ArticleSensors (Basel, Switzerland)2023
Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision.
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 17 papers.
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Who cites it
17 citing papers in PubMed.
- Characteristic wavelength selection for rice blast based on hyperspectral remote sensing and deep convolutional neural networks.Pest management science · 2026Article
- YOLO11-ECA: A Lightweight Tea Bud Detection Method for Complex Tea Plantation Environments.Sensors (Basel, Switzerland) · 2026Article
- Improving RGB image recognition in the YOLO11n algorithm for accurate detection of tea plant diseases.Journal of Zhejiang University. Science. B · 2026Article
- Bulk Tea Shoot Detection and Profiling Method for Tea Plucking Machines Using an RGB-D Camera.Sensors (Basel, Switzerland) · 2025Article
- A computer vision framework for proactive anomaly detection and risk reduction in airport baggage logistics.Scientific reports · 2025Article
- TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements.Sensors (Basel, Switzerland) · 2025Article
- Identification of fresh leaves of Anji White Tea: S-YOLOv10-ASI algorithm fusing asymptotic feature pyra-mid network.PloS one · 2025Article
- Enhancing multilevel tea leaf recognition based on improved YOLOv8n.Frontiers in plant science · 2025Article
- Lightweight tea bud detection method based on improved YOLOv5.Scientific reports · 2024Article
- Tea Bud Detection Model in a Real Picking Environment Based on an Improved YOLOv5.Biomimetics (Basel, Switzerland) · 2024Article
- Vision-Based Localization Method for Picking Points in Tea-Harvesting Robots.Sensors (Basel, Switzerland) · 2024Article
- Enhancing Grapevine Node Detection to Support Pruning Automation: Leveraging State-of-the-Art YOLO Detection Models for 2D Image Analysis.Sensors (Basel, Switzerland) · 2024Article
- A Method for Real-Time Recognition of Safflower Filaments in Unstructured Environments Using the YOLO-SaFi Model.Sensors (Basel, Switzerland) · 2024Article
- YOLOv8-MU: An Improved YOLOv8 Underwater Detector Based on a Large Kernel Block and a Multi-Branch Reparameterization Module.Sensors (Basel, Switzerland) · 2024Article
- Identification of cotton pest and disease based on CFNet- VoV-GCSP -LSKNet-YOLOv8s: a new era of precision agriculture.Frontiers in plant science · 2024Article
- Unveiling the Connection between Microbiota and Depressive Disorder through Machine Learning.International journal of molecular sciences · 2023Article
- Research on the Relative Position Detection Method between Orchard Robots and Fruit Tree Rows.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Tea bud target detection is essential for mechanized selective harvesting. To address the challenges of low detection precision caused by the complex backgrounds of tea leaves, this paper introduces a novel model called Tea-YOLOv8s. First, multiple data augmentation techniques are employed to increase the amount of information in the images and improve their quality. Then, the Tea-YOLOv8s model combines deformable convolutions, attention mechanisms, and improved spatial pyramid pooling, thereby enhancing the model's ability to learn complex object invariance, reducing interference from irrelevant factors, and enabling multi-feature fusion, resulting in improved detection precision. Finally, the improved YOLOv8 model is compared with other models to validate the effectiveness of the proposed improvements. The research results demonstrate that the Tea-YOLOv8s model achieves a mean average precision of 88.27% and an inference time of 37.1 ms, with an increase in the parameters and calculation amount by 15.4 M and 17.5 G, respectively. In conclusion, although the proposed approach increases the model's parameters and calculation amount, it significantly improves various aspects compared to mainstream YOLO detection models and has the potential to be applied to tea buds picked by mechanization equipment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.