Spectral reflectance response to nitrogen fertilization in field grown corn
Keywords:
spectrum, effective wavelengths, principal component analysis-loading (PCA-loading), prediction, vegetation indices (VIs), cornAbstract
This study was carried out to analyze the spectral reflectance response of different nitrogen levels for corn crops. Four different nitrogen treatments of 0%, 80%, 100% and 120% BMP (best management practice) were studied. Principal component analysis-loading (PCA-loading) was used to identify the effective wavelengths. Partial least squares (PLS) and multiple linear regression (MLR) models were built to predict different nitrogen values. Vegetation indices (VIs) were calculated and then used to build more prediction models. Both full and selected wavelengths-based models showed similar prediction trends. The overall PLS model obtained the coefficient of determination (R2) of 0.6535 with a root mean square error (RMSE) of 0.2681 in the prediction set. The selected wavelengths for overall MLR model obtained the R2 of 0.6735 and RMSE of 0.3457 in the prediction set. The results showed that the wavelengths in visible and near infrared region (350- 1000 nm) performed better than the two either spectral regions (1001-1350/1425-1800 nm and 2000-2400 nm). For each data set, the wavelengths around 555 nm and 730 nm were identified to be the most important to predict nitrogen rates. The vogelmann red edge index 2 (VOG 2) performed the best among all VIs. It demonstrated that spectral reflectance has the potential to be used for analyzing nitrogen response in corn. Keywords: spectrum, effective wavelengths, principal component analysis-loading (PCA-loading), prediction, vegetation indices (VIs), corn DOI: 10.25165/j.ijabe.20181104.2960 Citation: Xie C Q, Yang C, Hummel Jr A, Johnson G A, Izuno F T. Spectral reflectance response to nitrogen fertilization in field grown corn. Int J Agric & Biol Eng, 2018; 11(4): 118-126.References
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[28] Yang C, Lee W S, Williamson J G. Classification of blueberry fruit and leaves based on spectral signatures. Biosystems Engineering, 2012; 113(4): 351–362.
[29] Huang W J, Lamb D W, Niu Z, Zhang Y J, Liu L Y, Wang J H. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture, 2007; 8(4-5): 187–197.
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[2] Daughtry C S T, Walthall C L, Kim M S, Colstoun E B, McMurtrey Iii J E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 2000; 74(2): 229–239.
[3] Vigneau N, Ecarnot M, Rabatel G, Roumet P. Potential of field hyperspectral imaging as a nondestructive method to assess leaf nitrogen content in wheat. Field Crops Research, 2011; 122(1): 25–31.
[4] Jørgensen R N, Christensen L K, Bro R. Spectral reflectance at sub-leaf scale including the spatial distribution discriminating NPK stress characteristics in barley using multiway partial least squares regression. International Journal of Remote Sensing, 2007; 28(5): 943–962.
[5] Blackmer T M, Schepers J S, Varvel G E, Walter-Shea E A. Nitrogen deficiency detection using shortwave radiation from irrigated corn canopies. Agronomy Journal, 1996; 88(1): 1–5.
[6] Cassman K G, Dobermann A, Walters D T. Agroecosystems, nitrogen-use efficiency, and nitrogen management. AMBIO: A Journal of the Human Environment, 2002; 31(2): 132–140.
[7] Yu K Q, Zhao Y R, Li X L, Shao Y N, Liu F, He Y. Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant. Plos One, 2014; 9(12).
[8] Rathke G W, Behrens T, Diepenbrock W. Integrated nitrogen management strategies to improve seed yield, oil content and nitrogen efficiency of winter oilseed rape (Brassica napus L.): a review. Agriculture, Ecosystems & Environment, 2006; 117(2): 80–108.
[9] Zhang X L, Liu F, He Y, Gong A P. Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging. Biosystems Engineering, 2013; 115(1): 56–65.
[10] Arregui L M, Lasa B, Lafarga A, Iraneta I, Baroja E, Quemada M. Evaluation of chlorophyll meters as tools for N fertilization in winter wheat under humid Mediterranean conditions. European Journal of Agronomy, 2006; 24(2): 140–148.
