# The Application Status and Trends of Machine Vision in Tea Production
## 4 Production Management of Tea  
### Quality Evaluation and Grading of Tea  
In recent years, the application of machine vision to the task of tea variety recognition and quality detection has gradually increased and much has been achieved. In the process of tea production, the grading of tea buds fundamentally determines the quality of the product. Currently, this is mainly achieved through artificial sensory evaluation methods [57], and the results obtained are easily influenced by human subjectivity and environmental factors. Nowadays, although computer vision technology continues to intersect with agricultural engineering disciplines, existing research on tea bud classification is still very limited. In 2003, [PERSON] et al. [58] first studied tea soup color images and analyzed them using traditional computer image processing methods such as histogram dissimilarity measurements and single-level artificial neural network classification. The final classification rates reached 74.67% and 80%. In 2016, [PERSON] [59] used a BP neural network to construct a tea classifier to test three types of color and shape features, with an accuracy of over 90%. [PERSON] et al. [60] classified the quality of green tea by first using the mean shift method to divide the image into multiple different clustering regions, and then selecting representative regions from these larger regions. Then, using the method of region merging, the elliptical model was used to effectively classify the tea leaves of green tea; in 2018, [PERSON] et al. [61] established a Qimen black tea classification model based on shape feature histograms and support vector machines. They extracted eight geometric features, including absolute and relative geometric features, and generated shape feature histograms. The histogram distribution was input as a feature vector into the classifier for classification, and the combination with LSSVM had the best classification effect, with an accuracy of 95.71% for Qimen black tea classification. With the gradual application of spectral technology, [PERSON] et al. [62] and others tried to evaluate the quality of green tea in South Asia and East Asia through ultraviolet (UV) visible, Fourier transform infrared spectroscopy (FTIR), high-performance liquid chromatography (HPLC), and other methods, combined with some chemometric methods. UV spectral data combined with chemometrics can effectively achieve the authentication and differentiation of green tea samples from South Asia and East Asia. [PERSON] et al. [63] combined multispectral images with chemometrics and used threshold segmentation and manual sampling methods to remove the image background and construct spectral features, demonstrating a significant correlation between the spectral features of the fresh tea leaf canopy and tea quality parameters. [PERSON] et al. [64] introduced a reflectance-based multispectral imaging system to detect sugar adulteration in black tea instead of conventional methods, including and titrimetric methods, which are destructive, time-consuming and expensive. [PERSON] et al. [65] built a light-weight tea bud grading model by embedding a multiscale convolutional block attention module into the ShuffleNet v20.5x network and introducing multiple dimensional scaling (MDS). By focusing on the feature information in the tea samples, the grading accuracy of each sample in the test set reached 100%, 92.70%, and 89.89%, demonstrating better comprehensive performance than using complex network models; this has significant promise for guiding future deployment of networks in low-performance or mobile devices. Identifying the variety of tea plants is of great importance in quality inspection and resource protection in the tea industry. [PERSON] et al. [66] achieved discrimination of 16 types of high-yield tea plant varieties using a multispectral camera. The successive projection algorithm (SPA) was used to analyze the original parameters.