基于卷积神经网络的鲜茶叶智能分选系统研究

机采鲜茶叶中混有各种等级的茶叶,针对风选、筛选等分选方法难以做到精确细分的问题,结合计算机视觉技术和深度学习方法,设计了一套鲜茶叶智能分选系统,搭建了基于7层结构的卷积神经网络识别模型,通过共享权值和逐渐下降的学习速率,提高了卷积神经网络的训练性能。经过实验验证,该分选系统可以实现鲜茶叶的自动识别和分选,识别正确率不低于90%,可对鲜茶叶中的单芽、一芽一叶、一芽二叶、一芽三叶、单片叶、叶梗进行有效的类别分选。

Intelligent Fresh-tea-leaves Sorting System Research Based on Convolution Neural Network

Tea is a high-value crop throughout the world. Most fresh tea leaves are picked by machines, then various grades are mixed together including broken leaves and leaf stalks. In order to improve quality, the fresh tea leaves picked by machines need to be further classified. However, traditional methods such as winnowing and screening can only sort tea leaves roughly. A new kind of intelligent fresh-tea-leaf sorting system was proposed based on computer vision technology and deep learning method, which can identify and sort tea leaves automatically and accurately. In this system, convolution neural network (CNN) was used to recognize the images of fresh tea leaves, and there was a seven-layer network structure in the CNN identification model. Through image segmentation and scale transformation, the original image was normalized as the input of CNN. CNN was able to learn the characteristics of images independently and can avoid many complicated feature extraction. The preprocessed images were rotated and mapped to serve as the training set, which enhanced the generalization ability of CNN identification model. Meanwhile, the training performance was greatly improved by sharing weights and using a declining learning rate. Experiment results showed that the system can effectively sort out several kinds of tea leaves, single bud, a bud with a leaf, a bud with two leaves, a bud with three leaves, single leaf and leaf stalk. The identification accuracy was more than 90%.