In this study, image segmentation technology is utilized for segmentation of tea leaves and tender buds and deep learning technology is introduced for tea bud classification. Watershed algorithm has good robustness in the field of image segmentation under complex backgrounds, and the key of the algorithm is to determine the image segmentation threshold, which directly affects the accuracy of segmentation. “Maximum Between-Class Variance Method” (Otsu) as a great algorithm that can obtain the global optimal threshold is applied creatively to traditional watershed algorithm in this paper, which we call “Otsu Watershed Algorithm”. Then the structure of the “Squeeze-and-Excitation” (SE) block is adjusted appropriately to improve the feature presentation ability of the network by embedding into several common deep learning models. Extensive experiments demonstrate that this new SE block has superior accuracy and integration capability on challenging dataset and our tea bud dataset.

在这项研究中，图像分割技术被用于茶叶和嫩芽的分割，而深度学习技术被引入到茶芽的分类中。分水岭算法在复杂背景下的图像分割领域具有良好的鲁棒性，算法的关键是确定图像分割阈值，这直接影响分割的准确性。本文将“最大类间方差法”（Otsu）作为一种能够获得全局最优阈值的出色算法，将其创造性地应用于传统的分水岭算法，我们称之为“ Otsu分水岭算法”。然后，将“挤压和激励”（SE）块的结构适当地调整，以通过嵌入到几种常见的深度学习模型中来提高网络的特征表示能力。