[11] Tarpley L, Reddy K R, Sassenrath-Cole G F. Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration. Crop Science, 2000; 40(6): 1814–1819.
[12] Min M, Lee W S. Determination of significant wavelengths and prediction of nitrogen content for citrus. Transactions of the ASAE, 2005; 48(2): 455–461.
[13] Albayrak S. Use of reflectance measurements for the detection of N, P, K, ADF and NDF contents in sainfoin pasture. Sensors, 2008; 8(11): 7275–7286.
[14] Minnesota Department of Agriculture. http://www.mda.state.mn.us/ nitrogenbmps
[15] Xie C Q, Li X L, Nie P C, He Y. Application of time series hyperspectral imaging (TS-HSI) for determining water content within tea leaves during drying. Transactions of the ASABE, 2013; 56(6): 1431–1440.
[16] Cen H Y, Bao Y D, He Y, Sun D W. Visible and near infrared spectroscopy for rapid detection of citric and tartaric acids in orange juice. Journal of Food Engineering, 2007; 82(2): 253–260.
[17] He Y, Huang M, García A, Hernández A, Song H Y. Prediction of soil macronutrients content using near-infrared spectroscopy. Computers and Electronics in Agriculture, 2007; 58(2): 144–153.
[18] Zou X B, Shi J Y, Hao L M, Zhao J W, Mao H P, Chen Z W, et al. In vivo noninvasive detection of chlorophyll distribution in cucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging. Analytica Chimica Acta, 2011; 706(1): 105–112.
[19] Kamruzzaman M, ElMasry G, Sun D W, Allen P. Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Analytica Chimica Acta, 2012; 714: 57–67.
[20] Bieroza M, Baker A, Bridgeman J. New data mining and calibration approaches to the assessment of water treatment efficiency. Advances in Engineering Software, 2012; 44(1): 126–135.
[21] Wu D, Chen X J, Zhu X G, Guan X C, Wu G C. Uninformative variable
elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver. Analytical Methods, 2011; 3(8): 1790–1796.
[22] Kamruzzaman M, ElMasry G, Sun D W, Allen P. Application of NIR hyperspectral imaging for discrimination of lamb muscles. Journal of Food Engineering, 2011; 104(3): 332–340.
[23] Xie C Q, Li X L, Shao Y N, He Y. Color measurement of tea leaves at different drying periods using hyperspectral imaging technique. Plos One, 2014; 9(12).
[24] ElMasry G, Sun D W, Allen P. Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. Journal of Food Engineering, 2012; 110(1): 127–140.
[25] Bannari A, Morin D, Bonn F, Huete A R. A review of vegetation indices. Remote sensing reviews, 1995; 13(1-2): 95–120.
[26] ENVI 4.7 software (ITT Visual Information Solutions Inc., Boulder, CO, USA) help.
[27] Min M, Lee W S, Kim Y H, Bucklin R A. Nondestructive detection of nitrogen in Chinese cabbage leaves using Vis-NIR spectroscopy. HortScience, 2006; 41(1): 162–166.
[28] Yang C, Lee W S, Williamson J G. Classification of blueberry fruit and leaves based on spectral signatures. Biosystems Engineering, 2012; 113(4): 351–362.
[29] Huang W J, Lamb D W, Niu Z, Zhang Y J, Liu L Y, Wang J H. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture, 2007; 8(4-5): 187–197.
[30] Jain N, Ray S S, Singh J P, Panigrahy S. Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop. Precision Agriculture, 2007; 8(4-5): 225–239.
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Published
2018-08-08
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Xie, C., Yang, C., Jr, A. H., Johnson, G. A., & Izuno, F. T. (2018). Spectral reflectance response to nitrogen fertilization in field grown corn. International Journal of Agricultural and Biological Engineering, 11(4), 118–126. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/2960
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Natural Resources and Environmental Systems
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