AUTHOR=He Zhangzhang , Chen Xinyue , Gao Ying , Zhang Yu , Guo Yuheng , Zhai Tong , Wei Xiaochen , Li Huan , Zhu Haipeng , Fu Yongkun , Zhang Zhiliang 

TITLE=Fast real-time detection and counting of thrips in greenhouses with multi-level feature attention and fusion

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1663813

DOI=10.3389/fpls.2025.1663813

ISSN=1664-462X

ABSTRACT=Thrips can damage over 200 species across 62 plant families, causing significant economic losses worldwide. Their tiny size, rapid reproduction, and wide host range make them prone to outbreaks, necessitating precise and efficient population monitoring methods. Existing intelligent counting methods lack effective solutions for tiny pests like thrips. In this work, we propose the Thrip Counting and Detection Network (TCD-Net). TCD-Net is an fully convolutional network consisting of a backbone network, a feature pyramid, and an output head. First, we propose a lightweight backbone network, PartialNeXt, which optimizes convolution layers through Partial Convolution (PConv), ensuring both network performance and reduced complexity. Next, we design a lightweight channel-spatial hybrid attention mechanism to further refine multi-scale features, enhancing the model’s ability to extract global and local features with minimal computational cost. Finally, we introduce the Adaptive Feature Mixer Feature Pyramid Network (AFM-FPN), where the Adaptive Feature Mixer (AFM) replaces the traditional element-wise addition at the P level, enhancing the model’s ability to select and retain thrips features, improving detection performance for extremely small objects. The model is trained with the Object Counting Loss (OC Loss) specifically designed for the detection of tiny pests, allowing the network to predict a small spot region for each thrips, enabling real-time and precise counting and detection. We collected a dataset containing over 47K thrips annotations to evaluate the model’s performance. The results show that TCD-Net achieves an F1 score of 85.67%, with a counting result correlation of 75.50%. The model size is only 21.13M, with a computational cost of 114.36 GFLOPs. Compared to existing methods, TCD-Net achieves higher thrips counting and detection accuracy with lower computational complexity. The dataset is publicly available at github.com/ZZL0897/thrip_leaf_dataset.