ArticleSensors (Basel, Switzerland)2023
Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Lightweight Detection and Adaptive Path Planning for Selective Hotan Rose Harvesting.Sensors (Basel, Switzerland) · 2026Article
- GCP-YOLO: high-precision detection of tiny chili flowers in complex greenhouse scenes.Frontiers in plant science · 2026Article
- Flora-YOLO: A lightweight framework for real-time rose grading on edge devices via structural optimization and feature refinement.PloS one · 2026Article
- Accurate detection and density estimation of peach tree inflorescences using an improved YOLOv11 model.Frontiers in plant science · 2026Article
- Continuous Picking Path Planning Based on Lightweight Marigold Corollas Recognition in the Field.Biomimetics (Basel, Switzerland) · 2025Article
- A Recognition Method for Marigold Picking Points Based on the Lightweight SCS-YOLO-Seg Model.Sensors (Basel, Switzerland) · 2025Article
- RLK-YOLOv8: multi-stage detection of strawberry fruits throughout the full growth cycle in greenhouses based on large kernel convolutions and improved YOLOv8.Frontiers in plant science · 2025Article
- Metrology-Assisted Production in Agriculture and Forestry.Sensors (Basel, Switzerland) · 2024Article
- UN-YOLOv5s: A UAV-Based Aerial Photography Detection Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5.Sensors (Basel, Switzerland) · 2023Article
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3 authors.
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Abstract
Accurate recognition of the flowering stage is a prerequisite for flower yield estimation. In order to improve the recognition accuracy based on the complex image background, such as flowers partially covered by leaves and flowers with insignificant differences in various fluorescence, this paper proposed an improved CR-YOLOv5s to recognize flower buds and blooms for chrysanthemums by emphasizing feature representation through an attention mechanism. The coordinate attention mechanism module has been introduced to the backbone of the YOLOv5s so that the network can pay more attention to chrysanthemum flowers, thereby improving detection accuracy and robustness. Specifically, we replaced the convolution blocks in the backbone network of YOLOv5s with the convolution blocks from the RepVGG block structure to improve the feature representation ability of YOLOv5s through a multi-branch structure, further improving the accuracy and robustness of detection. The results showed that the average accuracy of the improved CR-YOLOv5s was as high as 93.9%, which is 4.5% better than that of normal YOLOv5s. This research provides the basis for the automatic picking and grading of flowers, as well as a decision-making basis for estimating flower yield.
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