{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T08:17:15Z","timestamp":1783671435203,"version":"3.55.0"},"reference-count":65,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2023QC116"],"award-info":[{"award-number":["ZR2023QC116"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2023MF048"],"award-info":[{"award-number":["ZR2023MF048"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021QC173"],"award-info":[{"award-number":["ZR2021QC173"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong Province","doi-asserted-by":"publisher","award":["2024RZB0206"],"award-info":[{"award-number":["2024RZB0206"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010887","name":"Weifang University of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010887","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.asoc.2026.115533","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T23:39:15Z","timestamp":1779493155000},"page":"115533","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Dual-phase transfer learning with swin transformer for automated vegetable disease detection in facility environments"],"prefix":"10.1016","volume":"201","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8769-5981","authenticated-orcid":false,"given":"Jun","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3155-4623","authenticated-orcid":false,"given":"Xuewei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2026.115533_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.jviromet.2021.114388","article-title":"Reverse transcription recombinase polymerase amplification assay for rapid detection of the cucurbit chlorotic yellows virus","volume":"300","author":"Zang","year":"2022","journal-title":"J. Virol. Methods"},{"key":"10.1016\/j.asoc.2026.115533_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107701","article-title":"A review of core agricultural robot technologies for crop productions","volume":"206","author":"Yang","year":"2023","journal-title":"Comput. Electron. Agric."},{"issue":"3","key":"10.1016\/j.asoc.2026.115533_bib3","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0230023","article-title":"Detection of melon necrotic spot virus by one-step reverse transcription loop-mediated isothermal amplification assay","volume":"15","author":"Qiao","year":"2020","journal-title":"Plos One"},{"issue":"5","key":"10.1016\/j.asoc.2026.115533_bib4","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1111\/jph.13080","article-title":"First report of Colletotrichum black leaf spot on strawberry caused by Colletotrichum siamense in China","volume":"170","author":"Wang","year":"2022","journal-title":"J. Phytopathol."},{"key":"10.1016\/j.asoc.2026.115533_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105701","article-title":"A detection and severity estimation system for generic diseases of tomato greenhouse plants","volume":"178","author":"Wspanialy","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.asoc.2026.115533_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106558","article-title":"Towards automated greenhouse: A state of the art review on greenhouse monitoring methods and technologies based on internet of things","volume":"191","author":"Li","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.asoc.2026.115533_bib7","doi-asserted-by":"crossref","first-page":"3361","DOI":"10.3389\/fpls.2021.773142","article-title":"Style-consistent image translation: A novel data augmentation paradigm to improve plant disease recognition","volume":"12","author":"Xu","year":"2022","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.asoc.2026.115533_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.105117","article-title":"Unsupervised image translation using adversarial networks for improved plant disease recognition","volume":"168","author":"Nazki","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.asoc.2026.115533_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106378","article-title":"EFDet: An efficient detection method for cucumber disease under natural complex environments","volume":"189","author":"Liu","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.asoc.2026.115533_bib10","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, J., Yu, L., Dong, H., Yun, K., & Wang, Y. (2021). Dba_ssd: a novel end-to-end object detection using deep attention module for hel** smart device with vegetable and fruit leaf plant disease detection.","DOI":"10.21203\/rs.3.rs-166579\/v1"},{"key":"10.1016\/j.asoc.2026.115533_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106101","article-title":"A vegetable disease recognition model for complex background based on region proposal and progressive learning","volume":"184","author":"Zhou","year":"2021","journal-title":"Comput. Electron. Agric."