Prediction model of moisture content of masson pine seedling roots based on near infrared spectroscopy

NI Chao, ZHANG Yun, GAO Handong

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2019, Vol. 43 ›› Issue (6) : 91-96.

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南京林业大学学报（自然科学版）
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JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2019, Vol. 43 ›› Issue (6) : 91-96. DOI: 10.3969/j.issn.1000-2006.201902028

Prediction model of moisture content of masson pine seedling roots based on near infrared spectroscopy

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Abstract

【Objective】 Masson pine is the main afforestation species in southern China. The moisture content of roots is an important indicator for evaluating the vigour of seedlings. In this study, the method for estimating moisture content of masson pine seedling roots based on near infrared spectroscopy is advanced. 【Method】 The spectrum of masson pine seedling root is obtained. The prediction model which combines variable-wise weighted stacked autoencoder with support vector regression, was proposed to estimate the moisture content in the root of masson pine seedlings. The variable-wise weighted stacked autoencoder is used to extract the features which relating to the output and the support vector regression further accurately predict the moisture contents by using the identified features. 【Result】 Compared with the other commonly used models, our model had the best performance in the root moisture prediction, withR2 value of 0.970 8 and RMSE of 0.635 8 in the training dataset and R2 value of 0.941 3 and RMSE of 1.027 0 in the validation dataset. 【Conclusion】 The combination of the variable-wise weighted stacked autoencoder and support vector regression is feasible to realize the accurate prediction of moisture content in masson pine seedlings based on near infrared spectroscopy.

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masson pine seedling / moisture content / near infrared spectroscopy / autoencoder / variable-wise weighted / support vector regression

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NI Chao , ZHANG Yun , GAO Handong. Prediction model of moisture content of masson pine seedling roots based on near infrared spectroscopy[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2019, 43(6): 91-96 https://doi.org/10.3969/j.issn.1000-2006.201902028

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