关键词: 蜜蜂觅食行为, 农作物授粉, 卷积神经网络, 对比分析, GoogLeNet V1, 传统机器学习

Abstract: Real-time monitoring of bee feeding behavior not only contributes to assess the pollination status of local crops,but also helps to take timely measures to improve the yield and quality of bee products.Computer vision technology was introduced into monitoring of the foraging behavior of bee(whether bee was carrying pollen or not).Five kinds of convolutional neural network(CNN)with differentdepths were designed and comparisons among them were made,and the deep network GoogLeNet V1 was compared with them,to find out the optimal model for monitoring the foraging behavior of bee.The results showed that,the GoogLeNet V1 had the highest recognition accuracy rate(0.953 6),whose training took up the longest time(7 326 s);The model with highest accuracy in shallow CNN was a network with 11 layers containing four convolutional layers,and its recognition accuracy was 0.903 6 with shorter training time (1 054 s).Finally, the results of comparison between CNN and traditional machine learning algorithms proved the distinct advantage of deep learning. In conclusion,the GoogLeNet V1 deep layer network is suitable for monitoring bee environments with high precision requirements and good equipment conditions.The 11-layer CNN designed is more in line with the actual needs of intelligent beekeeping.

Key words: Bee foraging behavior, Crop pollination, Convolutional neural network, Comparative analysis, GoogLeNet V1, Traditional machine learning