Image classification of intelligent farm based on convolutional neural network

Abstract

In order to solve the problem of perception and no decision-making in the agricultural modernization of Xinjiang Corps, an image classification method (TL-DA-SE-CNN) based on attention mechanism module (SENet) and convolutional neural network hybrid model transfer learning is proposed. This method selects four different CNN models for weight acquisition, including VGGNet, ResNet, InceptionNet and MobileNet. The model uses the SENet classifier instead of the fully connected layer of the convolutional neural network, extracts the structural high-order statistical features of the image for topic classification, and uses the BP algorithm to adjust the parameters, with a classification accuracy of 98.20%. Experimental results show that the technology of combining CNN with transfer learning, data augmentation and SENet improves the performance of livestock image classification, which is an effective application of convolutional neural network in farm automation clustering.

Keywords

• deep learning
• convolutional neural network
• data enhancement
• the migration study