{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:18:25Z","timestamp":1773015505904,"version":"3.50.1"},"reference-count":39,"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":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004070","name":"Khalifa University of Science, Technology and Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004070","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.2025.111192","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T11:51:47Z","timestamp":1762429907000},"page":"111192","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["SAMConvFormer+LLM: Exploring synergistic fusion of Segment Anything Model with joint convolutional transformer and large language model to advance dense agricultural crop analysis"],"prefix":"10.1016","volume":"240","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7679-081X","authenticated-orcid":false,"given":"Muhammad","family":"Owais","sequence":"first","affiliation":[]},{"given":"Israa","family":"Fahmy","sequence":"additional","affiliation":[]},{"given":"Lakmal","family":"Seneviratne","sequence":"additional","affiliation":[]},{"given":"Irfan","family":"Hussain","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.compag.2025.111192_b1","first-page":"8623","article-title":"Performance evaluations of convolutional neural network (CNN)-based models for semantic segmentation of plant leaf diseases","volume":"6","author":"Abd Almisreb","year":"2022","journal-title":"J. Posit. Sch. Psychol."},{"key":"10.1016\/j.compag.2025.111192_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.105091","article-title":"Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure","volume":"167","author":"Abdalla","year":"2019","journal-title":"Comput. Electron. Agric."},{"issue":"4","key":"10.1016\/j.compag.2025.111192_b3","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.3390\/make4040052","article-title":"A morphological post-processing approach for overlapped segmentation of bacterial cell images","volume":"4","author":"Abeyrathna","year":"2022","journal-title":"Mach. Learn. Knowl. Extr."},{"issue":"12","key":"10.1016\/j.compag.2025.111192_b4","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compag.2025.111192_b5","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H., 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 801\u2013818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"10.1016\/j.compag.2025.111192_b6","doi-asserted-by":"crossref","unstructured":"Chollet, F., 2017. Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1251\u20131258.","DOI":"10.1109\/CVPR.2017.195"},{"key":"10.1016\/j.compag.2025.111192_b7","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.compag.2025.111192_b8","series-title":"The llama 3 herd of models","author":"Dubey","year":"2024"},{"issue":"6","key":"10.1016\/j.compag.2025.111192_b9","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.3390\/agronomy14061178","article-title":"HPPEM: A high-precision blueberry cluster phenotype extraction model based on hybrid task cascade","volume":"14","author":"Gai","year":"2024","journal-title":"Agronomy"},{"issue":"2","key":"10.1016\/j.compag.2025.111192_b10","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s11263-017-1016-8","article-title":"End-to-end learning of deep visual representations for image retrieval","volume":"124","author":"Gordo","year":"2017","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.compag.2025.111192_b11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.compag.2025.111192_b12","series-title":"2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology","first-page":"1","article-title":"A survey of loss functions for semantic segmentation","author":"Jadon","year":"2020"},{"issue":"1","key":"10.1016\/j.compag.2025.111192_b13","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/2770706","article-title":"FCN network-based weed and crop segmentation for IoT-aided agriculture applications","volume":"2022","author":"Kamal","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"issue":"6","key":"10.1016\/j.compag.2025.111192_b14","doi-asserted-by":"crossref","first-page":"540","DOI":"10.4097\/kjae.2015.68.6.540","article-title":"T test as a parametric statistic","volume":"68","author":"Kim","year":"2015","journal-title":"Korean J. Anesthesiol."},{"key":"10.1016\/j.compag.2025.111192_b15","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.-Y., et al., 2023. Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 4015\u20134026.","DOI":"10.1109\/ICCV51070.2023.00371"},{"issue":"6","key":"10.1016\/j.compag.2025.111192_b16","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.3390\/agronomy13061503","article-title":"Real-time detection of crops with dense planting using deep learning at seedling stage","volume":"13","author":"Kong","year":"2023","journal-title":"Agronomy"},{"key":"10.1016\/j.compag.2025.111192_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108924","article-title":"AI for crop production\u2013where can large language models (LLMs) provide substantial value?","volume":"221","author":"Kuska","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111192_b18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B., 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.1016\/j.compag.2025.111192_b19","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T., 2015. