Integrated Sensor System for Rice Conditions Monitoring Based UGV
Keywords:
UGV, multi sensors, spectral vegetation indices, LAIAbstract
Ground-based platform systems provide a good tool for monitoring and managing crop conditions in precision agriculture applications and have been widely used for monitoring crop conditions. To develop an unmanned ground vehicle system (UGVS) based multi-sensors and test the feasibility of this system for monitoring rice conditions, an UGVS was developed to collect real-time rice condition information including NDVI values, reflectance measurements and crop canopy temperature in this study. Major components of the integrated system are GreenSeeker R100 system, hyper-spectroradiometer and infrared temperature sensor. The leaf area index (LAI) was measured by the CGMD302 Spectrometer. The Independent Samples T-Test method and the one way ANOVA method were used to determine the best spectral indices and analyze the relationship between the vegetation indices and rice LAI. It was found that the two best spectral indices for estimating LAI were NDVI (860 nm and 750 nm) with the correlation coefficient (R2) at 0.745 and RVI (853 nm and 751 nm) with the R2 at 0.724. The results show the UGVS can support multi-source information acquisition and is useful for crop management and precision agriculture applications. Keywords: unmanned ground vehicle system (UGVS), multi-sensors, rice growth condition, spectral vegetation indices, leaf area index (LAI) DOI: 10.3965/j.ijabe.20140702.009 Citation: Wang P, Lan Y B, Luo X W, Zhou Z Y, Wang Z, Wang Y. Integrated sensor system for monitoring rice growth conditions based on unmanned ground vehicle system. Int J Agric & Biol Eng, 2014; 7(2): 75-81.References
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Yao, X., Y.C. Tian, J. Ni, Y.S. Zhang, W.X. Cao, and Y. Zhu. 2012. Estimation of leaf pigment concentration in rice by near infrared reflectance spectroscopy. Analytical Chemistry. 40(4): 589-595.
Zhang H., Y. Lan, R. Lacey, W.C. Hoffmann, and Y. Huang. 2009b. Analysis of vegetation indices derived from aerial multispectral and ground hyperspectral data. Int J Agric & Biol Eng. 2(3): 1-8.
Zhang, H., X.G. Yao, X.B. Zhang, and K.F. Zheng. 2009a. A primary study on nitrogen content of rice leaf based on remote sensing at spiking stage. Journal of Nuclear Agricultural Sciences. 23(3): 364-368.
Zhang, H., Y. Lan, R. Lacey, W.C. Hoffmann, D.E. Martin, B. Fritz, and J.L. Jr. 2010. Ground-based spectral reflectance measurements for evaluating the efficacy of aerially- applied glyphosate treatments. Biosystems engineering. 107: 10-15.
Zhang, J.H., Wang, K., Bailey, J. S., and Wang, R. C. 2006. Predicting nitrogen status of rice using multispectral data at canopy scale. Pedosphere, 16, 108-117.
Zhang, N., and R. K. Taylor. 2001. Applications of a field
Berni, J.A.J., P.J. Zarco-Tejada, and L.Suarez. 2009a. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing. 47: 722-738.
Casanova, D., G.F. Epema, and J. Goudriaan. 1998. Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crops Research. 55: 83-92.
Castro-Esau, K. L., G. A. Sanchez-Azofeifa, and B. Rivard. 2006. Comparison of spectral indices obtained using multiple spectroradiometers. Remote Sens. Environ. 103: 276-288.
Chang, K.W., Y. Shen, and J.C. Lo.2005. Predicting rice yield using canopy reflectance measured at booting stage. Agron. J. 97: 872-878.
Chen, Q.C., Y.C. Tian, K.J. Gu, W. Wang, X. Yao, W.X. Cao, and Y. Zhu. 2011. Monitoring plant nitrogen accumulation with different canopy spectrometers at early growth stages in rice. Transactions of the CSAE. 27(1): 223-229.
Doraiswamy, P.C., S.Moulin, P.W.Cook, and A.Stern. 2003. Crop yield assessment from remote sensing. Photogrammetric Engineering & Remote Sensing. 69(6): 665-674.
Inoue, Y. 2003. Synergy of remote sensing and modeling for estimating ecophysiological processes in plant production. Plant Prod. Sci. 6,3-16.