},{"issue":"4","key":"10.1016\/j.asoc.2026.115533_bib12","doi-asserted-by":"crossref","first-page":"827","DOI":"10.3390\/electronics12040827","article-title":"Design of Efficient Methods for the Detection of Tomato Leaf Disease Utilizing Proposed Ensemble CNN Model","volume":"12","author":"Uluta\u015f","year":"2023","journal-title":"Electronics"},{"key":"10.1016\/j.asoc.2026.115533_bib13","first-page":"1","article-title":"Multiple diseases and pests detection based on federated learning and improved faster R-CNN","volume":"71","author":"Deng","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"10.1016\/j.asoc.2026.115533_bib14","article-title":"Deep convolution neural network based solution for detecting plant diseases","volume":"13","author":"Kumar","year":"2022","journal-title":"J. Pharm. Negat. Results"},{"issue":"4","key":"10.1016\/j.asoc.2026.115533_bib15","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1111\/nph.18056","article-title":"High-throughput measurement of plant fitness traits with an object detection method using Faster R-CNN","volume":"234","author":"Wang","year":"2022","journal-title":"N. Phytol."},{"key":"10.1016\/j.asoc.2026.115533_bib16","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2022.101886","article-title":"An improved Faster R-CNN model for multi-object tomato maturity detection in complex scenarios","volume":"72","author":"Wang","year":"2022","journal-title":"Ecol. Inform."},{"issue":"7","key":"10.1016\/j.asoc.2026.115533_bib17","doi-asserted-by":"crossref","first-page":"1700","DOI":"10.3390\/agronomy13071700","article-title":"Veg-DenseCap: Dense Captioning Model for Vegetable Leaf Disease Images","volume":"13","author":"Sun","year":"2023","journal-title":"Agronomy"},{"issue":"1","key":"10.1016\/j.asoc.2026.115533_bib18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big data"},{"issue":"10","key":"10.1016\/j.asoc.2026.115533_bib19","doi-asserted-by":"crossref","first-page":"439","DOI":"10.3390\/agriculture10100439","article-title":"Transfer learning-based search model for hot pepper diseases and pests","volume":"10","author":"Yin","year":"2020","journal-title":"Agriculture"},{"key":"10.1016\/j.asoc.2026.115533_bib20","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.procs.2020.03.225","article-title":"ToLeD: Tomato leaf disease detection using convolution neural network","volume":"167","author":"Agarwal","year":"2020","journal-title":"Procedia Comput. Sci."},{"issue":"1","key":"10.1016\/j.asoc.2026.115533_bib21","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s41348-020-00403-0","article-title":"Automated tomato leaf disease classification using transfer learning-based deep convolution neural network","volume":"128","author":"Thangaraj","year":"2021","journal-title":"J. Plant Dis. Prot."},{"key":"10.1016\/j.asoc.2026.115533_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106279","article-title":"Tomato plant disease detection using transfer learning with C-GAN synthetic images","volume":"187","author":"Abbas","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.asoc.2026.115533_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.106703","article-title":"Identification method of vegetable diseases based on transfer learning and attention mechanism","volume":"193","author":"Zhao","year":"2022","journal-title":"Comput. Electron. Agric."},{"issue":"17","key":"10.1016\/j.asoc.2026.115533_bib24","doi-asserted-by":"crossref","first-page":"8467","DOI":"10.3390\/app12178467","article-title":"Automatic detection of tomato diseases using deep transfer learning","volume":"12","author":"Khasawneh","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.asoc.2026.115533_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108408","article-title":"Known and unknown class recognition on plant species and diseases","volume":"215","author":"Meng","year":"2023","journal-title":"Comput. Electron. Agric."},{"issue":"1","key":"10.1016\/j.asoc.2026.115533_bib26","doi-asserted-by":"crossref","first-page":"139","DOI":"10.3390\/agriculture13010139","article-title":"Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks","volume":"13","author":"Saeed","year":"2023","journal-title":"Agriculture"},{"key":"10.1016\/j.asoc.2026.115533_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106098","article-title":"Few-shot vegetable disease recognition model based on image text collaborative representation learning","volume":"184","author":"Wang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.asoc.2026.115533_bib28","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2021.731688","article-title":"A plant disease recognition method based on fusion of images and graph structure text","volume":"12","author":"Wang","year":"2022","journal-title":"Front. Plant Sci."