High-throughput end-to-end aphid honeydew excretion behavior recognition method based on rapid adaptive motion-feature fusion.

ABSTRACT:

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 behavio

SUBMITTER: Song Z 

PROVIDER: S-EPMC12277367 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature