By improving the MobileNetV2 network, the obtained MobileNetV2-Tea model has an accuracy of 99% in the recognition of fresh tea images. The experimental results ...
The experimental results show that the MobileNetV2-Tea model can perform fast recognition of fresh tea leaves images under offline conditions.
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Nov 28, 2024 · In this study, we propose the YOLOV5M-SBSD tea bud lightweight detection model to address the above issues.
The model employs a Dynamic Head (DyHead), which enhances tea bud feature extraction through three types of perceptual attention mechanisms—scale, spatial, and ...
A lightweight deep learning model for tea bud recognition based on Yolov5 is proposed. It greatly reduces the amount of calculation and parameters of the model.
The proposed device is capable of non-destructive detection of tea polyphenol content in fresh tea leaves, which can provide effective technical support for tea ...
Missing: lightweight | Show results with:lightweight
Dec 20, 2024 · This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees.
We propose a novel lightweight network named YOLOv8-RCAA (YOLOv8-RepVGG-CBAM-Anchorfree-ATSS), aiming to locate and detect tea leaf diseases with high accuracy ...
Oct 10, 2024 · This offers promising prospects for lightweight models to efficiently perform real-time tea leaves detection tasks.
Missing: Rapid | Show results with:Rapid
Oct 22, 2024 · The improved YOLOv8 model in this study outperformed several other mainstream deep-learning models such as YOLOv5, YOLOX, Faster RCNN, and SSD ...