Sun Jun
Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing, 100081 P.R.China
Jin Xiaming
School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, 212013, P.R. China
Mao Hanping
Laboratory Venlo of Modern Agricultural Equipment, Jiangsu University, Zhenjiang, 212013, P.R. China
Wu Xiaohong
School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, 212013, P.R. China
Gao Hongyan
Laboratory Venlo of Modern Agricultural Equipment, Jiangsu University, Zhenjiang, 212013, P.R. China
Zhu Wenjing
Laboratory Venlo of Modern Agricultural Equipment, Jiangsu University, Zhenjiang, 212013, P.R. China
Liu Xiao
School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, 212013, P.R. China
ABSTRACT
This study was carried out to detect nitrogen content in lettuce leaves rapidly and non-destructively using visible and near infrared (VIS-NIR) hyperspectral imaging technology. Principal Component Analysis (PCA) was performed on the average spectra to reduce the spectral dimensionality and the principal components (PCs) were extracted as the input vectors of prediction models. Partial Least Square Regression (PLSR), Back Propagation Artificial Neural Network (BP-ANN), Extreme Learning Machine (ELM), Support Vector Machine Regression (SVR) were, respectively applied to relate the nitrogen content to the corresponding PCs to build the prediction models of nitrogen content. R2p of the PLSR model for nitrogen content was 0.91 and RMSEP was 0.32. BP model of structure 5-2-1 with R2p of 0.92 and RMSEP of 0.21, ELM model of structure 5-10-1 with R2p of 0.95 and RMSEP of 0.19 and SVR model for nitrogen with R2p of 0.96 and RMSEP of 0.18, all got good prediction performance. Compared with the other three models, SVR model has the better performance for predicting nitrogen content in lettuce leaves. This work demonstrated that the hyperspectral imaging technique coupled with PCA-SVR exhibits a considerable promise for nondestructive detection of nitrogen content in lettuce leaves.
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How to cite this article
Sun Jun, Jin Xiaming, Mao Hanping, Wu Xiaohong, Gao Hongyan, Zhu Wenjing and Liu Xiao, 2013. Detecting Nitrogen Content in Lettuce Leaves Based on Hyperspectral Imaging and Multiple Regression Analysis. Information Technology Journal, 12: 4845-4851.
DOI: 10.3923/itj.2013.4845.4851
URL: https://scialert.net/abstract/?doi=itj.2013.4845.4851
DOI: 10.3923/itj.2013.4845.4851
URL: https://scialert.net/abstract/?doi=itj.2013.4845.4851
REFERENCES
- Ding, L., T.Q. Liao and L. Tao, 2011. The method of sensors data fusion based on SVR. Chin. J. Sensors Actuators, 24: 710-713.
Direct Link - Barbin, D.F., G. ElMasry, D.W. Sun and P. Allen, 2012. Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. Analytica Chimica Acta, 719: 30-42.
CrossRef - ElMasry, G., N. Wang, A. ElSayed and M. Ngadi, 2007. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. J. Food Eng., 81: 98-107.
CrossRef - Shi, J.Y., X.B. Zou, J.W. Zhao, H.P. Mao, K.L. Wang, Z.W. Chen and X.W. Huang, 2011. Diagnostics of nitrogen deficiency in mini-cucumber plant by near infrared reflectance spectroscopy. Afr. J. Biotechnol., 10: 19687-19692.
Direct Link - Shi, J.Y., X.B. Zou, J.W. Zhao, K.L. Wang and Z.W. Chen et al., 2012. Nondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imaging. Scientia Horticulturae, 138: 190-197.
CrossRef - Graeff, S., D. Steffens and S. Schubert, 2001. Use of reflectance measurements for the early detection of N, P, Mg and Fe deficiencies in Zea mays L. J. Plant Nutr. Soil Sci., 164: 445-450.
CrossRef - Tian, Y.C., X. Yao, J. Yang, W.X. Cao, D.B. Hannaway and Y. Zhu, 2011. Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground-and space-based hyperspectral reflectance. Field Crops Res., 120: 299-310.
CrossRef - Yang, W., S. Nick and M.Z. Li, 2010. Nitrogen content testing and diagnosing of cucumber leaves based on multispectral imagines. Spectrosc. Spectral Anal., 30: 210-213.
PubMedDirect Link - Feng, Y.Z. and D.W. Sun, 2013. Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets. Talanta, 109: 74-83.
CrossRef - Zhang, X., F. Liu, Y. He and X. Gong, 2013. Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging. Biosyst. Eng., 115: 56-65.
CrossRefDirect Link - Zhang, Y.S., X. Yao, Y.C. Tian, W.X. Cao and Y. Zhu, 2010. Estimating leaf nitrogen content with near infrared reflectance spectroscopy in rice. Chin. J. Plant Ecol., 34: 704-712.
CrossRefDirect Link