{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T17:20:08Z","timestamp":1787937608354,"version":"build-2784847793"},"reference-count":60,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010418","name":"IITP","doi-asserted-by":"publisher","award":["IITP-2025-RS-2020-II201789"],"award-info":[{"award-number":["IITP-2025-RS-2020-II201789"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"IITP","doi-asserted-by":"publisher","award":["IITP-2025-RS-2023-00254592"],"award-info":[{"award-number":["IITP-2025-RS-2023-00254592"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019403","name":"Information Technology Research Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100019403","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Plant Phenomics"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1016\/j.plaphe.2025.100098","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T04:01:24Z","timestamp":1755748884000},"page":"100098","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":8,"title":["KDOSS-net: Knowledge distillation-based outpainting and semantic segmentation network for crop and weed images"],"prefix":"10.1016","volume":"7","author":[{"given":"Sang Hyo","family":"Cheong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung Jae","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su Jin","family":"Im","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juwon","family":"Seo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1214-9510","authenticated-orcid":false,"given":"Kang Ryoung","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.plaphe.2025.100098_bib1","series-title":"Proceedings of the 2015 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR); Boston, MA, USA","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"10.1016\/j.plaphe.2025.100098_bib2","doi-asserted-by":"crossref","DOI":"10.34133\/2020\/4152816","article-title":"Convolutional neural networks for image-based high-throughput plant phenotyping: a review","volume":"2020","author":"Jiang","year":"2020","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.plaphe.2025.100098_bib3","series-title":"Proceedings of the 2015 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI); Munich, Germany","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"issue":"7","key":"10.1016\/j.plaphe.2025.100098_bib4","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1049\/ipr2.12447","article-title":"Edge-aware image outpainting with attentional generative adversarial networks","volume":"16","author":"Li","year":"2022","journal-title":"IET Image Process."},{"key":"10.1016\/j.plaphe.2025.100098_bib5","article-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015","journal-title":"arXiv"},{"key":"10.1016\/j.plaphe.2025.100098_bib6","series-title":"Proceedings of the 2016 ASABE International Meeting","article-title":"Image processing for crop\/weed discrimination in fields with high weed pressure","author":"Li","year":"2016"},{"issue":"6","key":"10.1016\/j.plaphe.2025.100098_bib7","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1002\/rob.21675","article-title":"Effective vision-based classification for separating sugar beets and weeds for precision farming","volume":"34","author":"Lottes","year":"2017","journal-title":"J. Field Robot."},{"key":"10.1016\/j.plaphe.2025.100098_bib8","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.compag.2017.07.028","article-title":"Maize and weed classification using color indices with support vector data description in outdoor fields","volume":"141","author":"Zheng","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.plaphe.2025.100098_bib9","series-title":"Proceedings of the International Conference on Agricultural Engineering; Aarhus, Denmark","article-title":"Pixel-wise classification of weeds and crop in images by using a fully convolutional neural network","author":"Dyrmann","year":"2016"},{"key":"10.1016\/j.plaphe.2025.100098_bib10","series-title":"Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS); Madrid, Spain","article-title":"Joint stem detection and crop-weed classification for plant-specific treatment in precision farming","author":"Lottes","year":"2018"},{"key":"10.1016\/j.plaphe.2025.100098_bib11","series-title":"Paper Presented At: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); Honolulu, HI, USA","article-title":"The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation","author":"Jegou","year":"2017"},{"issue":"4","key":"10.1016\/j.plaphe.2025.100098_bib12","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0215676","article-title":"Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields","volume":"14","author":"Ma","year":"2019","journal-title":"PLoS