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学者姓名：张祎彤
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Abstract ：
作为推进农业农村现代化的重要抓手和建设农业强国的关键路径，现代化大农业发展对实现乡村振兴具有重要意义。基于国家关于东北振兴的战略部署，辽宁省率先提出建设现代化大农业发展先行地的战略定位，积极探索农业高质量发展新模式。文章系统阐释了现代化大农业的理论内涵与典型特征，全面分析辽宁省的资源禀赋优势与产业发展基础，提出了辽宁省未来打造现代化大农业发展先行地的规划布局。基于以上研究内容，为进一步推动现代化大农业发展先行地建设，围绕农业生产体系现代化、经营体系创新、产业体系升级以及农民增收机制构建等关键环节提出政策建议。
Keyword ：
东北全面振兴 东北全面振兴 现代化大农业 现代化大农业 粮食安全 粮食安全 高质量发展 高质量发展
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| GB/T 7714 | 仇焕广 , 张祎彤 . 以现代化大农业发展先行地建设为引领，推动辽宁农业高质量发展 [J]. | 辽宁大学学报(哲学社会科学版) , 2025 , 53 (02) : 1-12 . |
| MLA | 仇焕广 等. "以现代化大农业发展先行地建设为引领，推动辽宁农业高质量发展" . | 辽宁大学学报(哲学社会科学版) 53 . 02 (2025) : 1-12 . |
| APA | 仇焕广 , 张祎彤 . 以现代化大农业发展先行地建设为引领，推动辽宁农业高质量发展 . | 辽宁大学学报(哲学社会科学版) , 2025 , 53 (02) , 1-12 . |
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Abstract ：
After rough planing, defects such as wormholes and small patches of green bark residue and decay are often overlooked and misclassified. Strip-like defects, including splinters and chipped edges, are easily confused with the natural bamboo grain, and a single elongated defect is frequently fragmented into multiple detection boxes. This study proposes a modified TASNet-YOLO model, an improved detector built on YOLO11n. Unlike prior YOLO-based bamboo defect detectors, TASNet-YOLO is a mechanism-guided redesign that jointly targets two persistent failure modes-limited visibility of small, low-contrast defects and fragmentation of elongated defects-while remaining feasible for real-time production settings. In the backbone, a newly designed TriMAD_Conv module is introduced as the core unit, enhancing the detection of wormholes as well as small-area defects such as green bark residue and decay. The additive-gated C3k2_AddCGLU is further integrated at selected C3k2 stages. The combination of additive interaction and CGLU improves channel selection and detail retention, highlighting differences between splinters and chipped edges and bamboo grain strips, thereby reducing false positives and improving precision. In the neck, the neck replaces nearest-neighbor upsampling and CBS with SNI-GSNeck to improve cross-scale alignment and fusion. Under an acceptable real-time budget, predictions for splinters and chipped edges become more contiguous and better aligned to edges, while wormholes predictions are more circular and less noisy. Experiments on our in-house dataset (8445 bamboo-strip defect images) show that, compared with YOLO11n, the proposed model improves detection accuracy by 5.1%, achieves 106.4 FPS, and reduces computational costs by 0.4 GFLOPs per forward pass. These properties meet the throughput demand of 2 m/s conveyor lines, and the compact model size and compute footprint make edge deployment straightforward for fast online screening and preliminary quality grading in industrial production.
Keyword ：
AddCGLU AddCGLU bamboo strips bamboo strips defect detection defect detection multi-scale feature fusion multi-scale feature fusion YOLO11 YOLO11
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| GB/T 7714 | Zhang, Yitong , Gao, Rui , Ji, Min et al. TASNet-YOLO: An Identification and Classification Model for Surface Defects of Rough Planed Bamboo Strips [J]. | FORESTS , 2025 , 16 (10) . |
| MLA | Zhang, Yitong et al. "TASNet-YOLO: An Identification and Classification Model for Surface Defects of Rough Planed Bamboo Strips" . | FORESTS 16 . 10 (2025) . |
| APA | Zhang, Yitong , Gao, Rui , Ji, Min , Zhang, Wei , Yu, Wenquan , Wang, Xiangfeng . TASNet-YOLO: An Identification and Classification Model for Surface Defects of Rough Planed Bamboo Strips . | FORESTS , 2025 , 16 (10) . |
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