English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Tea chrysanthemum detection at its flowering stage is one of the keycomponents for selective chrysanthemum harvesting robot development. However,it is a challenge to detect flowering chrysanthemums under unstructured fieldenvironments given the variations on illumination, occlusion and object scale.In this context, we propose a highly fused and lightweight deep learningarchitecture based on YOLO for tea chrysanthemum detection (TC-YOLO). First, inthe backbone component and neck component, the method uses the Cross-StagePartially Dense Network (CSPDenseNet) as the main network, and embeds customfeature fusion modules to guide the gradient flow. In the final head component,the method combines the recursive feature pyramid (RFP) multiscale fusionreflow structure and the Atrous Spatial Pyramid Pool (ASPP) module with cavityconvolution to achieve the detection task. The resulting model was tested on300 field images, showing that under the NVIDIA Tesla P100 GPU environment, ifthe inference speed is 47.23 FPS for each image (416 * 416), TC-YOLO canachieve the average precision (AP) of 92.49% on our own tea chrysanthemumdataset. In addition, this method (13.6M) can be deployed on a single mobileGPU, and it could be further developed as a perception system for a selectivechrysanthemum harvesting robot in the future.

Tea Chrysanthemum Detection under Unstructured Environments Using the TC-YOLO Model