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Fresh Tea Shoot Maturity Estimation via Multispectral Imaging and Deep Label Distribution Learning
Bin CHEN JiLi YAN
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2020/09/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
fresh tea shoot maturity estimation, deep label distribution learning, Kullback-Leibler loss function, label ambiguity,
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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.