| 光谱学与光谱分析 |
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| Monitoring of Winter Wheat Aboveground Fresh Biomass Based on Multi-Information Fusion Technology |
| ZHENG Ling1, 2, ZHU Da-zhou3, DONG Da-ming1, ZHANG Bao-hua1，WANG Cheng1, ZHAO Chun-jiang1* |
1. Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
2. School of Electronic Information Engineering, Anhui University, Hefei 230039, China
3. Institute of Food and Nutrition Development, Ministry of Agriculture, Beijing 100081, China |
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Abstract The aim was to find a nondestructive way to improve the accuracy of detecting the winter wheat aboveground fresh biomass(AGFB). In this study, data fusion technology of the spectroscopy technology and the machine vision technology were used to analyze the AGFB and solve the problem that the accuracy of the prediction model of a single technology is not high. In this experiment, canopy spectra and canopy pictures of 93 samples at seeding stage were collected. Canopy spectra and side images of 200 samples at medium and later growth stage were collected. Spectral reflectance as the spectral absorption parameter was used to construct the AGFB prediction models based on the spectra technology at different stages; The wheat coverage were extracted from canopy pictures and side images by using image processing technology to build the AGFB prediction models. Multivariate regression analysis (MRA) and Partial least-squares regression analysis(PLS) were implemented on the feature variables from the spectral information and image information. The results showed that, compared with the individual image model and spectral model, the AGFB prediction models of PLS based on multi-information at different stages shows better performance. At the seeding stage, the determination coefficient (R2) of PLS models based on multi-information was 0.881，and the RMSE was 0.015 kg. The R2 of PLS models based on multi-information was 0.791, the RMSE was 0.059 kg at middle and final stages. It demonstrated that the precision of model based on multi-information fusion technology, which increased utilization of image and spectral information, was improved for AGFB detecting, which is than the individual image model and spectral model.
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Received: 2015-01-18
Accepted: 2015-04-20
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Corresponding Authors:
ZHAO Chun-jiang
E-mail: zhaocj@nercita.org.cn
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[1] Damiran D, DelCurto T, Johnson D, et al. Northwest Science, 2006, 80(4): 259.
[2] Hansen P M, Schjoerring J K. Remote Sensing of Environment, 2003, 86(4): 542.
[3] WANG Da-cheng, WANG Ji-hua, JIN Ning, et al(王大成, 王纪华, 靳 宁, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2008, 24(Supp.2): 196.
[4] FU Yuan-yuan, WANG Ji-hua, YANG Gui-jun, et al(付元元, 王纪华, 杨贵军, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(5): 1315.
[5] Moges S M, Raun W R, Mullen R W, et al. Journal of Plant Nutrition, 2005, 27(8): 1431.
[6] Erdle K, Mistele B, Schmidhalter U. Field Crops Research, 2013, 141: 16.
[7] Reyniers M, Vrindts E, Baerdemaeker J D. Biosystems Engineering, 2004, 89(4): 383.
[8] SONG Kai-shan, ZHANG Bai, LI Fang, et al(宋开山, 张 柏, 李 方, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2005, 21(1): 36.
[9] Gao X, Huete A R, Ni W, et al. Remote Sensing of Environment, 2000, 74(3): 609.
[10] Mutanga O, Skidmore A K. International Journal of Applied Earth Observation and Geoinformation, 2004, 5(2): 87.
[11] Fu Y Y, Yang G J, Wang J H, et al. Computers and Electronics in Agriculture, 2014, 100: 51.
[12] Shatadal P, Tan J. Applied Engineering in Agriculture, 2003, 19(1): 65.
[13] Zhao C J, Li C J, Wang Q, et al. Computer and Computing Technologies in Agriculture II, Volume 1. Springer US, 2009: 253.
[14] LEI Yong-wen, WANG Juan, GUO Jin-qiang, et al(雷咏雯, 王 娟, 郭金强, 等). Acta Agriculturae Boreali-Occidentalis Sinica(西北农业学报), 2006, 15(3): 45.
[15] Lati R N, Filin S, Eizenberg H. Weed Science, 2011, 59(2): 276.
[16] SHAN Cheng-gang, LIAO Shu-hua, GONG Yu, et al(单成钢, 廖树华, 龚 宇, 等). Acta Agronomica Sinica(作物学报), 2007, 33(3): 419.
[17] CHEN Quan-sheng, ZHAO Jie-wen, CAI Jian-rong(陈全胜, 赵杰文, 蔡健荣). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2008, 24(3): 5.
[18] Van Der Wal D, Herman P M J. Remote Sensing of Environment, 2007, 111(1): 89.
[19] DiNatale C, Zude-Sasse M, Macagnano A, et al. Analytica Chimica Acta, 2002, 459(1): 107.
[20] Rudnitskaya A, Kirsanov D, Legin A, et al. Sensors and Actuators B: chemical, 2006, 116(1): 23.
[21] Huang Y, Lan Y, Hoffmann W C, et al. Journal of Bionic Engineering, 2007, 4(1): 53. |
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