{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T09:16:28Z","timestamp":1775034988444,"version":"3.50.1"},"reference-count":49,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100007847","name":"Jilin Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["YD ZJ202401376ZYTS"],"award-info":[{"award-number":["YD ZJ202401376ZYTS"]}],"id":[{"id":"10.13039\/100007847","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,14]]},"DOI":"10.1109\/cw68232.2025.00069","type":"proceedings-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T19:50:39Z","timestamp":1774986639000},"page":"430-437","source":"Crossref","is-referenced-by-count":0,"title":["Research on Grain Recognition Algorithm Based on Class-Balanced Sampling Method"],"prefix":"10.1109","author":[{"given":"Guangjie","family":"Liu","sequence":"first","affiliation":[{"name":"Changchun Normal University,Department of Computer Science and Technology,Changchun,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Diao","sequence":"additional","affiliation":[{"name":"Changchun Normal University,Department of Computer Science and Technology,Changchun,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"107832","article-title":"Mixed-distribution transfer with entropy-based filtering for imbalanced data","volume":"114","author":"Zhang","year":"2021","journal-title":"Pattern Recognition"},{"key":"ref2","first-page":"105917","article-title":"NearMiss-3: A New Undersampling Method for Imbalanced Datasets","volume":"196","author":"Zhang","year":"2020","journal-title":"Knowledge-Based Systems"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00957"},{"key":"ref5","article-title":"Test-Time Augmentation for Class Imbalance","author":"Khan","year":"2024","journal-title":"ICLR"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.20965\/jaciii.2023.p0474"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3147838"},{"key":"ref8","article-title":"An improved pure fully connected neural network with gradient-based dynamic weighting for rice grain classification","author":"Peng","year":"2025","journal-title":"arXiv preprint."},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.02.016"},{"key":"ref10","first-page":"107179","article-title":"Addressing Class Imbalance in Agriculture: A Review of Sampling Strategies, Loss Functions and Ensemble Methods","volume":"200","author":"Miao","year":"2022","journal-title":"Computers and Electronics in Agriculture"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/11538059_91"},{"issue":"2","key":"ref12","first-page":"125","article-title":"Attention-based dynamic weighting in agricultural object detection","volume":"15","author":"Yang","year":"2023","journal-title":"Journal of Agricultural Informatics"},{"key":"ref13","first-page":"3651","article-title":"Conditional DETR for fast training convergence","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Zhou","year":"2022"},{"issue":"1","key":"ref14","first-page":"1","article-title":"Safe-level-SMOTE: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem","volume":"35","author":"Bunkhumpornpat","year":"2012","journal-title":"Knowledge and Information Systems"},{"key":"ref15","article-title":"Meta-Weight-Net++: Learning adaptive weights for long-tailed visual recognition via metalearning","author":"Li","year":"2024","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref16","first-page":"61","article-title":"Test-time augmentation for improving minority class recognition in imbalanced datasets","volume":"169","author":"Zhang","year":"2023","journal-title":"Pattern Recognition Letters"},{"key":"ref17","first-page":"124","article-title":"Space-spectral deep CNN for rice variety classification using hyperspectral imaging","volume":"175","author":"Chatnuntawech","year":"2018","journal-title":"Biosystems Engineering"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107426"},{"issue":"5","key":"ref19","first-page":"2156","article-title":"Gradient-informed sampling for classimbalanced detection","volume":"34","author":"Chen","year":"2023","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02044"},{"key":"ref21","article-title":"Dense Distinct Query DETR (DDQ- DETR)","volume-title":"Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops","author":"Zhang","year":"2023"},{"key":"ref22","first-page":"101765","article-title":"Improving Pest Detection in Crops Using Synthetic Data and Class-Balanced Training","volume":"72","author":"Xu","year":"2024","journal-title":"Ecological Informatics"},{"key":"ref23","first-page":"256","article-title":"Pseudo-labeling with balanced sampling for rare-class detection","volume":"450","author":"Li","year":"2022","journal-title":"Neurocomputing"},{"key":"ref24","article-title":"ResNet deep models and transfer learning technique for classification of rice grain