1. 基于 BPNN 的烤烟褐变程度识别及其分类烟叶质量分析.
- Author
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孙光伟, 郭青青, 柳 均, 冯 吉, 孙敬国, 张鹏龙, 吴哲宽, 李建平, and 陈振国
- Subjects
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LEAF color , *BACK propagation , *TOBACCO , *EVALUATION methodology , *ENZYMATIC browning - Abstract
【Objective】The present paper aimed to establish a BP (Back propagation) neural network (BPNN) automatic recognition system to achieve the standardization and quantification of flue-cured tobacco browning. 【Method】Taken the upper tobacco leaves of Yunyan 87 as a sample, scanning to obtain characteristic information such as tobacco leaf color, established a BP neural network to identify the degree of browning of flue-cured tobacco, and output the discrimination results, and compared the similarity with the results of artificially discriminating the browning of flue-cured tobacco as a reference. Through the analysis and comparison of appearance quality, conventional chemical composition, polyphenol content, TSNAs content and sensory quality changes, the accuracy of the BP neural network automatic recognition system and manual recognition results were veriofied.【Result】Establishing the BP neural network model could accurately identify tobacco browning level, and its recognition accuracy was 98.75%.There was no significant different among grading tobacco appearance quality, routine chemical compositions, polyphenol content, the content of TSNAs and sensory quality of change and the artificial recognition (P<0.05).Which provided objective evaluation method for noise smoke, and the availability of tobacco leaves with different browning degrees was effectively distinguished.【Conclusion】The accuracy of the BP neural network recognition system for identifying the degree of tobacco browning is close to that of manual recognition. The BP neural network automatic recognition system can be used to replace manual recognition, which provides a reference for promoting the construction of tobacco BP. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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