{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:01:51Z","timestamp":1790694111476,"version":"4.1.0"},"reference-count":56,"publisher":"Elsevier BV","issue":"3","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":[[2026,6,14]],"date-time":"2026-06-14T00:00:00Z","timestamp":1781395200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003627","name":"Rural Development Administration","doi-asserted-by":"publisher","award":["RS-2026-25505437"],"award-info":[{"award-number":["RS-2026-25505437"]}],"id":[{"id":"10.13039\/501100003627","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":[[2026,9]]},"DOI":"10.1016\/j.plaphe.2026.100242","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:23:32Z","timestamp":1782231812000},"page":"100242","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"title":["MoCoProto: Enhancing few-shot pest image classification with self-supervised representation learning"],"prefix":"10.1016","volume":"8","author":[{"given":"Dong","family":"Jin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helin","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianghua","family":"Piao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0002-9386","authenticated-orcid":false,"given":"Yeong Hyeon","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.plaphe.2026.100242_bib1","series-title":"The Europa Directory of International Organizations 2021","first-page":"297","article-title":"Food and agriculture organization of the United Nations\u2014FAO","author":"Canton","year":"2021"},{"key":"10.1016\/j.plaphe.2026.100242_bib2","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.cropro.2003.10.001","article-title":"Safeguarding production\u2014losses in major crops and the role of crop protection","volume":"23","author":"Oerke","year":"2004","journal-title":"Crop Prot."},{"key":"10.1016\/j.plaphe.2026.100242_bib3","series-title":"Smart Agricultural Technology","article-title":"Unravelling the use of artificial intelligence in management of insect pests","author":"Kariyanna","year":"2024"},{"key":"10.1016\/j.plaphe.2026.100242_bib4","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1111\/jen.13223","article-title":"Leveraging machine learning for advancing insect pest control: a bibliometric analysis","volume":"149","author":"Wang","year":"2025","journal-title":"J. Appl. Entomol."},{"key":"10.1016\/j.plaphe.2026.100242_bib5","first-page":"13","article-title":"Pest management using machine learning algorithms: a review","volume":"8","author":"Durgabai","year":"2018","journal-title":"Int. J. Comput. Sci. Eng. Inf. Technol. Res."},{"key":"10.1016\/j.plaphe.2026.100242_bib6","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2025.1538163","article-title":"Leveraging deep learning for plant disease and pest detection: a comprehensive review and future directions","volume":"16","author":"Shoaib","year":"2025","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.plaphe.2026.100242_bib7","doi-asserted-by":"crossref","first-page":"42277","DOI":"10.1007\/s11042-023-15221-3","article-title":"Survey on crop pest detection using deep learning and machine learning approaches","volume":"82","author":"Chithambarathanu","year":"2023","journal-title":"Multimed. Tool. Appl."},{"key":"10.1016\/j.plaphe.2026.100242_bib8","doi-asserted-by":"crossref","first-page":"2193","DOI":"10.1007\/s11063-022-10978-4","article-title":"Intelligent identification of jute pests based on transfer learning and deep convolutional neural networks","volume":"55","author":"Sourav","year":"2023","journal-title":"Neural Process. Lett."},{"key":"10.1016\/j.plaphe.2026.100242_bib9","article-title":"EfficientNetV2 model for plant disease classification and Pest recognition","volume":"45","author":"Devi","year":"2023","journal-title":"Comput. Syst. Sci. Eng."},{"key":"10.1016\/j.plaphe.2026.100242_bib10","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s11760-021-01909-2","article-title":"PlantDiseaseNet: convolutional neural network ensemble for plant disease and pest detection","volume":"16","author":"Turkoglu","year":"2022","journal-title":"Signal Image Video Process"},{"key":"10.1016\/j.plaphe.2026.100242_bib11","doi-asserted-by":"crossref","first-page":"6018","DOI":"10.1002\/jsfa.13462","article-title":"EResNet-SVM: an overfitting-relieved deep learning model for recognition of plant diseases and pests","volume":"104","author":"Xiong","year":"2024","journal-title":"J. Sci. Food Agric."},{"key":"10.1016\/j.plaphe.2026.100242_bib12","first-page":"3008","article-title":"Overview of pest detection and recognition algorithms","volume":"13","author":"Guo","year":"2024","journal-title":"Electronics (Basel)"},{"key":"10.1016\/j.plaphe.2026.100242_bib13","doi-asserted-by":"crossref","first-page":"354","DOI":"10.3390\/rs15020354","article-title":"Remote sensing in field crop monitoring: a comprehensive review of sensor systems, data analyses and recent advances","volume":"15","author":"Omia","year":"2023","journal-title":"Remote Sens (Basel)"},{"key":"10.1016\/j.plaphe.2026.100242_bib14","doi-asserted-by":"crossref","first-page":"5032","DOI":"10.3390\/s23115032","article-title":"PlantInfoCMS: scalable plant disease information collection and management system for training AI models","volume":"23","author":"Jin","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.plaphe.2026.100242_bib15","first-page":"1","article-title":"Generalizing