ArticlePlants (Basel, Switzerland)2022
Medicinal Chrysanthemum Detection under Complex Environments Using the MC-LCNN Model.
Article in Plants (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Lightweight Detection and Adaptive Path Planning for Selective Hotan Rose Harvesting.Sensors (Basel, Switzerland) · 2026Article
- Continuous Picking Path Planning Based on Lightweight Marigold Corollas Recognition in the Field.Biomimetics (Basel, Switzerland) · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Medicinal chrysanthemum detection is one of the desirable tasks of selective chrysanthemum harvesting robots. However, it is challenging to achieve accurate detection in real time under complex unstructured field environments. In this context, we propose a novel lightweight convolutional neural network for medicinal chrysanthemum detection (MC-LCNN). First, in the backbone and neck components, we employed the proposed residual structures MC-ResNetv1 and MC-ResNetv2 as the main network and embedded the custom feature extraction module and feature fusion module to guide the gradient flow. Moreover, across the network, we used a custom loss function to improve the precision of the proposed model. The results showed that under the NVIDIA Tesla V100 GPU environment, the inference speed could reach 109.28 FPS per image (416 × 416), and the detection precision (AP
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Registered trials
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