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"10.1016\/j.compag.2025.111192_b20","series-title":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","first-page":"1","article-title":"Canny edge detection on NVIDIA CUDA","author":"Luo","year":"2008"},{"issue":"1","key":"10.1016\/j.compag.2025.111192_b21","doi-asserted-by":"crossref","first-page":"13601","DOI":"10.1038\/s41598-022-17840-6","article-title":"Intelligent yield estimation for tomato crop using SegNet with VGG19 architecture","volume":"12","author":"Maheswari","year":"2022","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.compag.2025.111192_b22","doi-asserted-by":"crossref","first-page":"26","DOI":"10.2478\/ausi-2018-0002","article-title":"Fruit recognition from images using deep learning","volume":"10","author":"Muresan","year":"2018","journal-title":"Acta Univ. Sapientiae Inform."},{"key":"10.1016\/j.compag.2025.111192_b23","doi-asserted-by":"crossref","first-page":"164546","DOI":"10.1109\/ACCESS.2020.3021739","article-title":"Semantic segmentation of litchi branches using DeepLabV3+ model","volume":"8","author":"Peng","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.compag.2025.111192_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108168","article-title":"GPT-aided diagnosis on agricultural image based on a new light YOLOPC","volume":"213","author":"Qing","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111192_b25","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.compag.2025.111192_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.104129","article-title":"The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis","volume":"128","author":"Salvi","year":"2021","journal-title":"Comput. Biol. Med."},{"issue":"7954","key":"10.1016\/j.compag.2025.111192_b27","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1038\/d41586-023-00816-5","article-title":"GPT-4 is here: what scientists think","volume":"615","author":"Sanderson","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.compag.2025.111192_b28","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C., 2018. Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4510\u20134520.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10.1016\/j.compag.2025.111192_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2024.110821","article-title":"FruitSeg30_Segmentation dataset & mask annotations: A novel dataset for diverse fruit segmentation and classification","volume":"56","author":"Shamrat","year":"2024","journal-title":"Data Brief"},{"issue":"1","key":"10.1016\/j.compag.2025.111192_b30","doi-asserted-by":"crossref","first-page":"20552","DOI":"10.1038\/s41598-024-71013-1","article-title":"Deep learning approach for detecting tomato flowers and buds in greenhouses on 3p2r gantry robot","volume":"14","author":"Singh","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compag.2025.111192_b31","first-page":"105","article-title":"Opening and closing","author":"Soille","year":"2004","journal-title":"Morphol. Image Anal.: Princ. Appl."},{"key":"10.1016\/j.compag.2025.111192_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106150","article-title":"Apple, peach, and pear flower detection using semantic segmentation network and shape constraint level set","volume":"185","author":"Sun","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111192_b33","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","volume":"vol. 31","author":"Szegedy","year":"2017"},{"issue":"1","key":"10.1016\/j.compag.2025.111192_b34","doi-asserted-by":"crossref","first-page":"9716","DOI":"10.1038\/s41598-024-60375-1","article-title":"An improved semantic segmentation algorithm for high-resolution remote sensing images based on DeepLabv3+","volume":"14","author":"Wang","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compag.2025.111192_b35","doi-asserted-by":"crossref","unstructured":"Wang, L., Yang, J., Zhang, Y., Wang, F., Zheng, F., 2024b. Depth-Aware Concealed Crop Detection in Dense Agricultural Scenes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 17201\u201317211.","DOI":"10.1109\/CVPR52733.2024.01628"},{"key":"10.1016\/j.compag.2025.111192_b36","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compag.2025.111192_b37","article-title":"Multi-scale contextual swin transformer for crop image segmentation","volume":"vol. 2759","author":"Xu","year":"2024"},{"key":"10.1016\/j.compag.2025.111192_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.06.001","article-title":"Fruit detection for strawberry harvesting robot in non-structural environment based on mask-RCNN","volume":"163","author":"Yu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.111192_b39","series-title":"2023 11th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","first-page":"1","article-title":"SegFormer-based Cotton Planting Areas extraction from high-resolution remote sensing images","author":"Zhang","year":"2023"}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169925012980?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169925012980?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T23:18:22Z","timestamp":1773011902000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169925012980"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":39,"alternative-id":["S0168169925012980"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2025.111192","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":"SAMConvFormer+LLM: Exploring synergistic fusion of Segment Anything Model with joint convolutional transformer and large language model to advance dense agricultural crop analysis","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2025.111192","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"111192"}}