Compressed CNN Plant Leaf Ｒecognition Model Fused with Bayesian

Abstract

Aiming at the problem that there are many parameters in the process of plant leaf recognition and it is easy to produce over-fitting，in order to reduce the cost of storage and calculation，this paper proposes a plant leaf recognition convolutional neural network model based on Bayesian fusion. Firstly，the recursive Bayesian algorithm is used for network pruning to adaptively remove network redundancy. Then，the convolutional layer and the fully connected layer in the K-Means cluster quantization network are introduced to compress the entire convolutional neural network. Finally，combined with the classical convolutional neural network model AlexNet， plant leaf recognition experiments were carried out. The experimental results show that in the same data set，the compressed convolutional neural network takes less storage space，36 times compression and achieves a slightly better performance with 88. 58% accuracy than the original network and other compression methods.

Keywords

• convolutional neural network
• model compression
• pruning
• parameter quantization
• bayesian algorithm