Identifying Ramie Variety by Combining the Hyperspectral Technology with the Principal Component Analysis
CAO Xiao-lan1,2, DENG Meng-jie1, CUI Guo-xian2*
1. College of Information Science and Technology, Hunan Agricultural University, Changsha 410128, China
2. Ramie Research Institute of Hunan Agricultural University, Changsha 410128, China
Abstract:Ramie(Boehmeiria nivea L)is a special and traditional fiber crop in China, having higher economic status. Determining the hyperspectral reflectance of ramie leaves with the spectrometer and developing a hyperspectrum-based method of ramie variety identification of high efficiency will be beneficial for the cultivation of ramie, the development and utilization of germplasm resources as well as the provision of critical technological supports to realize the top quality and high production of ramie and the accurate management of ramie croplands, which are significant for improving ramie yield and quality. In order to apply the hyperspectral technology for identifying ramie varieties, total 1458 hyperspectral data on the ramie leaves coming from nine ramie varieties of different genotypes were collected. According to these data, we explored the using of the Principal Components Analysis(PCA) to reduce dimensions of the hyperspectral data and how to determine the best appropriate number of principal factors in the PCA. Further, we compared different combinations constituted by different principal factors and different Discriminant Analysis approaches, and the results of the ramie variety identifying models based on the hyperspectrum of ramie leaves were established. After the principal component analysis of the full-band data sample, with 2~20 principal components as the feature variables, we applied three discriminant models, namely the Linear Discriminant analysis(LDA), the Quadratic Discriminant Analysis(QDA), and the Mahalanobis Distance Discriminant Analysis, (MD-DA), to create variety discriminant models and used them to predict, and with the accuracy of the prediction set as the evaluation criteria, the effects of various combinations were compared. The results showed that when we used the cumulative contribution rate(≥85%) as the criteria and selected two principal components, the accuracies for the LDA, the QDA and the MD-DA prediction sets were respectively 32.92%, 38.48% and 33.54%; but, when we used the feature value(≥1) as the criteria, and selected eleven principal components, the accuracies for the prediction sets of above discriminant models were respectively 68.72%, 87.04% and 83.54%; and further, when we considered the accuracy of the prediction set as the preferential criteria and selected twenty principal components, the accuracies for above discriminant models were all significantly improved and were respectively 84.98%, 95.68% and 95.27%. Therefore, we can draw the following conclusions: (1) it is feasible to establish the ramie leaf-based hyperspectral variety identification model by combining the PCA and the DA, but there are big differences between results due to different numbers of factors, different DA criterias and different combination approaches; (2)The impact of the number of principal factors on the identification results are significant, and the appropriate adding of the principal components can notably improve the accuracies of corresponding models, thus it is not confined to how to select the feature values of the PCA and the accumulative variance contribution rate ; (3) When the numbers of principal factors are the same, among above three discriminant criteria, the effect of the QDA is the best while that of the LDA is the worst; (4) Twenty principal components and the QDA approach constitute the best combination, which makes data dimensions be hugely reduced, from 2031 dimensions of the full-band down to 20 dimensions, and the accuracy of the prediction set is 95.68%.
Key words:Ramie; Hyperspectrum; Principal components analysis; Discriminant analysis
曹晓兰,邓梦洁,崔国贤. 高光谱结合主成分分析的苎麻品种识别[J]. 光谱学与光谱分析, 2019, 39(06): 1905-1908.
CAO Xiao-lan, DENG Meng-jie, CUI Guo-xian. Identifying Ramie Variety by Combining the Hyperspectral Technology with the Principal Component Analysis. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1905-1908.
[1] WANG Dong,WU Jian(王 岽,吴 见). Geography and Geo-Information Science(地理与地理信息科学),2015,31(2):29.
[2] MA Hui-ling,WANG Ruo-lin,CAI Cheng,et al(马惠玲,王若琳,蔡 骋,等). Journal of Agricultural Machinery(农业机械学报),2017,48(4):305.
[3] ZANG Zhuo, LIN Hui, YANG Min-hua(臧 卓, 林 辉, 杨敏华). Science of Surveying and Mapping(测绘科学),2014,39(2):146.
[4] LIU Yao,TAN Ke-zhu, CHEN Yue-hua, et al(刘 瑶,谭克竹,陈月华,等). Soybean Science(大豆科学), 2016, 35(4): 672.
[5] Manjunath K R,Ray S S, Panigrahy S. Indian Society Remote Sense, 2011, 39(4): 599.
[6] Mubarakat Shuaibu,Won Suk Lee,John Schueller,et al. Computers and Electronics in Agriculture, 2018, 148(5): 45.
[7] LIU Fei, YANG Chun-yan, XIE Jian-xin(刘 飞,杨春艳, 谢建新). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016,36(5): 1363.
[8] SU Jian-guang, DAI Zhi-gang(粟建光, 戴志刚). Chinese Hemp Plant Germplasm Resources and Their Main Characters(中国麻类作物种质资源及其主要性状). Beijing: China Agricultural Press(北京: 中国农业出版社), 2016. 9.
[9] BAO Jun-peng,ZHANG Xuan-ping(鲍军鹏,张选平). Introduction to Artificial Intelligence(人工智能导论). Beijing: China Machine Press(北京:机械工业出版社),2013. 5.
[10] XIE Long-han,SHANG Tao,CAI Ming-jing(谢龙汉,尚 涛,蔡明京). SPSS Statistical Analysis and Data Mining (SPSS统计分析与数据挖掘). Beijing: Electronic Industry Press(北京:电子工业出版社),2014. 4.
[11] YAN Yan-lu,CHEN Bin,ZHU Da-zhou(严衍禄,陈 斌,朱大洲). The Principle, Technology and Application of Near Infrared Spectroscopy(近红外光谱分析的原理、技术与应用). Beijing: China Light Industry Press(北京: 中国轻工业出版社),2013. 1.