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

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

SUBMITTER: He Z 

PROVIDER: S-EPMC12408579 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature