中圖分類(lèi)號(hào)：TP183 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.223297 中文引用格式： 楊詒斌，王俊強(qiáng)，柴世豪. 基于CNN的智慧農(nóng)場(chǎng)圖像分類(lèi)方法[J]. 電子技術(shù)應(yīng)用，2023，49(4)：33-38. 英文引用格式： Yang Yibin，Wang Junqiang，Chai Shihao. Image classification of intelligent farm based on convolutional neural network[J]. Application of Electronic Technique，2023，49(4)：33-38.
Image classification of intelligent farm based on convolutional neural network
Yang Yibin1，2，Wang Junqiang1，2，Chai Shihao1
(1.School of Instrumentation and Electronics， North China University， Taiyuan 030051， China； 2.Institute of Frontier Interdisciplinary Sciences， North China University， Taiyuan 030051， China)
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.
Key words : deep learning；convolutional neural network；data enhancement；the migration study