One"},{"issue":"12","key":"10.1016\/j.plaphe.2025.100098_bib13","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.plaphe.2025.100098_bib14","series-title":"Proceedings of the 2019 Computer Analysis of Images and Patterns (CAIP); Salerno, Italy","article-title":"UAV image based crop and weed distribution estimation on embedded GPU boards","author":"Fawakherji","year":"2019"},{"key":"10.1016\/j.plaphe.2025.100098_bib15","series-title":"Proceedings of the 2015 International Conference on Learning Representations (ICLR); San Diego, CA, USA","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2015"},{"key":"10.1016\/j.plaphe.2025.100098_bib16","doi-asserted-by":"crossref","DOI":"10.3389\/frsen.2022.755939","article-title":"Deep convolutional neural networks for weeds and crops discrimination from UAS imagery","volume":"3","author":"Hashemi-Beni","year":"2022","journal-title":"Front. Remote Sensing"},{"key":"10.1016\/j.plaphe.2025.100098_bib17","series-title":"Proceedings of the 2018 European Conference on Computer Vision (ECCV); Munich, Germany","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.plaphe.2025.100098_bib18","series-title":"Proceedings of the 2017 Neural Information Processing Systems (Neurips); Long Beach, CA, USA","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"issue":"1","key":"10.1016\/j.plaphe.2025.100098_bib19","doi-asserted-by":"crossref","first-page":"65","DOI":"10.3390\/s23010065","article-title":"Transformer-based weed segmentation for grass management","volume":"23","author":"Jiang","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.plaphe.2025.100098_bib20","series-title":"Proceedings of the 2021 Neural Information Processing Systems (Neurips); Virtual","article-title":"SegFormer: simple and efficient design for semantic segmentation with transformers","author":"Xie","year":"2021"},{"key":"10.1016\/j.plaphe.2025.100098_bib21","article-title":"ENet: a deep neural network architecture for real-time semantic segmentation","author":"Paszke","year":"2016","journal-title":"arXiv"},{"key":"10.1016\/j.plaphe.2025.100098_bib22","series-title":"Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA); Brisbane, Australia","article-title":"Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNs","author":"Milioto","year":"2018"},{"issue":"10","key":"10.1016\/j.plaphe.2025.100098_bib23","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.3390\/electronics9101602","article-title":"CED-Net: crops and weeds segmentation for smart farming using a small cascaded encoder-decoder architecture","volume":"9","author":"Khan","year":"2020","journal-title":"Electronics"},{"key":"10.1016\/j.plaphe.2025.100098_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107146","article-title":"MTS-CNN: multi-Task semantic segmentation-convolutional neural network for detecting crops and weeds","volume":"199","author":"Kim","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.plaphe.2025.100098_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108862","article-title":"SC-Net: a new strip convolutional network model for rice seedling and weed segmentation in paddy field","volume":"220","author":"Liao","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.plaphe.2025.100098_bib26","series-title":"Proceedings of the 2020 IEEE International Conference for Innovation in Technology (INOCON). Bangalore, India","article-title":"Reduced U-Net architecture for classifying crop and weed using pixel-wise segmentation","author":"Arun","year":"2020"},{"key":"10.1016\/j.plaphe.2025.100098_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106242","article-title":"A modified U-Net with a specific data argumentation method for semantic segmentation of weed images in the field","volume":"187","author":"Zou","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.plaphe.2025.100098_bib28","unstructured":"KDOSS-Net, GitHub; https:\/\/github.com\/geki1004\/KDOSS-Net (accessed on 2025 April 16)."