from a few examples: a survey on few-shot learning","volume":"53","author":"Wang","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.plaphe.2026.100242_bib16","first-page":"5149","article-title":"Meta-learning in neural networks: a survey","volume":"44","author":"Hospedales","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.plaphe.2026.100242_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105240","article-title":"Few-shot cotton pest recognition and terminal realization","volume":"169","author":"Li","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.plaphe.2026.100242_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106055","article-title":"Meta-learning baselines and database for few-shot classification in agriculture","volume":"182","author":"Li","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.plaphe.2026.100242_bib19","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.3390\/agronomy12081733","article-title":"Insect pest image recognition: a few-shot machine learning approach including maturity stages classification","volume":"12","author":"Gomes","year":"2022","journal-title":"Agronomy"},{"key":"10.1016\/j.plaphe.2026.100242_bib20","doi-asserted-by":"crossref","first-page":"6880","DOI":"10.3390\/s23156880","article-title":"A multi-layer feature fusion method for few-shot image classification","volume":"23","author":"Gomes","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.plaphe.2026.100242_bib21","series-title":"Fifth Workshop on Meta-Learning at the Conference on Neural Information Processing Systems","article-title":"On the role of pre-training for meta few-shot learning","author":"Chen","year":"2021"},{"key":"10.1016\/j.plaphe.2026.100242_bib22","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4367","article-title":"Dynamic few-shot visual learning without forgetting","author":"Gidaris","year":"2018"},{"key":"10.1016\/j.plaphe.2026.100242_bib23","article-title":"A closer look at few-shot classification","author":"Chen","year":"2019","journal-title":"arXiv Preprint arXiv:1904.04232"},{"key":"10.1016\/j.plaphe.2026.100242_bib24","article-title":"Simpleshot: revisiting nearest-neighbor classification for few-shot learning","author":"Wang","year":"2019","journal-title":"arXiv Preprint arXiv:1911.04623"},{"key":"10.1016\/j.plaphe.2026.100242_bib25","series-title":"Computer Vision\u2013ECCV 2020: 16Th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XIV 16","first-page":"266","article-title":"Rethinking few-shot image classification: a good embedding is all you need?","author":"Tian","year":"2020"},{"key":"10.1016\/j.plaphe.2026.100242_bib26","article-title":"Matching networks for one shot learning","volume":"29","author":"Vinyals","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.plaphe.2026.100242_bib27","article-title":"Meta-learning for semi-supervised few-shot classification","author":"Ren","year":"2018","journal-title":"arXiv Preprint arXiv:1803.00676"},{"key":"10.1016\/j.plaphe.2026.100242_bib28","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"1399","article-title":"Multi-domain few-shot learning and dataset for agricultural applications","author":"Nuthalapati","year":"2021"},{"key":"10.1016\/j.plaphe.2026.100242_bib29","series-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"248","article-title":"Imagenet: a large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"10.1016\/j.plaphe.2026.100242_bib30","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2022.1033544","article-title":"Prior knowledge auxiliary for few-shot pest detection in the wild","volume":"13","author":"Wang","year":"2023","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.plaphe.2026.100242_bib31","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2024.3415112","article-title":"A survey on self-supervised learning: algorithms, applications, and future trends","author":"Gui","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.plaphe.2026.100242_bib32","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3733","article-title":"Unsupervised feature learning via non-parametric instance discrimination","author":"Wu","year":"2018"},{"key":"10.1016\/j.plaphe.2026.100242_bib33","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2536","article-title":"Context encoders: feature learning by inpainting","author":"Pathak","year":"2016"},{"key":"10.1016\/j.plaphe.2026.100242_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107448","article-title":"Self-supervised transformer-based pre-training method using latent semantic masking auto-encoder for Pest and disease classification","volume":"203","author":"Liu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.plaphe.2026.100242_bib35","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.plaphe.2026.100242_bib36","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8787","article-title":"Ip102: a large-scale benchmark dataset for insect Pest recognition","author":"Wu","year":"2019"},{"key":"10.1016\/j.plaphe.2026.100242_bib37","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","first-page":"70","article-title":"Weakly supervised learning guided by activation mapping applied to a novel citrus Pest benchmark","author":"Bollis","year":"2020"},{"key":"10.1016\/j.plaphe.2026.100242_bib38","doi-asserted-by":"crossref","DOI":"10.1002\/ppj2.20079","article-title":"Self-supervised learning improves classification of agriculturally important insect pests in plants","volume":"6","author":"Kar","year":"2023","journal-title":"Plant Phenome J."