appliedsciences  
Design of a Moisture Content Detection System for Yinghong No. 9 Tea Leaves Based on Machine Vision  
[PERSON] 1, [PERSON] 2, [PERSON] 2, [PERSON] 2, [PERSON] 2,3, [PERSON] 4 and [PERSON] 2,*  
Footnote 23: https://www. moisture content of the tea leaves [4]. [PERSON] et al. used hyperspectral technology to classify and predict whether it was the front or the back of the tea leaves. The prediction accuracy of the front of the tea leaves was 0.951, and the prediction accuracy of the back was 0.918 [5]. However, whether it is near-infrared spectroscopy, hyperspectral technology, or chemical determination methods, the detection cost is relatively high, the efficiency is low, and they are only suitable for laboratories; moreover, it will be difficult to deploy them in production. With the rapid development of computers, machine vision technology has received widespread attention again. Machine vision involves many cutting-edge fields, such as artificial intelligence, image processing, and pattern recognition. The low-cost and high-efficiency performance of machine vision makes it widely used in the field of agriculture [6]. Therefore, this technology has also begun to be applied in the field of tea leaves detection. [PERSON] et al. researched the role of illumination in the discrimination of tea samples based on the textural features of tea granules, which showed that the best discrimination was obtained with darkfield illumination, with a variance of 96%, whereas brightfield illumination showed low discrimination, with only an 83% variance [7]. [PERSON] et al. used multispectral and depth images to establish the regression model of tea moisture content, which showed that the prediction accuracies of the front and back of the tea leaves were 0.77 and 0.68, respectively [8]. [PERSON] et al. designed an automatic tea-category identification (TCI) system to identify green, oolong, and black teas, which used a 3 CCD digital camera [9]. [PERSON] et al. used the NDT (nondestructive testing) method to detect the moisture content of stacked leaves through linear PLS (partial least squares) and nonlinear SVM (support vector machine) [10]. [PERSON] et al. applied deep learning methods to predict the moisture content rapidly and nondestructively in withered leaves, and the results showed that deep learning methods can better characterize the correlation between the images and the moisture than traditional linear PLS and nonlinear SVR algorithms [11]. However, most of these research objects are single, fresh leaves or finished tea, and there is no research on the moisture content of stacked tea leaves of Yinghong No. 9. At present, the neural network method for moisture content prediction has become very common, among which the backpropagation (BP) neural network shows good performance in dealing with nonlinear problems [12; 13]. However, the BP neural network easily falls into the local optimal value. Improper assignments will lead to a decline in the prediction accuracy of the model during initialization. [PERSON] et al. established the BP-ANN model to monitor the relationship between the drying parameters and the state of moisture under different microwave vacuum drying conditions for different typical fruits and vegetables [14]. [PERSON] et al. studied and investigated the effects of edible coating and drying temperature on the properties of dehydrated pineapple cubes through artificial neural networks (ANNs) and multiple linear regression (MLR) [15]. When there are too many neurons in the input layer, the parameters of the model will increase, and the prediction speed will decrease. Therefore, we designed a moisture content detection system for the stacking of Yinghong No. 9 tea leaves based on machine vision. The color-texture method was used to extract the image features of Yinghong No. 9 tea leaves. Two different Yinghong No. 9 tea leaves databases were constructed based on linear discriminant analysis (LDA) and principal component analysis (PCA).