Enhanced YOLOv5s with BiFormer attention for citrus spring shoot detection: Optimized phenological period and cross-regional application

作者：Wenhuan Liu, Chuan Jian, Shu-ao Zhang, Guo-xun Cong, Hongbo Wang, Jingyi Li, Xiao-meng Li, Jun Tang, Yanyan Ma, Yongqiang Zheng · 发表于：Smart Agricultural Technology · 年份：2025 · DOI：10.1016/j.atech.2025.101446 · 被引用次数：1 · 研究领域：Smart Agriculture and AI、CCD and CMOS Imaging Sensors、Image Processing Techniques and Applications

• Proposed an enhanced YOLOv5s-BiFormer model that outperforms mainstream lightweight detectors (YOLOv7-tiny, YOLOv8s, YOLOv12s, YOLOv13s, SSD) in classifying citrus SSWF (spring shoots with flowers) and SSWOF (spring shoots without flowers) under complex field conditions; it features a compact parameter size (7.3 M), moderate computational cost (26.1 G Flops), and stable real-time speed (104 FPS), fully meeting the deployment requirements of resource-constrained devices. • Identified the optimal phenological window for precise spring shoot recognition (14 to 4 days before full bloom) via 10 temporal grouping schemes and segmented regression analysis, achieving >90% precision and >75% recall to streamline monitoring efficiency. • Developed a real-time Android application based on Flask and cloud architecture, enabling spring shoot recognition and quantification within 5–8 seconds across device tiers, bridging model development and on-site deployment. • Established a cross-regionally robust monitoring system (SSWF mAP: 95.0%–99.3%; SSWOF mAP: 91.0%–99.1% across six citrus-producing regions), providing data-driven guidance for refined orchard management. Orchard production management is closely tied to key phenological stages of tree development, where accurate monitoring of emerging organs (e.g., citrus spring shoots) is critical for agricultural decision-making and production efficiency. However, real-time precise detection faces challenges from dynamic morphological variatio...