},{"key":"10.1016\/j.plaphe.2026.100242_bib39","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"9588","article-title":"With a little help from my friends: nearest-neighbor contrastive learning of visual representations","author":"Dwibedi","year":"2021"},{"key":"10.1016\/j.plaphe.2026.100242_bib40","first-page":"21271","article-title":"Bootstrap your own latent-a new approach to self-supervised learning","volume":"33","author":"Grill","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.plaphe.2026.100242_bib41","series-title":"International Conference on Machine Learning","first-page":"12310","article-title":"Barlow twins: self-supervised learning via redundancy reduction","author":"Zbontar","year":"2021"},{"key":"10.1016\/j.plaphe.2026.100242_bib42","series-title":"ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1745","article-title":"Self-supervised learning for few-shot image classification","author":"Chen","year":"2021"},{"key":"10.1016\/j.plaphe.2026.100242_bib43","article-title":"Learning representations by maximizing mutual information across views","volume":"32","author":"Bachman","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.plaphe.2026.100242_bib44","series-title":"The caltech-ucsd birds-200-2011 Dataset","author":"Wah","year":"2011"},{"key":"10.1016\/j.plaphe.2026.100242_bib45","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122173","article-title":"SSL-ProtoNet: self-supervised learning prototypical networks for few-shot learning","volume":"238","author":"Lim","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.plaphe.2026.100242_bib46","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"Snell","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.plaphe.2026.100242_bib47","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.3390\/s21051601","article-title":"Agripest: a large-scale domain-specific benchmark dataset for practical agricultural Pest detection in the wild","volume":"21","author":"Wang","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.plaphe.2026.100242_bib48","series-title":"International Conference on Learning Representations","article-title":"Optimization as a model for few-shot learning","author":"Ravi","year":"2017"},{"key":"10.1016\/j.plaphe.2026.100242_bib49","article-title":"Representation learning with contrastive predictive coding","author":"van den Oord","year":"2018","journal-title":"arXiv Preprint arXiv:1807.03748"},{"key":"10.1016\/j.plaphe.2026.100242_bib50","article-title":"Improved baselines with momentum contrastive learning","author":"Chen","year":"2020","journal-title":"arXiv Preprint arXiv:2003.04297"},{"key":"10.1016\/j.plaphe.2026.100242_bib51","first-page":"12","article-title":"Average hashing for perceptual image similarity in mobile phone application","volume":"4","author":"Haviana","year":"2016","journal-title":"J. Telematics Informatics"},{"key":"10.1016\/j.plaphe.2026.100242_bib52","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/TBDATA.2019.2921572","article-title":"Billion-scale similarity search with GPUs","volume":"7","author":"Johnson","year":"2019","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.plaphe.2026.100242_bib53","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8808","article-title":"Few-shot learning via embedding adaptation with set-to-set functions","author":"Ye","year":"2020"},{"key":"10.1016\/j.plaphe.2026.100242_bib54","series-title":"International Conference on Machine Learning","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"Finn","year":"2017"},{"key":"10.1016\/j.plaphe.2026.100242_bib55","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"9062","article-title":"Meta-baseline: exploring simple meta-learning for few-shot learning","author":"Chen","year":"2021"},{"key":"10.1016\/j.plaphe.2026.100242_bib56","doi-asserted-by":"crossref","first-page":"7789","DOI":"10.1109\/TCSVT.2023.3282777","article-title":"Few-shot learning meets transformer: unified query-support transformers for few-shot classification","volume":"33","author":"Wang","year":"2023","journal-title":"IEEE Trans. Circ. Syst. Video Technol."}],"container-title":["Plant Phenomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2643651526000798?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2643651526000798?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T14:50:53Z","timestamp":1790693453000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2643651526000798"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":56,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["S2643651526000798"],"URL":"https:\/\/doi.org\/10.1016\/j.plaphe.2026.100242","relation":{},"ISSN":["2643-6515"],"issn-type":[{"value":"2643-6515","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MoCoProto: Enhancing few-shot pest image classification with self-supervised representation learning","name":"articletitle","label":"Article Title"},{"value":"Plant Phenomics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.plaphe.2026.100242","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 Nanjing Agricultural University.","name":"copyright","label":"Copyright"}],"article-number":"100242"}}