关键词: 槟榔, 神经网络, 分级, 主成分分析法, 机器视觉

Abstract: Aiming at the problems of low labor productivity and low accuracy of betel nut manual classification, a study on machine vision betel nut classification based on genetic neural network was carried out. Using four kinds of betel nut images as the research object, a 6-layer structure genetic neural network was designed to grade betel nut. Although the classification accuracy was high, the network structure was complex. The method of reducing the dimensionality of the image features and reducing the genetic neural network to a 3-layer structure was studied. The 3-layer neural network was trained and validated with 400 and 100 betel nut images. After adjusting the learning rate of the network, the trained and validated accuracy reached more than 95%. And the accuracy of betel nut classification was 90%. The threelayer genetic neural network after data dimension reduction can realize the realtime betel nut grading, which provides technical support for machine grading.

Key words: betel nut, neural network, grading, principal component analysis, machine vision