Black tea withering moisture detection method based on convolution neural network confidence,Journal of Food Process Engineering

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Black tea withering moisture detection method based on convolution neural network confidence
Journal of Food Process Engineering ( IF 2.7 ) Pub Date : 2020-04-27 , DOI: 10.1111/jfpe.13428
Ting An _{1,

2} , Huan Yu ₁ , Chongshan Yang _{1,

2} , Gaozhen Liang _{1,

2} , Jiayou Chen _{1,

3} , Zonghua Hu ₁ , Bin Hu ₂ , Chunwang Dong ₁

Affiliation

Deep learning method was applied to rapidly and nondestructively predict the moisture content in withered leaves. In this study, a withering moisture detection method based on confidence of convolution neural network (CNN) was proposed. The method used data augmentation to preprocess the original image. The prediction results obtained by the CNN model were compared with the results of traditional partial least squares (PLS) and support vector machine regression (SVR) models. The results clarified that the quantitative prediction model of the moisture content in withering leaves based on the confidence of convolutional neural network has the best prediction performance. The performance parameters of the optimal prediction model: correlation coefficient (R_p), root‐mean‐square error of external verification set (RMSEP) and relative standard deviation (RPD) are 0.9957, 0.0059, and 9.5781, respectively. Compared with traditional linear PLS and nonlinear SVR algorithms, deep learning method can better characterize the correlation between images and moisture. The moisture‐related information in the image can be extracted to a greater degree by the convolution kernel of the convolutional neural network. The model has better generalization, which can rapidly and nondestructively predict the moisture content in withered leaves.

中文翻译：

基于卷积神经网络置信度的红茶枯萎水分检测方法

深度学习方法被用于快速，无损地预测枯萎叶片中的水分含量。提出了一种基于卷积神经网络置信度的枯萎水分检测方法。该方法使用数据增强来预处理原始图像。将CNN模型获得的预测结果与传统的偏最小二乘（PLS）和支持向量机回归（SVR）模型的结果进行比较。结果表明，基于卷积神经网络置信度的枯萎叶片水分定量预测模型具有最佳的预测性能。最优预测模型的性能参数：相关系数（R _p），外部验证集的均方根误差（RMSEP）和相对标准偏差（RPD）分别为0.9957、0.0059和9.5781。与传统的线性PLS和非线性SVR算法相比，深度学习方法可以更好地表征图像与水分之间的相关性。卷积神经网络的卷积核可以更大程度地提取图像中与水分有关的信息。该模型具有更好的泛化能力，可以快速无损地预测枯萎叶片中的水分含量。

更新日期：2020-04-27

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