varieties","author":"He","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"ref25","first-page":"106433","article-title":"Object Detection in Agricultural Fields: A Case Study on Wheat and Rice","volume":"190","author":"Chen","year":"2022","journal-title":"Computers and Electronics in Agriculture"},{"key":"ref26","first-page":"12345","article-title":"Class-Balanced Sampling for Imbalanced Agricultural Image Datasets","volume":"10","author":"Wang","year":"2022","journal-title":"IEEE Access"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107426"},{"key":"ref28","first-page":"8452","article-title":"Contrastive cross-modal learning with dynamic class reweighting","volume":"31","author":"Wang","year":"2022","journal-title":"IEEE Transactions on Image Processing"},{"key":"ref29","volume-title":"Class Imbalance Problem in Agricultural Image Classification: A Review","author":"Wang","year":"2024"},{"key":"ref30","first-page":"123","article-title":"Advances in Grain Quality Detection Using Deep Learning Techniques","volume":"112","author":"Li","year":"2024","journal-title":"Trends in Food Science & Technology"},{"key":"ref31","first-page":"103987","article-title":"Object Detection in Agricultural Robotics: A Review of Recent Developments","volume":"150","author":"Chen","year":"2024","journal-title":"Robotics and Autonomous Systems"},{"key":"ref32","first-page":"45","article-title":"Class-Balanced Data Augmentation Strategies for Crop Disease Detection","volume":"168","author":"Zhao","year":"2024","journal-title":"Pattern Recognition Letters"},{"key":"ref33","first-page":"567","article-title":"Class Imbalance in Deep Learning for Agricultural Image Classification: Challenges and Solutions","volume":"36","author":"Zhou","year":"2024","journal-title":"Neural Computing and Applications"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2019.00140"},{"issue":"1","key":"ref35","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/1746-4811-8-34","article-title":"Object Detection for Plant Phenotyping: Recent Advances and Future Directions","volume":"20","author":"Liu","year":"2024","journal-title":"Plant Methods"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2023.1304962"},{"key":"ref37","first-page":"107234","article-title":"Deep Learning Approaches for Grain Crop Disease Detection: A Survey","volume":"202","author":"Chen","year":"2024","journal-title":"Computers and Electronics in Agriculture"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-02660-8"},{"key":"ref39","article-title":"RTMDet: An empirical study of designing real- time object detectors","author":"Lyu","year":"2022","journal-title":"arXiv preprint"},{"key":"ref40","article-title":"Faster R- CNN: Towards real- time object detection with region proposal networks","volume-title":"Proc. Advances in Neural Information Processing Systems (NeurIPS)","volume":"28","author":"Ren","year":"2015"},{"key":"ref41","article-title":"Exploring plain vision transformer backbones for object detection (ViTDet)","volume-title":"Proc. European Conference on Computer Vision (ECCV)","author":"Li","year":"2022"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52729.2023.00708"},{"key":"ref43","article-title":"DINO: DETR with improved denoising anchor boxes for end- to- end object detection","volume-title":"Proc. International Conference on Learning Representations (ICLR)","author":"Zhang","year":"2023"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"ref45","article-title":"Deformable DETR: Deformable transformers for end- to- end object detection","volume-title":"Proc. International Conference on Learning Representations (ICLR)","author":"Zhu","year":"2021"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72970-6_3"},{"key":"ref47","article-title":"EXCB: Enhanced Cross-attention Block for Long-tailed Object Detection","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV)","author":"Xiaoming","year":"2024"},{"key":"ref48","first-page":"11402","article-title":"Seesaw Loss for Long-Tailed Instance Segmentation","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Yuxin","year":"2021"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"}],"event":{"name":"2025 International Conference on Cyberworlds (CW\uff09","location":"Jinzhong, China","start":{"date-parts":[[2025,10,14]]},"end":{"date-parts":[[2025,10,16]]}},"container-title":["2025 International Conference on Cyberworlds (CW\uff09"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11455361\/11455414\/11455474.pdf?arnumber=11455474","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T06:14:54Z","timestamp":1775024094000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11455474\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/cw68232.2025.00069","relation":{},"subject":[],"published":{"date-parts":[[2025,10,14]]}}}