Tender Leaf Identification for Early-Spring Green Tea Based on Semi-Supervised Learning and Image Processing

Abstract

Tea is one of the most common beverages in the world. Automated machinery that is suitable for plucking high-quality green tea is necessary for tea plantations and the identification of tender leaves is one of the key techniques. In this paper, we proposed a method that combines semi-supervised learning and image processing to identify tender leaves. Both in two-dimensional and three-dimensional space, the three R, G, and B components of tender leaves and their backgrounds were trained and tested. The gradient-descent method and the Adam algorithm were used to optimize the objective function, respectively. The results show that the average accuracy of tender leaf identification is 92.62% and the average misjudgment rate is 18.86%. Our experiments have shown that green tea tender leaves in early spring can be identified effectively using the model based on semi-supervised learning, which has strong versatility and perfect adaptability, so as to improve the problem of deep learning requiring a large number of labeled samples.