TeaBudNet: A Lightweight Framework for Robust Small Tea Bud Detection in Outdoor Environments via Weight-FPN and Adaptive Pruning

作者：Yi Li, Zeming Zhang, Jie Zhang, Jingsha Shi, Xiaoyang Zhu, Bing‐Yu Chen, Lan Yi, Yanling Jiang, Wanyi Cai, Xianming Tan, Zhaohong Lu, Hailin Peng, Dandan Tang, Yaning Zhu, Liqiang Tan, Kunhong Li, Feng Yang, Chenyao Yang · 发表于：Agronomy · 年份：2025 · DOI：10.3390/agronomy15081990 · 被引用次数：4 · 研究领域：Advanced Chemical Sensor Technologies、Smart Agriculture and AI

The accurate detection of tea buds in outdoor environments is crucial for the intelligent management of modern tea plantations. However, this task remains challenging due to the small size of tea buds and the limited computational capabilities of the edge devices commonly used in the field. Existing object detection models are typically burdened by high computational costs and parameter loads while often delivering suboptimal accuracy, thus limiting their practical deployment. To address these challenges, we propose TeaBudNet, a lightweight and robust detection framework tailored for small tea bud identification under outdoor conditions. Central to our approach is the introduction of Weight-FPN, an enhanced variant of the BiFPN designed to preserve fine-grained spatial information, thereby improving detection sensitivity to small targets. Additionally, we incorporate a novel P2 detection layer that integrates high-resolution shallow features, enhancing the network’s ability to capture detailed contour information critical for precise localization. To further optimize efficiency, we present a Group–Taylor pruning strategy, which leverages Taylor expansion to perform structured, non-global pruning. This strategy ensures a consistent layerwise evaluation while significantly reducing computational overhead. Extensive experiments on a self-built multi-category tea dataset demonstrate that TeaBudNet surpasses state-of-the-art models, achieving +5.0% gains in AP@50 while reducing pa...