Fresh Tea Shoot Maturity Estimation via Multispectral Imaging and Deep Label Distribution Learning

Author:

CHEN Bin1,YAN JiLi1

Affiliation:

1. College of Mathematics Physics and Information Engineering, Jiaxing University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Reference21 articles.

1. [1] L. Luo, X. Zou, J. Xiong, et al., “Automatic positioning for picking point of grape picking robot in natural environment,” Trans. Chinese Society of Agricultural Engineering, vol.31, pp.14-21, 2015.

2. [2] G. Ya, “Research on the application of automation software control system in tea garden mechanical picking,” International Conference on Applications and Techniques in Cyber Security and Intelligence, pp.1830-1836, 2019.

3. [3] W. Pei and X. Wang, “The two-dimension coordinates extraction of tea shoots picking based on image information,” Acta Agriculturae Zhejiangensis, vol.28, pp.522-527, 2016.

4. [4] Y. Liang, J. Lu, L. Zhang, S. Wu, and Y. Wu, “Estimation of tea quality by infusion colour difference analysis,” Journal of the Science of Food and Agriculture, vol.85, no.2, pp.286-292, 2005. 10.1002/jsfa.1953

5. [5] A.K. Hazarika, S. Chanda, S. Sabhapondit, S. Sanyal, P. Tamuly, S. Tasrin, D. Sing, B. Tudu, and R. Bandyopadhyay, “Quality assessment of fresh tea leaves by estimating total polyphenols using near infrared spectroscopy,” Journal of Food Science and Technology, vol.55, no.12, pp.4867-4876, 2018. 10.1007/s13197-018-3421-6

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