Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics

Abstract

Hyperspectral imaging technology was applied to detect and recognize six different varieties of Longjing fresh tea. The data contained image and spectral information at 370–1042 nm; color and texture features were the foci of the image research. Spectral pre‐processing was performed by multiplicative scatter correction (MSC) and standard normal variate (SNV), and then, we selected the corresponding position variable and vegetation indexes as spectral features. Representative features including the most information were chose by principal component analysis (PCA). A novel back propagation (BP) neural network, with a self‐generated number of hidden layer neurons, was proposed. Using spectral features, image features, and spectral image fusion features as input, three fresh tea recognition models were established: the improved BP neural network, traditional BP neural network, and support vector machine (SVM). Results suggested that the improved BP neural network could promote performance of the model, especially for the spectral pre‐processed data. Mixed‐feature models did better than individual feature models, with 100% accuracy of the predictive set. This study shows that hyperspectral imaging technology can be a potential rapid and nondestructive approach to identify different varieties of Longjing fresh teas. Practical applications This article introduced application of hyperspectral imaging technology to identify Longjing fresh tea of different origins and varieties. Samples were analyzed using spectral and image characteristics. We provided a basis for full utilization of tea characteristics. At the same time, an improved BP neural network, with less calculation complexity and workload than the traditional BP neural network, was proposed. In summary, we outlined a convenient and reliable method for differentiation of Longjing fresh teas. Furthermore, we established a theoretical foundation for development of portable instruments to be used in similar studies.

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

“…Pre-processing techniques have been proposed for reducing noise or modifying the base and slope shift from spectral data, and some previous studies showed that they could sharpen the peaks and valleys of spectra [57,58]. Thus, applying pre-processing transformations was considered to enhance the more chemically relevant peaks and reduce the effects of baseline shifts and overall curvature over the original reflectance (OR).…”

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

“…Various studies using near-infrared spectroscopy and hyperspectral imaging to identify tea varieties and geographical origins have been reported. Yan et al [28] used hyperspectral imaging (spectral range: 370-1042 nm) to classify six different varieties of Loingjing fresh tea. The classification accuracy could reach over 94%.…”

A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model

“…Convolutional neural networks, as an important member of image classification algorithms, have the advantages of high recognition accuracy, fast detection speed, and great development potential [ 12 ], have achieved considerable success in image classification [ 13 ], object detection [ 14 ], pose estimation [ 15 ], image segmentation [ 16 ], and face recognition [ 17 , 18 ], have great scaling advantages [ 19 ], and have been widely used in agriculture [ 20 ], healthcare [ 21 ], education [ 22 ], energy [ 23 ], industrial inspection [ 24 ], and other fields [ 25 ]. Currently, convolutional neural networks have been used for tea tree pest and disease identification [ 26 ], tea grade sieving [ 7 ], and the sorting of tea tree fresh leaves [ 8 ], but for the recognition and classification of different species of green tea based on ResNet, a typical convolutional neural network is proposed by researchers in recent years to perform computer vision tasks, which minimizes the gradient disappearance problem caused by increasing the depth of the network due to the introduction of the residual module and reduces the redundancy of information in the data while maintaining a high accuracy rate, which is simple and practical.…”