Biye Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Biye Li has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Bo Ma
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
1 shared publication - Hongjian Zhang
College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China
1 shared publication - Jinxing Wang
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
1 shared publication - Junlin Mu
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
1 shared publication - Rongsen Xie
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
1 shared publication - Ruofei Liu
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
1 shared publication
Latest Publications
- Open Access
- 22 October 2025
- Published by MDPI AG in Horticulturae
- Vol. 11 (11) , 1273
MDAS-YOLO: A Lightweight Adaptive Framework for Multi-Scale and Dense Pest Detection in Apple Orchards
Accurate monitoring of orchard pests is vital for green and efficient apple production. Yet images captured by intelligent pest-monitoring lamps often contain small targets, weak boundaries, and crowded scenes, which hamper detection accuracy. We present MDAS-YOLO, a lightweight detection framework tailored for smart pest monitoring in apple orchards. At the input stage, we adopt the LIME++ enhancement to mitigate low illumination and non-uniform lighting, improving image quality at the source. On the model side, we integrate three structural innovations: (1) a C3k2-MESA-DSM module in the backbone to explicitly strengthen contours and fine textures via multi-scale edge enhancement and dual-domain feature selection; (2) an AP-BiFPN in the neck to achieve adaptive cross-scale fusion through learnable weighting and differentiated pooling; and (3) a SimAM block before the detection head to perform zero-parameter, pixel-level saliency re-calibration, suppressing background redundancy without extra computation. On a self-built apple-orchard pest dataset, MDAS-YOLO attains 95.68% mAP, outperforming YOLOv11n by 6.97 percentage points while maintaining a superior trade-off among accuracy, model size, and inference speed. Overall, the proposed synergistic pipeline—input enhancement, early edge fidelity, mid-level adaptive fusion, and end-stage lightweight re-calibration—effectively addresses small-scale, weak-boundary, and densely distributed pests, providing a promising and regionally validated approach for intelligent pest monitoring and sustainable orchard management, and offering methodological insights for future multi-regional pest monitoring research.
- 22 October 2025
- Published by MDPI AG in Horticulturae
- Vol. 11 (11) , 1273
Open AccessMDAS-YOLO: A Lightweight Adaptive Framework for Multi-Scale and Dense Pest Detection in Apple Orchards
Accurate monitoring of orchard pests is vital for green and efficient apple production. Yet images captured by intelligent pest-monitoring lamps often contain small targets, weak boundaries, and crowded scenes, which hamper detection accuracy. We present MDAS-YOLO, a lightweight detection framework tailored for smart pest monitoring in apple orchards. At the input stage, we adopt the LIME++ enhancement to mitigate low illumination and non-uniform lighting, improving image quality at the source. On the model side, we integrate three structural innovations: (1) a C3k2-MESA-DSM module in the backbone to explicitly strengthen contours and fine textures via multi-scale edge enhancement and dual-domain feature selection; (2) an AP-BiFPN in the neck to achieve adaptive cross-scale fusion through learnable weighting and differentiated pooling; and (3) a SimAM block before the detection head to perform zero-parameter, pixel-level saliency re-calibration, suppressing background redundancy without extra computation. On a self-built apple-orchard pest dataset, MDAS-YOLO attains 95.68% mAP, outperforming YOLOv11n by 6.97 percentage points while maintaining a superior trade-off among accuracy, model size, and inference speed. Overall, the proposed synergistic pipeline—input enhancement, early edge fidelity, mid-level adaptive fusion, and end-stage lightweight re-calibration—effectively addresses small-scale, weak-boundary, and densely distributed pests, providing a promising and regionally validated approach for intelligent pest monitoring and sustainable orchard management, and offering methodological insights for future multi-regional pest monitoring research.