IEICE TRANSACTIONS on Information
Fresh Tea Shoot Maturity Estimation via Multispectral Imaging and Deep Label Distribution Learning
Summary :
Fresh Tea Shoot Maturity Estimation (FTSME) is the basement of automatic tea picking technique, determines whether the shoot can be picked. Unfortunately, the ambiguous information among single labels and uncontrollable imaging condition lead to a low FTSME accuracy. A novel Fresh Tea Shoot Maturity Estimating method via multispectral imaging and Deep Label Distribution Learning (FTSME-DLDL) is proposed to overcome these issues. The input is 25-band images, and the output is the corresponding tea shoot maturity label distribution. We utilize the multiple VGG-16 and auto-encoding network to obtain the multispectral features, and learn the label distribution by minimizing the Kullback-Leibler divergence using deep convolutional neural networks. The experimental results show that the proposed method has a better performance on FTSME than the state-of-the-art methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.9 pp.2019-2022
- Publication Date
- 2020/09/01
- Publicized
- 2020/06/01
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDL8038
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Bin CHEN, JiLi YAN, "Fresh Tea Shoot Maturity Estimation via Multispectral Imaging and Deep Label Distribution Learning" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 9, pp. 2019-2022, September 2020, doi: 10.1587/transinf.2020EDL8038.
Abstract: Fresh Tea Shoot Maturity Estimation (FTSME) is the basement of automatic tea picking technique, determines whether the shoot can be picked. Unfortunately, the ambiguous information among single labels and uncontrollable imaging condition lead to a low FTSME accuracy. A novel Fresh Tea Shoot Maturity Estimating method via multispectral imaging and Deep Label Distribution Learning (FTSME-DLDL) is proposed to overcome these issues. The input is 25-band images, and the output is the corresponding tea shoot maturity label distribution. We utilize the multiple VGG-16 and auto-encoding network to obtain the multispectral features, and learn the label distribution by minimizing the Kullback-Leibler divergence using deep convolutional neural networks. The experimental results show that the proposed method has a better performance on FTSME than the state-of-the-art methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8038/_p
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@ARTICLE{e103-d_9_2019,
author={Bin CHEN, JiLi YAN, },
journal={IEICE TRANSACTIONS on Information},
title={Fresh Tea Shoot Maturity Estimation via Multispectral Imaging and Deep Label Distribution Learning},
year={2020},
volume={E103-D},
number={9},
pages={2019-2022},
abstract={Fresh Tea Shoot Maturity Estimation (FTSME) is the basement of automatic tea picking technique, determines whether the shoot can be picked. Unfortunately, the ambiguous information among single labels and uncontrollable imaging condition lead to a low FTSME accuracy. A novel Fresh Tea Shoot Maturity Estimating method via multispectral imaging and Deep Label Distribution Learning (FTSME-DLDL) is proposed to overcome these issues. The input is 25-band images, and the output is the corresponding tea shoot maturity label distribution. We utilize the multiple VGG-16 and auto-encoding network to obtain the multispectral features, and learn the label distribution by minimizing the Kullback-Leibler divergence using deep convolutional neural networks. The experimental results show that the proposed method has a better performance on FTSME than the state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.2020EDL8038},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - Fresh Tea Shoot Maturity Estimation via Multispectral Imaging and Deep Label Distribution Learning
T2 - IEICE TRANSACTIONS on Information
SP - 2019
EP - 2022
AU - Bin CHEN
AU - JiLi YAN
PY - 2020
DO - 10.1587/transinf.2020EDL8038
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2020
AB - Fresh Tea Shoot Maturity Estimation (FTSME) is the basement of automatic tea picking technique, determines whether the shoot can be picked. Unfortunately, the ambiguous information among single labels and uncontrollable imaging condition lead to a low FTSME accuracy. A novel Fresh Tea Shoot Maturity Estimating method via multispectral imaging and Deep Label Distribution Learning (FTSME-DLDL) is proposed to overcome these issues. The input is 25-band images, and the output is the corresponding tea shoot maturity label distribution. We utilize the multiple VGG-16 and auto-encoding network to obtain the multispectral features, and learn the label distribution by minimizing the Kullback-Leibler divergence using deep convolutional neural networks. The experimental results show that the proposed method has a better performance on FTSME than the state-of-the-art methods.
ER -
English
