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Open Access Forecasting model for nitrogen content of maize canopy during seedling stage in cold region based on imaging spectral technique

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Heilongjiang is a big province of corn production, where corn is widely planted.Because of the influence of soil and light, the research about corn canopy at stage of seedling with imaging spectral technique is less.In recent years, the direct analysis of spectral data and the monitoring modeling of vegetation index have become effective methods in the research of imaging spectrum technology on crop nutrient and growth analysis.So this paper was focused on the canopy at seedling stage of maize.To explore the nitrogen content of maize canopy in cold region, 2 methods were used to analyze the spectral data of canopy image.The experiment was carried out in Fangzheng county, Harbin city.The tested corn variety was Heyu20.The fertilization gradient of each test region was 0,0 and 100 kg/hm2 nitrogen.This experiment used the imaging spectrometer to collect image and the German AA3 analyzer to measure the corn ammonium nitrogen content.To ensure the integrity of the image, this paper chose to extract the image directly.In this experiment, we chose the high correlation band with each band as the variable of vegetation index.The bands were the 43rd band (525 nm), 57th band (566 nm), 102nd band (700 nm), 107th band (715 nm) and 168th band (895 nm).And then bands were brought into the vegetation index about RSI (ratio spectral index), DSI (difference spectral index), NDSI (normalized difference spectral index) and NDVI (normalized difference vegetation index).Under 50 kg/hm2 nitrogen application rate, the content of nitrogen in maize leaves had a higher correlation with NDVI, RSI (715nm, 700 nm), DSI (700nm, 566 nm), and NDSI (715nm, 700 nm).And under 0 and 100 kg/hm2 nitrogen application rate, the nitrogen content of maize leaves had a higher correlation with NDVI, RSI (895nm, 700 nm), RSI (715nm, 700 nm), DSI (700nm, 566 nm), DSI (895nm, 700nm, 525 nm) and NDSI (715nm, 700 nm).This paper established the single variable and multivariable forecasting model of nitrogen element content in maize canopy by using vegetation index and nitrogen content.The function included power function, exponential function, logarithmic function, linear function and quadratic functions.This paper tested the accuracy with the confidence ellipse F test model.Under the nitrogen application rate of 0 and 50 kg/hm2, the effect of the prediction model with NDVI was the best, and the R 2 values were 0.719 and 0.803 respectively.When the nitrogen application rate was 100 kg/hm2, the multivariate prediction effect was better, and the R2 reached 0.657.Using the F test to examine the forecast model, its Fmeasure was less than F0.05.The R 2 values between predicted values and measured values for the nitrogen content of maize canopy under different nitrogen application levels were 0.724,0.798 and 0.655 respectively and the RMSE (root mean squared error) values were 0.156,0.140 and 0.156 mg/g, respectively; the forecast model was available and the prediction model could be used.In this paper, the prediction model of nitrogen content in maize canopy has good applicability, and can provide support for the application of microUAVs (unmanned aerial vehicles) in agriculture.
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Keywords: imaging spectrum; maize canopy; models; nitrogen; spectrum analysis; vegetation index

Document Type: Research Article

Publication date: July 1, 2016

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  • Transations of the Chinese Society of Agricultural Engineering(TCSAE), founded in 1985, is sponsored by the Chinese Chemical Society. TCSAE has been indexed by EI Compendex, CAB Inti, CSA. TCSAE is devoted to reporting the academic developments of Agricultural Engineering mainly in China and some developments from abroad. The primary topics that we consider are the following: comprehensive research, agricultural equipment and mechanization, soil and water engineering, agricultural information and electrical technologies, agricultural bioenvironmental and energy engineering, land consolidation and rehabilitation engineering, agricultural produce processing engineering.

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