Paper Infomation
Prediction Model of Nitrogen Content in Apple Leaves based on Ground Imaging Spectroscopy
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Author: Baichao Li, Xicun Zhu, Ruiyang Yu, Xiaoyan Guo, Shujing Cao, Huansan Zhao
Abstract: A prediction model of apple leaf nitrogen content based on ground imaging spectroscopy was established to rapidly and nondestructively detect nitrogen content in apple leaves. SOC710VP hyperspectral imager was used to obtain the imaging spectral information of apple leaves, and the average spectral curve of interest region was extracted. The study is to analyze the characteristics of imaging spectral curves of apple leaves with different nitrogen content. On the basis of the SG smoothing and first derivative pretreatment of the spectral curve, the maximum sensitive band with nitrogen content is screened and the spectral parameters are constructed. Three modeling methods of BP, SVM and RF were used to establish the prediction model of nitrogen content in apple leaves. The results showed that in the visible range, the nitrogen content of apple leaves was negatively correlated with the reflectance of the spectral curve, and was most obvious in the green range. The R² of BP, SVM and RF of apple leaf nitrogen content prediction model were 0.7283, 0.8128, 0.9086, RMSE were 0.9359, 0.7365, 0.5368, the R² of test model were 0.6260, 0.7294, 0.6512, RMSE were 0.9460, 0.7350, 0.9024.Comparing the prediction results of the three models, the optimal prediction model is SVM model, which can well predict the nitrogen content of apple leaves.
Keywords: Apple Leaves, SVM, Ground Imaging Spectroscopy
References:
[1] Stroppiana D Boschetti M,Brivio P, et al.Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry[J].Field Crops Research，2009，111(1/2): 119一129.
[2] Goel,Prasher S.O.,Landry J.A., et al. Potential of airbornesensing to detect nitrogen deficiency and weed infestation inElectronics in Agriculture, 2003, 38: 99-124.
[3] Vigneau N., Ecarnot M.,Rabatel G.,et al.Potential of field hyperspectral imaging as a nondestructive method to assess leaf nitrogen content in Wheat. Field Crops Research,2011，122: 25-31.
[4] Jinmeng LI, Xujun YE, Qiaonan WANG, et al. Development of prediction models for determining N content in citrus leaves based on hyperspectral imaging technology [J]. Spectroscopy and Spectral Analysis, 2014, 34(1): 212-216.
[5] Xiaolei ZHANG, Fei LIU, Pengcheng NIE, et al. Rapid detection of nitrogen content and distribution in oilseed rape leaves based on hyperspectral imaging[J]. Spectroscopy and Spectral Analysis, 2014, 34(9): 2513-2518.
[6] Keqiang YU, Yanru ZHAO, Xiaoli LI, et al. Application of hyperspectral imaging for visualization of nitrogen content in pepper leaf with different positions[J]. Spectroscopy and Spectral Analysis, 2015, 35(3): 746-750.
[7] Shuwen WANG, Shan ZHAO, Changli ZHANG,et. Forecasting model for nitrogen content of maize canopy during seedling stage in cold region based on imaging spectral technique [J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(13): 149-154.
[8] Jun SUN, Xiaming JIN, Hanping MAO, et al. Detection of nitrogen content in lettuce leaves based on spectroscopy and texture using hyperspectral imaging technology[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(10): 175-181.
[9] Jun SUN, Xiaming JIN, Hanping MAO, et al. Discrimination of Lettuce Leaves’Nitrogen Status Based on Hyperspectral Imaging Technology and ELM[J]. Transactions of the Chinese Society of Agricultural Machinery, 2014, 45(7): 277-282.
[10] Yande Liu, Xiaogang JIANG,Yanhua Zhou, et al. Quantitative analysis of chlorophyll, water and nitrogen for navel orange leaf based on imaging technology Hyperspectral[J]. Journal of Chinese Agricultural Mechanization, 2016, 37(3): 218-224.
[11] Xicun ZHU, Yuanmao JIANG, Gengxing ZHAO, et al. Hyperspectral estimation of kalium content in apple florescence canopy based on fuzzy recognition[J]. Spectroscopy and Spectral Analysis,2013, 33(4): 1023-1027.
[12] Longguo WU, Jianguo HE, Guishan LIU, et al. Non-destructive determination of moisture in jujubes based on near-infrared hyperspectral imagine technique[J]. Journal of Optoelectronics•Laser, 2014,25(1): 135-140.
[13] Ronghua JI, Lihua ZHENG, Xiaolei DENG, et al. Forecasting Chlorophyll Content and Moisture of Apple Leaves in Different Tree Growth Period Based on Spectral Reflectance[J]. Transactions of the Chinese Society of Agricultural Machinery, 2014,45(8): 274-280.
[14] Qinhong LIAO, Dongyan ZHANG, Jihua WANG, et al. Assessment of chlorophyll content using a new vegetation index based on multi-angular hyperspectral image data[J]. Spectroscopy and Spectral Analysis, 2014, 34(6): 1599-1604.
[15] Yanru ZHAO, Keqiang YU, Xiaoli LI, et al. Study on SPAD visualization of pumpkin leaves based on hyperspectral imaging technology[J]. Spectroscopy and Spectral Analysis, 2014, 34(5): 1378-1382.
[16] Xibin DING, Fei LIU, Chu ZHANG, et al. Prediction of SPAD value in oilseed rape leaves using hyperspectral imaging technique[J]. Spectroscopy and Spectral Analysis, 2015, 35(2): 486-491.
[17] Jianmin TANG, Qinhong LIAO, Yiqing LIU, et al. Estimating leaf area index of crops based on hyperspectral compact airborne spectrographic imager(CASI) data [J]. Spectroscopy and Spectral Analysis, 2015, 35(5): 1351-1356.
[18] Yipeng YANG, Qiao WANG, Qing XIAO, et al. Quantitative remote sensing inversion methods of chlorophyll-a concentration in Taihu lake based on TM data[J]. Geograpy and Geo-Information Science, 2006, 22(2): 5-8.
[19] Shuangping HUANG, Tiansheng HONG, Xuejun YUE, et al.Multiple regression analysis of citrus leaf nitrogen content using hyperspectral technology[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(5): 132-138.
[20] Shuangping HUANG, Tiansheng HONG, Xuejun YUE, et al. Hyperspectral estimation model of total phosphorus content for citrus leaves[J]. Transactions of the Chinese Society of Agricultural Machinery, 2013, 44(4): 195, 202-207.
[21] Liwen WANG, Yaxing WEI, et al. Progress in inversion of vegetation nitrogen concentration by hyperspectral remote sensing[J]. Spectroscopy and Spectral Analysis, 2013, 33(10): 2823-2827.
[22] Tian XIA, Wenbin WU, Qingbo ZHOU, et al. Comparison of two inversion methods for winter wheat leaf area index based on hyperspectral remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(3): 139-147.
[23] Keqiang YU, Yanru ZHAO, Xiaoli LI, et al. Application of hyperspectral imaging for visualization of nitrogen content in pepper leaf with different positions[J]. Spectroscopy and Spectral Analysis, 2015, 35(3): 746-750.