# Sorting of Fresh Tea Leaf Using Deep Learning and Air Blowing
## 1 Introduction  
Tea is an important economic crop in China, where the tea cultivation area ranks first in the world and the total value of production is more than CNY 600 billion (USD 86.6 billion). To improve the utilization of tea resources, the machine harvesting of fresh tea leaves has begun in several important tea growing areas. The fresh leaves of machine-picked tea have different specifications, including single leaf (SL), one bud and one leaf (OBOL), one bud and two leaves (OBTL), and one bud and three leaves (OBRL), as well as low uniformity [1]. These four types of tea fresh leaves not only differ in the number of leaves they contain in appearance, but they also differ in the content of internal substances such as L-theamine and catechins. Furthermore, the production of Chinese famous tea has very high requirements for fresh leaf raw materials, for example, Liu'an Gua Pian made only from SL as raw material. Therefore, it is impossible to use machine harvesting in the processing of famous tea products, resulting in a significant decline in economic benefits. There are two main ways to solve the above problem: One is to develop an intelligent harvesting machine for famous tea [2] to realize the intelligent identification of the fresh tea leaf grade of the tea. The other, proposed in this study, is applying the idea of picking and fusion to develop an intelligent sorting system for fresh tea leaf sorting according to the requirements of different types offamous tea. The fresh tea leaves are categorized into SL, OBOL, OBTL, and OBRL, as shown in Figure 1, and correspond to the grading system used in the processing of famous tea. Owing to the complexity and variability of the physical properties of fresh tea leaves, especially the fact that the fresh leaves will continue to lose moisture for some time after harvesting, which leads to significant changes in the physical surface properties of the fresh leaves after harvesting up until the sorting process [3], it is challenging to develop a fast and highly accurate identification system suitable for the fresh tea leaf grades. Computer vision is nondestructive and highly automated, and it has been used in strawberry grading systems [4], fermentation degree monitoring of black tea [5,6], apple sorting systems [7], and in other fields. In recent years, conventional machine learning has been applied to the development of fresh tea leaf grade recognition systems and has achieved some success [8,9]. [PERSON] et al. investigated feature classification methods using an improved genetic algorithm to screen features with the best combination of three different classifiers with the best number of dimensions, and finally a support vector machine (SVM) achieved a recognition accuracy of 97% in the 28-dimensional feature set [10]. [PERSON] et al. extracted the features of tea leaves by also determining the topology of the tea leaf and used SVM to identify the fresh tea leaves based on the sorting; consequently, the recognition accuracy reached 94% [11]. However, it is difficult for machine learning to obtain effective feature values for some complex problems, and there are differences in recognition results when different sorting scenarios are present. Recently, deep learning algorithms have been proven effective in tea pest detection [12, 13, 14], famous tea picker recognition [15], and fresh tea leaf sorting [16]. They have also been successfully applied in product sorting research [17] for date fruit classification [18], grading and sorting of garlics [19], and seed sorting [20,21]. However, given the large number of materials in the fresh tea leaf grading link and the problems of fresh leaf collusion and entanglement, its detection speed and accuracy do not achieve the desired effect. The proposed You Only Look Once (YOLO) model provides unlimited opportunities to solve this problem and has been applied to the development of various agricultural grading systems, such as lotus seed sorting [22], fruit classification [23], and pear fruit sorting [24].