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[利用近红外光谱法对设施栽培小型黄瓜植株早期缺磷状况的快速诊断]

[Rapid diagnostics of early phosphorus deficiency in mini-cucumber plants under protected cultivation by near infrared spectroscopy].

作者信息

Shi Ji-yong, Zou Xiao-bo, Zhao Jie-wen, Mao Han-ping, Wang Kai-liang, Chen Zheng-wei, Huang Xiao-wei

机构信息

School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.

出版信息

Guang Pu Xue Yu Guang Pu Fen Xi. 2011 Dec;31(12):3264-8.

PMID:22295773
Abstract

The morphological symptom of phosphorus deficiency at early stage is similar to the appearance of leaf aging process in preliminary phase, so that visual diagnostics of phosphorus deficiency in mini-cucumber plants at early stage is practically impossible. Near infrared reflectance spectra contain information about differences in compositions of leaf tissues between phosphorus-deficient plants and healthy plants. In the present paper, near infrared reflectance spectroscopy was used to provide diagnostic information on phosphorus deficiency of mini-cucumber plants grown under non-soil conditions. Near infrared spectra was collected from 90 leaves of mini-cucumber plants. Raw cucumber spectra was preprocessed by SNV and divided into 27 intervals. The top 10 principal components (PCs) were extracted as the input of BP-ANN classifiers by principal component analysis (PCA) while the values of nutrient deficient were used as the output variables of BP-ANN and three layers BP-ANN discrimination model was built. The best experiment results were based on the top 3 principal components of No. 7 interval when the spectra was divided into 27 intervals and identification rates of the ANN model are 100% in both training set and the prediction set. The overall results show that NIR spectroscopy combined with BP-ANN can be efficiently utilized for rapid and early diagnostics of phosphorus deficiency in mini-cucumber plants.

摘要

早期缺磷的形态症状与叶片衰老初期的外观相似,因此实际上无法通过视觉诊断小黄瓜植株早期的缺磷情况。近红外反射光谱包含了缺磷植株与健康植株叶片组织成分差异的信息。在本文中,利用近红外反射光谱法为无土栽培条件下生长的小黄瓜植株的缺磷情况提供诊断信息。从小黄瓜植株的90片叶子上采集近红外光谱。黄瓜原始光谱经标准正态变量变换(SNV)预处理后被划分为27个区间。通过主成分分析(PCA)提取前10个主成分(PCs)作为BP人工神经网络(BP-ANN)分类器的输入,同时将养分缺乏值用作BP-ANN的输出变量,并建立了三层BP-ANN判别模型。当光谱被划分为27个区间时,基于第7区间的前3个主成分得到了最佳实验结果,人工神经网络模型在训练集和预测集中的识别率均为100%。总体结果表明,近红外光谱结合BP-ANN可有效用于小黄瓜植株缺磷的快速早期诊断。

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