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Recognition of Tobacco Leaf Curing Stage Based on Deep Learning

文献类型： 会议论文

第一作者： Panzhen Zhao

作者： Panzhen Zhao 1 ; Songfeng Wang 2 ; Hao Xia 3 ; Yingpeng Dai 2 ;

作者机构： 1.Key Laboratory of Tobacco Biology and Processing, Institute of Tobacco Research of CAAS Graduate School of Chinese Academy of Agricultural Sciences, Qingdao, China

2.Key Laboratory of Tobacco Biology and Processing, Institute of Tobacco Research of CAAS, Qingdao, China

3.Ji’ an Branch of Jiangxi Provincial Tobacco Company, Ji'an, China

关键词： Deep learning;Training;Accuracy;Process control;Quality control;Curing;Stability analysis

会议名称： International Conference on Electrical Engineering, Control and Robotics

主办单位：

页码： 305-309

摘要： The aim of this study is to improve the intelligent curing technology in Jiangxi and promote its high-quality development. Four different deep learning models, namely AlexNet, VGG16, GoogLeNet, and ResNet18, were used to recognize the typical curing stages of tobacco leaves in Jiangxi. Firstly, a dataset of typical curing stages of tobacco leaves in the Jiangxi region was established. Secondly, the four deep learning models were trained and optimized. Finally, the performance of the four models was evaluated and analyzed. The experimental results showed that all four deep learning models were capable of recognizing the typical curing stages of tobacco leaves in Jiangxi. However, the ResNet18 deep learning model performed the best, achieving an accuracy of 95.80% on the test dataset. In terms of performance, it exhibited higher classification accuracy and stability compared to the other three deep learning models. This study has important practical value and significance for the automated management and quality control of tobacco leaf curing processes in the Jiangxi region.

分类号： tp2

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