High-Throughput End-to-End Aphid Honeydew Excretion Behavior Recognition Method Based on Rapid Adaptive Motion-Feature Fusion

Zhongqiang Song¹Jiahao Shen¹Qiaoyi Liu¹Wanyue Zhang²Ziqian Ren¹Kaiwen Yang¹Xinle Li¹Jialei Liu³Fengming Yan³Wenqiang Li¹Yuqing Xing¹Lili Wu^1*

• ¹Henan Agricultural University, Zhengzhou, Henan Province, China
• ²City University of Hong Kong, Kowloon, Hong Kong, SAR China
• ³College of Plant Protection, Henan Agricultural University, Zhengzhou, Henan Province, China

Introduction: Aphids are significant agricultural pests and vectors of plant viruses. Their Honeydew Excretion(HE) behavior holds critical importance for investigating feeding activities and evaluating plant resistance levels. Addressing the challenges of suboptimal efficiency, inadequate real-time capability, and cumbersome operational procedures inherent in conventional manual and chemical detection methodologies, this research introduces an end-to-end multi-target behavior detection framework. This framework integrates spatiotemporal motion features with deep learning architectures to enhance detection accuracy and operational efficacy.Methods: This study established the first fine-grained dataset encompassing aphid Crawling Locomotion(CL), Leg Flicking(LF), and HE behaviors, offering standardized samples for algorithm training. A rapid adaptive motion feature fusion algorithm was developed to accurately extract highgranularity spatiotemporal motion features. Simultaneously, the RT-DETR detection model underwent deep optimization: a spline-based adaptive nonlinear activation function was introduced, and the Kolmogorov-Arnold network was integrated into the deep feature stage of the ResNet50 backbone network to form the RK50 module. These modifications enhanced the model's capability to capture complex spatial relationships and subtle features.Results and discussion: Experimental results demonstrated that the proposed framework achieved an average precision of 85.9%. Compared with the model excluding the RK50 module, the mAP50 improved by 2.9%, and its performance in detecting small-target honeydew significantly surpassed mainstream algorithms. This study presents an innovative solution for automated monitoring of aphids' fine-grained behaviors and provides a reference for insect behavior recognition research. The datasets, codes, and model weights were made available on GitHub(https://github.com/kuieless/RAMF-Aphid-Honeydew-Excretion-Behavior-Recognition).