Huajia Wang’s scientific contributions

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Publications (2)


Tea Bud Picking Point Localization Method in Natural Environmet Based on Attitude Guidance
  • Preprint

January 2024

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2 Reads

Huajia Wang

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Zilin Xia

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Jinan Gu

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[...]

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Yu Zuo

FIGURE E Comparing computer vision with human vision.
FIGURE Vision-based automatic tea picking machine, reproduced from Li Y. et al. () and Chen et al. ().
A review on the application of computer vision and machine learning in the tea industry
  • Article
  • Full-text available

April 2023

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432 Reads

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45 Citations

Tea is rich in polyphenols, vitamins, and protein, which is good for health and tastes great. As a result, tea is very popular and has become the second most popular beverage in the world after water. For this reason, it is essential to improve the yield and quality of tea. In this paper, we review the application of computer vision and machine learning in the tea industry in the last decade, covering three crucial stages: cultivation, harvesting, and processing of tea. We found that many advanced artificial intelligence algorithms and sensor technologies have been used in tea, resulting in some vision-based tea harvesting equipment and disease detection methods. However, these applications focus on the identification of tea buds, the detection of several common diseases, and the classification of tea products. Clearly, the current applications have limitations and are insufficient for the intelligent and sustainable development of the tea field. The current fruitful developments in technologies related to UAVs, vision navigation, soft robotics, and sensors have the potential to provide new opportunities for vision-based tea harvesting machines, intelligent tea garden management, and multimodal-based tea processing monitoring. Therefore, research and development combining computer vision and machine learning is undoubtedly a future trend in the tea industry.

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Citations (1)


... Although progress has been made in evaluating driving behavior and correlations, existing methods face several limitations. In particular, machine learning models are sensitive to data partitioning and struggle with complexity when handling large-scale, high-dimensional driving data [30][31][32]. This leads to longer computation times and higher demands on computer performance. ...

Reference:

Enhancing Driving Safety Evaluation Through Correlation Analysis of Driver Behavior
A review on the application of computer vision and machine learning in the tea industry