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基于近红外光谱技术的茶园土壤酸碱状况快速判别
盛孟鸽,王凯,王玉洁,许姗姗,胡欣,侯智炜,张正竹,谷勋刚,宁井铭
0
(安徽农业大学茶树生物学与资源利用国家重点实验室,合肥 230036;安徽农业大学理学院,合肥 230036;安徽农业大学资源与环境学院,合肥 230036)
摘要:
为了实现对茶园土壤酸碱状况量化判别,以7个省份茶园313份土壤为材料,以酸碱度(pH表示)值在4.5~5.5的范围为最适宜茶树生长区间,将pH值划分为<4.5,4.5~5.5和>5.5 3个范围,提出了将近红外光谱信息与贝叶斯(Bayes)判别相结合进行定性判别酸碱状况是否适合茶树正常生长。在此基础上,采用多元线性回归(multiple linear regression,MLR)定量预测pH值。通过一阶导数(first derivative,1stDer)对光谱预处理,通过逐步判别分析(stepwise discriminant analysis)优选20条特征光谱,基于特征光谱数据结合Bayes判别构建定性判别模型,再通过MLR构建pH值的定量预测模型。结果表明,采用本研究的方法和构建的模型对茶园土壤酸碱状况总体准确判别率达83.54%,pH值预测相关系数均在0.928 6以上,预测精度较高。证明运用该方法能实现对茶园土壤酸碱状况快速预测。
关键词:  一阶导数  贝叶斯判别  逐步判别分析  多元线性回归  茶园土壤  酸碱
DOI:10.13610/j.cnki.1672-352x.20180825.032
基金项目:国家重点研发计划(2016YFD0200900)和中国现代农业(茶叶)专项体系(CARS-19)共同资助。
Rapid identification soil acid-base of tea garden based on near infrared spectroscopy
SHENG Mengge,WANG Kai,WANG Yujie,XU Shanshan,HU Xin,HOU Zhiwei,ZHANG Zhengzhu,GU Xungang,NING Jingming
(State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036;School of Science, Anhui Agricultural University, Hefei 230036;School of Resources and Environment, Anhui Agricultural University, Hefei 230036)
Abstract:
In order to develop a digitized discriminant on soil base conditions in tea garden, 313 soil samples were collected from tea gardens of seven different provinces. Usually, the pH value of soil between 4.5 and 5.5 is the most suitable range for the growth of tea plant. The soil pH values were divided into three ranges of <4.5, 4.5-5.5 and >5.5, respectively. Near infrared spectroscopy (NIRS) information combined with Bayesian discriminant was used to judge whether the base conditions are suitable for the normal growth of tea plant or not. On this basis, a multiple linear regression (MLR) model was established for the quantitative prediction of the pH values. The first derivative (1stDer) was used to preprocess NIRS data for removing redundant information. Twenty characteristic spectra were optimized and selected by stepwise discriminant analysis. According to the characteristic spectral data combined with Bayesian discriminant, the qualitative prediction model was established, and the quantitative prediction model of pH value was also established according to MLR. The results indicated that stepwise discriminant analysis combined with Bayesian discriminant model was a promising tool for the discrimination of soil base conditions, and the total accurate recognition rate of 83.54% was achieved. In addition, the result also showed that the correlation coefficients of the three ranges were all above 0.928 6 by MLR model, and the prediction accuracy was extremely high. The results of this study proven that rapidly and scientifically discrimination soil base conditions in tea garden can be achieved via the method developed in this paper.
Key words:  first derivative  Bayesian discriminant  stepwise discriminant analysis  multiple linear regression  tea garden soil  acid-base condition

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