Field monitoring of wheat seedling stage with hyperspectral imaging
Keywords:
wheat seedling, monitoring, ASD, hyperspectral imaging, partial least squaresAbstract
Nutrient elements such as chlorophyll, nitrogen and water at the seedling stage are important key factors that could influence growth, development and even the final yield of wheat. In this study, the spectral data of canopy and single wheat plant leaves at seedling stage were acquired in field by using ASD non-imaging hyperspectrometer and imaging spectrometer respectively to establish prediction models for monitoring the growth at the seedling stage of wheat. According to the comparative analysis of models results built through partial least square algorithm (PLS), it was found that the models built using spectral data of canopy based on ASD non-imaging hyperspectrometer and imaging spectrometer both had low precision, which was possibly caused by background such as soil; while the model established from single wheat plant leaves based on the imaging spectrometer had a better effect. At last, the PLS model was established for chlorophyll SPAD value of wheat seedling leaves based on imaging spectrometry and its correlation coefficient R reached 0.8836, and the correlation coefficient R of the relevant model for nitrogen content was 0.8520, suggesting that the superiority of location monitoring of growth at seedling stage of wheat based on hyperspectral imaging is significant. Keywords: wheat seedling, monitoring, ASD, hyperspectral imaging, partial least squares DOI: 10.3965/j.ijabe.20160905.1707 Citation: Wu Q, Wang C, Fang J J, Ji J W. Field monitoring of wheat seedling stage with hyperspectral imaging. Int J Agric & Biol Eng, 2016; 9(5): 143-148.References
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[2] Ramadan Z, Hopke P K, Johnson M J, Scow K. Application of PLS and back propagation neural networks for the estimation of soil properties. Chemometrics and Intelligent Laboratory Systems, 2005; 75(1): 23–30.
[3] Lu W Z, Yuan H F, Xu G T. Modern near infrared spectral analysis technology. Beijing: China Petrochemical Press, 2000.
[4] Huang W J, Wang J H, Liu L Y, Zhao C J, Wang J D, Du X H. The red edge parameters diversification disciplinarian and its application for nutrition diagnosis. Remote Sensing Technology and Application, 2003; 18(4): 206–211. (in Chinese with English abstract)
[5] Ni Y N. Application of chemometrics in analytical chemistry. Beijing: Science Press, 2004.
[6] Liu Y D, Ying Y B, Fu X P. Study on predicting sugar content and valid acidity of apples by near infrared diffuse reflectance technique. Spectroscopy and Spectral Analysis, 2005; 25(11): 1793–1796. (in Chinese with English abstract)
[7] Xu L, Shao X G. Chemometrics Methods. Beijing: Science Press, 1997.
[8] Pu R L, Gong P. Analysis on correlation of forest biochemistry with CASI hyperspectral remote sensing data. Journal of Remote Sensing, 1997; 1(2): 115–123.
[9] Broge N H, Mortensen J V. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sensing of Environment, 2002; 81(1): 45–57.
[10] Zhang X, Liu Y L, Zhao C J, Zhang B. Estimating wheat nitrogen concentration with high spectral resolution image. Journal of Remote Sensing, 2003; 7(3): 176–181. (in Chinese with English abstract)
[11] Wang J H, Zhao C J, Guo X W, Tian Q. Study on the water status of the wheat leaves diagnosed by the spectral reflectance. Agricultural Sciences in China, 2001; 34(1): 1–4. (in Chinese with English abstract)
[12] Wu C S, Xiang Y Q, Zheng L F, Tong Q X. Estimating chlorophyll density of crop canopies by using hyperspectral data. Journal of Remote Sensing, 2000; 4(3): 228–232. (in Chinese with English abstract)
[13] Jing J J, Wang J H, Wang J D, Liu L Y, Huang W J, Zhao C J. Spectral characteristics of winter wheat under different nitrogen nutrient. Remote Sensing Information, 2003; 2: 29–31. (in Chinese with English abstract)
[14] Li Y X, Xie X J, Xu D F. Application of hyperspectral remote sensing technology in monitoring crop growth. Journal of Triticeae Crops, 2009; 29(1): 174–178.
[15] Zhang D Y, Song X Y, Ma Z H, Yang G J, Huang W J. Assessment of the developed pushbroom imaging spectrometer in single leaf scale. Scientia Agriculture Sinica, 2010; 43(11): 2239–2245.
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Published
2016-09-30
How to Cite
Qiong, W., Cheng, W., Jingjing, F., & Jianwei, J. (2016). Field monitoring of wheat seedling stage with hyperspectral imaging. International Journal of Agricultural and Biological Engineering, 9(5), 143–148. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/1707
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Information Technology, Sensors and Control Systems
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