robot."},{"key":"10.1016\/j.compag.2025.111214_b0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107339","article-title":"Multi-object tracking using deep sort and modified centernet in cotton seedling counting","volume":"202","author":"Yang","year":"2022","journal-title":"Comput. Electron. Agric."},{"issue":"2","key":"10.1016\/j.compag.2025.111214_b0245","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1111\/jipb.13388","article-title":"Recent progression and future perspectives in cotton genomic breeding","volume":"65","author":"Yang","year":"2023","journal-title":"J. Integr. Plant Biol."},{"issue":"1","key":"10.1016\/j.compag.2025.111214_b0250","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"},{"key":"10.1016\/j.compag.2025.111214_b0255","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.compag.2025.111214_b0260","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., et al. (2022a). DINO: DETR with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605."},{"issue":"5","key":"10.1016\/j.compag.2025.111214_b0265","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.3390\/s20051520","article-title":"Applications of deep learning for dense scenes analysis in agriculture: a review","volume":"20","author":"Zhang","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.compag.2025.111214_b0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107905","article-title":"SwinT-YOLO: detection of densely distributed maize tassels in remote sensing images","volume":"210","author":"Zhang","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111214_b0275","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., et al. (2022b). ByteTrack: Multi-object tracking by associating every detection box, in: European conference on computer vision: Springer, 1-21. doi: 10.1007\/978-3-031-20047-2_1.","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"10.1016\/j.compag.2025.111214_b0280","doi-asserted-by":"crossref","first-page":"0100","DOI":"10.34133\/plantphenomics.0100","article-title":"A multiscale point-supervised network for counting maize tassels in the wild","volume":"5","author":"Zheng","year":"2023","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.compag.2025.111214_b0285","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable DETR: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169925013201?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169925013201?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T02:41:55Z","timestamp":1773628915000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169925013201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":57,"alternative-id":["S0168169925013201"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2025.111214","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Dense cotton boll counting with transformer-based video tracking and a customized phenotyping robot for data collection","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2025.111214","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 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":"111214"}}