},{"key":"10.1016\/j.plaphe.2025.100098_bib29","series-title":"Proceedings of the 2018 Neural Information Processing Systems (Neurips); Montreal, Canada","article-title":"Image inpainting via generative multi-column convolutional neural networks","author":"Wang","year":"2018"},{"key":"10.1016\/j.plaphe.2025.100098_bib30","series-title":"Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR); Long Beach, CA, USA","article-title":"Wide-context semantic image extrapolation","author":"Wang","year":"2019"},{"key":"10.1016\/j.plaphe.2025.100098_bib31","article-title":"Image outpainting and harmonization using generative adversarial networks","author":"Van Hoorick","year":"2019","journal-title":"arXiv"},{"key":"10.1016\/j.plaphe.2025.100098_bib32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2023.02.021","article-title":"Generalized image outpainting with U-transformer","volume":"162","author":"Gao","year":"2023","journal-title":"Neural Netw."},{"key":"10.1016\/j.plaphe.2025.100098_bib33","series-title":"Paper Presented At: Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV); Montreal, QC, Canada","article-title":"Swin Transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.plaphe.2025.100098_bib34","series-title":"Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV); Seoul, South Korea","article-title":"Free-form image inpainting with gated convolution","author":"Yu","year":"2019"},{"key":"10.1016\/j.plaphe.2025.100098_bib35","series-title":"Paper Presented At: Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV); Seoul, South Korea","article-title":"Boundless: generative adversarial networks for image extension","author":"Krishnan","year":"2019"},{"key":"10.1016\/j.plaphe.2025.100098_bib36","series-title":"Proceedings of the 2017 Neural Information Processing Systems (Neurips); Long Beach, CA, USA;","article-title":"Improved training of Wasserstein GANs","author":"Gulrajani","year":"2017"},{"key":"10.1016\/j.plaphe.2025.100098_bib37","doi-asserted-by":"crossref","first-page":"2526","DOI":"10.1109\/TMM.2021.3086618","article-title":"MFFENet: multiscale feature fusion and enhancement network for RGB-thermal urban road scene parsing","volume":"24","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.plaphe.2025.100098_bib38","doi-asserted-by":"crossref","first-page":"3483","DOI":"10.1109\/TMM.2022.3161852","article-title":"PGDENet: progressive guided fusion and depth enhancement network for RGB-D indoor scene parsing","volume":"25","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Multimed."},{"issue":"1","key":"10.1016\/j.plaphe.2025.100098_bib39","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/TIV.2022.3164899","article-title":"MTANet: Multitask-aware network with hierarchical multimodal fusion for RGB-T urban scene understanding","volume":"8","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Intell. Vehicles"},{"key":"10.1016\/j.plaphe.2025.100098_bib40","series-title":"Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR); Salt Lake City, UT, USA","article-title":"MobileNetV2: inverted residuals and linear bottlenecks","author":"Sandler","year":"2018"},{"key":"10.1016\/j.plaphe.2025.100098_bib41","series-title":"Proceedings of the 2014 Neural Information Processing Systems (Neurips); Montreal, Canada","article-title":"Do deep nets really need to be deep?","author":"Ba","year":"2014"},{"key":"10.1016\/j.plaphe.2025.100098_bib42","article-title":"FitNets: hints for thin deep nets","author":"Romero","year":"2014","journal-title":"arXiv"},{"key":"10.1016\/j.plaphe.2025.100098_bib43","article-title":"Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer","author":"Zagoruyko","year":"2016","journal-title":"arXiv"},{"key":"10.1016\/j.plaphe.2025.100098_bib44","series-title":"Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Honolulu, HI, USA","article-title":"A gift from knowledge distillation: fast optimization, network minimization, and transfer learning","author":"Yim","year":"2017"},{"key":"10.1016\/j.plaphe.2025.100098_bib45","series-title":"Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV); Seoul, South Korea","article-title":"Similarity-preserving knowledge distillation","author":"Tung","year":"2019"},{"key":"10.1016\/j.plaphe.2025.100098_bib46","series-title":"Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV); Montreal, Canada","article-title":"Channel-wise knowledge distillation for dense prediction","author":"Shu","year":"2021"},{"key":"10.1016\/j.plaphe.2025.100098_bib47","series-title":"Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW); Paris, France","article-title":"A simple and generic framework for feature distillation via channel-wise transformation","author":"Liu","year":"2023"},{"key":"10.1016\/j.plaphe.2025.100098_bib48","series-title":"Proceedings of the AAAI Conference Artificial Intelligence; Vancouver, Canada","article-title":"Understanding the role of the projector in knowledge