ArticleFrontiers in plant science2022
Dragon fruit detection in natural orchard environment by integrating lightweight network and attention mechanism.
Article in Frontiers in plant science, 2022. 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.
- Quality Detection for Dragon Fruit Based on the End-of-Arm Spectral Sensor of the Harvesting Robot.Foods (Basel, Switzerland) · 2026Article
- Lightweight highland barley detection based on improved YOLOv5.Plant methods · 2025Article
- An anchor-based YOLO fruit detector developed on YOLOv5.PloS one · 2025Article
- Real-time and lightweight detection of grape diseases based on Fusion Transformer YOLO.Frontiers in plant science · 2024Article
- Design and experiment of Panax notoginseng root orientation transplanting device based on YOLOv5s.Frontiers in plant science · 2024Article
- A Dragon Fruit Picking Detection Method Based on YOLOv7 and PSP-Ellipse.Sensors (Basel, Switzerland) · 2023Article
- YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor.Frontiers in plant science · 2023Article
- Application of improved YOLOv7-based sugarcane stem node recognition algorithm in complex environments.Frontiers in plant science · 2023Article
- An improved YOLOv5s model using feature concatenation with attention mechanism for real-time fruit detection and counting.Frontiers in plant science · 2023Article
- A simplified network topology for fruit detection, counting and mobile-phone deployment.PloS one · 2023Article
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7 authors.
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Abstract
An improved lightweight network (Improved YOLOv5s) was proposed based on YOLOv5s in this study to realise all-weather detection of dragon fruit in a complex orchard environment. A ghost module was introduced in the original YOLOv5s to realise the lightweight of the model. The coordinate attention mechanism was joined to make the model accurately locate and identify the dense dragon fruits. A bidirectional feature pyramid network was built to improve the detection effect of dragon fruit at different scales. SIoU loss function was adopted to improve the convergence speed during model training. The improved YOLOv5s model was used to detect a dragon fruit dataset collected in the natural environment. Results showed that the mean average precision (
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