关键词: 红枣, 深度图像, BP神经网络, 特征提取, 体积测量

Abstract: In order to realize the rapid non⁃destructive measurement of jujube volume and improve the precision of jujube volume classification,a method based on depth image and BP neural network was proposed.By collecting the depth image of jujube,plane segmentation and jujube clustering were carried out by segmentation algorithm,and cylinder fitting of the jujube point cloud after clustering was carried out to establish a 3D model of jujube.Five features,including long diameter,short diameter,contour circumference,projection area and sphericity,were extracted from the 3D model of jujube by using the bounding box algorithm and convex hull method.Seven sets of BP neural network models with different feature combinations were established to predict the volume of jujube.By analyzing the prediction results of seven groups of models,the results showed that the model containing all the characteristics of jujube had the best prediction results.The determination coefficient(R²)of forecasted values and measured values of volume was 0.861 87,the root mean square error(RMSE)was 1.66 mL,and the average relative error between the predicted volume and the measured volume was 6.65%.The results demonstrate that the estimation of jujube volume by depth image and BP neural network has high prediction accuracy.

Key words: Jujube, Depth image, BP neural network, Feature extraction, Volume measurement