distillation","author":"Miles","year":"2024"},{"key":"10.1016\/j.plaphe.2025.100098_bib49","article-title":"Attention-guided feature distillation for semantic segmentation","author":"Mansourian","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.plaphe.2025.100098_bib50","series-title":"Proceedings of the 2014 European Conference on Computer Vision Workshops (ECCVW); Zurich, Switzerland","first-page":"12","article-title":"A crop\/weed field image dataset for the evaluation of computer vision based precision agriculture tasks","author":"Haug","year":"2014"},{"issue":"10","key":"10.1016\/j.plaphe.2025.100098_bib51","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1177\/0278364917720510","article-title":"Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields","volume":"36","author":"Chebrolu","year":"2017","journal-title":"Int. J. Robot Res."},{"key":"10.1016\/j.plaphe.2025.100098_bib52","series-title":"Paper Presented At: Proceedings of the AAAI Conference on Artificial Intelligence. Virtual","article-title":"Edge-aware guidance fusion network for RGB-thermal scene parsing","author":"Zhou","year":"2022"},{"issue":"12","key":"10.1016\/j.plaphe.2025.100098_bib53","doi-asserted-by":"crossref","first-page":"7096","DOI":"10.1109\/TCSVT.2023.3275314","article-title":"MMSMCNet: modal memory sharing and morphological complementary networks for RGB-T urban scene semantic segmentation","volume":"33","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"issue":"4","key":"10.1016\/j.plaphe.2025.100098_bib54","doi-asserted-by":"crossref","first-page":"3222","DOI":"10.1109\/TCSVT.2024.3508058","article-title":"MDNet: Mamba-effective diffusion-distillation network for RGB-thermal urban dense prediction","volume":"35","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"issue":"11","key":"10.1016\/j.plaphe.2025.100098_bib55","doi-asserted-by":"crossref","first-page":"17610","DOI":"10.1109\/JIOT.2025.3537281","article-title":"Feature contrast difference and enhanced network for RGB-D indoor scene classification in internet of things","volume":"12","author":"Zhou","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.plaphe.2025.100098_bib56","first-page":"1","article-title":"Multiattentive perception and multilayer transfer network using knowledge distillation for RGB-D indoor scene parsing","author":"Zhou","year":"2025","journal-title":"IEEE Transact. Neural Networks Learn. Syst."},{"key":"10.1016\/j.plaphe.2025.100098_bib57","series-title":"Proceedings of the 2017 IEEE\/CVF International Conference on Computer Vision (ICCV); Venice, Italy","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"},{"issue":"4","key":"10.1016\/j.plaphe.2025.100098_bib58","doi-asserted-by":"crossref","first-page":"407","DOI":"10.4103\/aca.ACA_94_19","article-title":"Application of student's t-test, analysis of variance, and covariance","volume":"22","author":"Mishra","year":"2019","journal-title":"Ann. Card Anaesth."},{"issue":"1","key":"10.1016\/j.plaphe.2025.100098_bib59","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1037\/0033-2909.112.1.155","article-title":"A power primer","volume":"112","author":"Cohen","year":"1992","journal-title":"Psychol. Bull."},{"key":"10.1016\/j.plaphe.2025.100098_bib60","series-title":"Paper Presented At: Proceedings of the 2023 Neural Information Processing Systems (Neurips); New Orleans, Louisiana, USA","article-title":"Visual instruction tuning","author":"Liu","year":"2023"}],"container-title":["Plant Phenomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2643651525001049?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2643651525001049?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T21:00:51Z","timestamp":1772917251000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2643651525001049"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":60,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["S2643651525001049"],"URL":"https:\/\/doi.org\/10.1016\/j.plaphe.2025.100098","relation":{},"ISSN":["2643-6515"],"issn-type":[{"value":"2643-6515","type":"print"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"KDOSS-net: Knowledge distillation-based outpainting and semantic segmentation network for crop and weed images","name":"articletitle","label":"Article Title"},{"value":"Plant Phenomics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.plaphe.2025.100098","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. on behalf of Nanjing Agricultural University.","name":"copyright","label":"Copyright"}],"article-number":"100098"}}