ArticlePlant phenomics (Washington, D.C.)2023
A Tea Buds Counting Method Based on YOLOv5 and Kalman Filter Tracking Algorithm.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 52 citations in OpenAlex.
- YOLO11-ECA: A Lightweight Tea Bud Detection Method for Complex Tea Plantation Environments.Sensors (Basel, Switzerland) · 2026Article
- Active learning for data-efficient segmentation of tea buds and leaves.Frontiers in plant science · 2026Article
- A crop cultivation monitoring platform for evaluating the early growth of cucumbers.Frontiers in plant science · 2026Article
- Precise Image Color Correction Based on Dual Unmanned Aerial Vehicle Cooperative Flight.Plant phenomics (Washington, D.C.) · 2025Article
- SSP-MambaNet: An automated system for detection and counting of missing seedlings in glass greenhouse-grown virus-free strawberry.Plant phenomics (Washington, D.C.) · 2025Article
- 3D-NOD: 3D new organ detection in plant growth by a spatiotemporal point cloud deep segmentation framework.Plant phenomics (Washington, D.C.) · 2025Article
- Field-deployable lightweight YOLOv8n for real-time detection and counting of Maize seedlings using UAV RGB imagery.Frontiers in plant science · 2025Article
- Tea Bud Detection Model in a Real Picking Environment Based on an Improved YOLOv5.Biomimetics (Basel, Switzerland) · 2024Article
- Visualization of metabolite distribution based on matrix-assisted laser desorption/ionization-mass spectrometry imaging of tea seedlings (Horticulture research · 2024Article
- Article
- One to All: Toward a Unified Model for Counting Cereal Crop Heads Based on Few-Shot Learning.Plant phenomics (Washington, D.C.) · 2024Article
- Small target tea bud detection based on improved YOLOv5 in complex background.Frontiers in plant science · 2024Article
- Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming.Frontiers in plant science · 2024Article
- A method of identification and localization of tea buds based on lightweight improved YOLOV5.Frontiers in plant science · 2024Article
- Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision.Sensors (Basel, Switzerland) · 2023Article
- A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model.Frontiers in plant science · 2023Article
- TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection.Frontiers in plant science · 2023Article
- Semi-supervised Counting of Grape Berries in the Field Based on Density Mutual Exclusion.Plant phenomics (Washington, D.C.) · 2023Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The tea yield estimation provides information support for the harvest time and amount and serves as a decision-making basis for farmer management and picking. However, the manual counting of tea buds is troublesome and inefficient. To improve the efficiency of tea yield estimation, this study presents a deep-learning-based approach for efficiently estimating tea yield by counting tea buds in the field using an enhanced YOLOv5 model with the Squeeze and Excitation Network. This method combines the Hungarian matching and Kalman filtering algorithms to achieve accurate and reliable tea bud counting. The effectiveness of the proposed model was demonstrated by its mean average precision of 91.88% on the test dataset, indicating that it is highly accurate at detecting tea buds. The model application to the tea bud counting trials reveals that the counting results from test videos are highly correlated with the manual counting results (
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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.