},{"issue":"20","key":"10.1016\/j.asoc.2026.115533_bib29","doi-asserted-by":"crossref","first-page":"58293","DOI":"10.1007\/s11042-023-17824-2","article-title":"Efficient plant disease identification using few-shot learning: a transfer learning approach","volume":"83","author":"Uskaner Hepsa\u011f","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.asoc.2026.115533_bib30","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2022.1037655","article-title":"Data-centric annotation analysis for plant disease detection: Strategy, consistency, and performance","volume":"13","author":"Dong","year":"2022","journal-title":"Front. Plant Sci."},{"issue":"1","key":"10.1016\/j.asoc.2026.115533_bib31","doi-asserted-by":"crossref","DOI":"10.1016\/j.plaphe.2025.100024","article-title":"PlantCaFo: An efficient few-shot plant disease recognition method based on foundation models","volume":"7","author":"Jiang","year":"2025","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.asoc.2026.115533_bib32","unstructured":"Dosovitskiy, A. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929."},{"issue":"4","key":"10.1016\/j.asoc.2026.115533_bib33","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/j.eng.2019.04.012","article-title":"Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives","volume":"5","author":"Qi","year":"2019","journal-title":"Engineering"},{"key":"10.1016\/j.asoc.2026.115533_bib34","first-page":"12042","article-title":"Towards robust vision transformer","author":"Mao","year":"2022","journal-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit."},{"issue":"1","key":"10.1016\/j.asoc.2026.115533_bib35","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","article-title":"A survey on vision transformer","volume":"45","author":"Han","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115533_bib36","unstructured":"Mehta, S., & Rastegari, M. (2021). Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv preprint arXiv:2110.02178."},{"key":"10.1016\/j.asoc.2026.115533_bib37","first-page":"10012","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021","journal-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis."},{"key":"10.1016\/j.asoc.2026.115533_bib38","unstructured":"Hughes, D., & Salath\u00e9, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060."},{"issue":"6","key":"10.1016\/j.asoc.2026.115533_bib39","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1109\/TLA.2018.8444395","article-title":"Annotated Plant Pathology Databases for Image-Based Detection and Recognition of Diseases","volume":"16","author":"Barbedo","year":"2018","journal-title":"IEEE Lat. Am. Trans."},{"key":"10.1016\/j.asoc.2026.115533_bib40","doi-asserted-by":"crossref","unstructured":"Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., & Batra, N. (2019). Plantdoc: a dataset for visual plant disease detection.","DOI":"10.1145\/3371158.3371196"},{"key":"10.1016\/j.asoc.2026.115533_bib41","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1109\/TIP.2021.3049334","article-title":"Plant disease recognition: A large-scale benchmark dataset and a visual region and loss reweighting approach","volume":"30","author":"Liu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.asoc.2026.115533_bib42","article-title":"Dataset of Tomato Leaves","volume":"V1","author":"Huang","year":"2020","journal-title":"Mendeley Data"},{"key":"10.1016\/j.asoc.2026.115533_bib43","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2023.1225409","article-title":"Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning","volume":"14","author":"Xu","year":"2023","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.asoc.2026.115533_bib44","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109835","article-title":"WCG-VMamba: A multi-modal classification model for corn disease","volume":"230","author":"Wang","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.asoc.2026.115533_bib45","article-title":"Crop-conditional semantic segmentation for efficient agricultural disease assessment","volume":"5","author":"Picon","year":"2025","journal-title":"Artif. Intell. Agric."},{"key":"10.1016\/j.asoc.2026.115533_bib46","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109734","article-title":"Deep learning for plant stress detection: A comprehensive review","volume":"229","author":"Paul","year":"2025","journal-title":"Comput. Electron. Agric."},{"issue":"2","key":"10.1016\/j.asoc.2026.115533_bib47","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.13786","article-title":"Intelligent Computing for Crop Monitoring in CIoT: Leveraging AI and Big Data Technologies","volume":"42","author":"Ahmed","year":"2025","journal-title":"Expert Syst."},{"key":"10.1016\/j.asoc.2026.115533_bib48","doi-asserted-by":"crossref","DOI":"10.1016\/j.eja.2024.127400","article-title":"Research on tomato disease image recognition method based on DeiT","volume":"162","author":"Sun","year":"2025","journal-title":"Eur. J. Agron."},{"issue":"4","key":"10.1016\/j.asoc.2026.115533_bib49","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"10.1016\/j.asoc.2026.115533_bib50","first-page":"10012","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021","journal-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis."},{"key":"10.1016\/j.asoc.2026.115533_bib51","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"10.1016\/j.asoc.2026.115533_bib52","first-page":"3","article-title":"Cbam: Convolutional block attention module","author":"Woo","year":"2018","journal-title":"Proc. Eur. Conf. Comput. Vis. (ECCV)"},{"key":"10.1016\/j.asoc.2026.115533_bib53","first-page":"1","article-title":"AD-YOLO: A real-time YOLO network with Swin Transformer and attention mechanism for airport scene detection","volume":"73","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"3","key":"10.1016\/j.asoc.2026.115533_bib54","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TVT.2024.3496513","article-title":"HCLT-YOLO: A hybrid CNN and lightweight Transformer architecture for object detection in complex traffic scenes","volume":"74","author":"Chen","year":"2025","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10.1016\/j.asoc.2026.115533_bib55","doi-asserted-by":"crossref","DOI":"10.1016\/j.aej.2025.01.032","article-title":"Object detection in real-time video surveillance using attention based transformer-YOLOv8 model","author":"Nimma","year":"2025","journal-title":"Alex. Eng. J."},{"key":"10.1016\/j.asoc.2026.115533_bib56","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s11554-025-01785-w","article-title":"FA-YOLO: A YOLO model based on attention mechanism and feature fusion for object detection in multi-classification underwater datasets","volume":"22","author":"Wang","year":"2025","journal-title":"J. Real. -Time Image Process."},{"key":"10.1016\/j.asoc.2026.115533_bib57","doi-asserted-by":"crossref","first-page":"7392","DOI":"10.1109\/TMM.2025.3599020","article-title":"Fusion-Mamba for cross-modality object detection","volume":"27","author":"Dong","year":"2025","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.asoc.2026.115533_bib58","article-title":"Hybrid-YOLO: Lightweight Mamba-Transformer hybrid with multi-scale fusion for real-world traffic detection","author":"Wang","year":"2025","journal-title":"ICT Express"},{"issue":"Part A","key":"10.1016\/j.asoc.2026.115533_bib59","article-title":"A synthetic aperture radar small ship detector based on transformers and multi-dimensional parallel feature extraction","volume":"137","author":"Fu","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115533_bib60","unstructured":"Song, H., Sun, D., Chun, S., Jampani, V., Han, D., Heo, B., \u2026 & Yang, M.H. (2021). Vidt: An efficient and effective fully transformer-based object detector. arXiv preprint arXiv:2110.03921."},{"key":"10.1016\/j.asoc.2026.115533_bib61","series-title":"European conference on computer vision","first-page":"213","article-title":"End-to-end object detection with transformers","author":"Carion","year":"2020"},{"key":"10.1016\/j.asoc.2026.115533_bib62","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., & Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159."},{"key":"10.1016\/j.asoc.2026.115533_bib63","first-page":"3651","article-title":"Conditional detr for fast training convergence","author":"Meng","year":"2021","journal-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis."},{"key":"10.1016\/j.asoc.2026.115533_bib64","first-page":"18558","article-title":"Lite DETR: An Interleaved Multi-Scale Encoder for Efficient DETR","author":"Li","year":"2023","journal-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)"},{"key":"10.1016\/j.asoc.2026.115533_bib65","article-title":"DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection","author":"Zhang","year":"2023","journal-title":"Int. Conf. Learn. Represent. (ICLR)"}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626009816?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626009816?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T07:49:48Z","timestamp":1783669788000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494626009816"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":65,"alternative-id":["S1568494626009816"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115533","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Dual-phase transfer learning with swin transformer for automated vegetable disease detection in facility environments","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115533","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":"115533"}}