Jiang,N., W.G. Li, and P.J. Du. 2012. Application of different remote sensing data fusion methods to rice area monitoring in south area. Journal of Southwest University (Natural Science Edition). 34(6): 1-8.
Lan, Y., H. Zhang, R. Lacey, W.C. Hoffmann, and W.F. Wu. 2009. Development of an integrated sensor and instrumentation system for measuring crop conditions. Agricultural Engineering International: the CIGR Ejournal. Manuscript IT 08 1115. Vol. XI.
Lan, Y., S.J. Thomson, Y. Huang, W.C. Hoffmann, and H. Zhang. 2010. Current status and future directions of precision aerial application for site-specific crop management in the USA. Computers and Electronics in Agriculture. 74: 34-38.
Li, J.Y., T.M. Zhang, X.D. Peng, G.Q. Yan, and Y. Chen. 2010. The application of small UAV ( SUAV) in farmland information monitoring system. Journal of Agricultural Mechanization Research. (5): 183-186.
Luck J., M. Spackman, A. Freeman, P. Trebicki, W. Griffiths, K. Finlay and S. Chakraborty. 2011. Climate change and diseases of food crops. Plant Pathology. 60: 113-121.
Moran, M.S., Y. Inoue, and E.M., Barnes. 1997. Opportunities and limitation for image-based remote sensing in precision crop Management. Remote Sensing of Environment, 61: 319-346.
Pinter, Jr., P. J., Hatfield, J.L., Schepers, J.S., Barnes,E.M., M.S.,Moran, C.S.T.,Daughtry, and D.R.,Upchurch. 2003.
Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing. 69(6): 647-664.
Rango, A., A. Laliberte, C. Steele, E.H. Jeffrey, B. Bestelmeyer, T. Schmugge, A. Roanhorse, and V. Jenkins. 2006. Using unmanned aerial vehicles for rangelands: current applications and future potentials. Environmental Practice. 8: 159-168.
Rango, A., A.S. Laliberte, J.E. Herrick, C. Winters, K. Havstad, and C. Steele. 2009. Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. Journal of Applied Remote Sensing, 3(1): 33542.
Scotford, I.M., and P.C.H. Miller. 2004. Combination of spectral reflectance and ultrasonic sensing to monitor the growth of winter wheat. Biosystems Engineering. 87(1): 27- 38.
Stafford, J.V. 2000. Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research. 76: 267-275.
Sun, X.M., Q.F. Zhou, and Q.X. He. 2005. Hyperspectral variables in predictingleaf chlorophyll content and grain protein content in rice. Acta Agronomica Sinica. 31(7): 844-850.
Tian, Y.C., J.Yang, X.Yao, and W.X. Cao, Y. Zhu. 2010. Monitoring canopy leaf nitrogen concentration based on leaf hyper spectral indices in rice. Acta Agronomica Sinica. 36(9): 1529-1537.
Tian, Y.C., J.Yang, X.Yao, Y. Zhu, and W.X. Cao. 2009. Quantitative relationship between hyper-spectral vegetation indices and leaf area index of rice. Chinese Journal of Applied Ecology. 20 (7):1685-1690.
Tian, Y.C., J.Yang, X.Yao, Y. Zhu, and W.X. Cao.2009.Quantitative relationship between hyper-spectral red edge position and canopy leaf nitrogen concentration in rice. Acta Agronomica Sinica. 35(9): 1681-1690.
Tumbo S.D., D.G. Wagner, and P.H. Heinemann. 2002a. Hyper spectral-based neural network for predicting chlorophyll status in corn. Transactions of the ASAE. 45(3): 825-832.
Tumbo, S. D., D. G. Wagner, and P.H. Heinemann. 2002b. On-the-go sensing of chlorophyll status in corn. Transaction of ASAE. 45(4): 1207-1215.
Warren, G., and Metternicht, G.2005. Agricultural applications of high-resolution digital multispectral imagery: Evaluating within-field spatial variability of canola (Brassica napus) in Western Australia. Photogrammetric Engineering and Remote Sensing, 71: 595-602.
Yao, X., Y.C. Tian, J. Ni, Y.S. Zhang, W.X. Cao, and Y. Zhu. 2012. Estimation of leaf pigment concentration in rice by near infrared reflectance spectroscopy. Analytical Chemistry. 40(4): 589-595.
Zhang H., Y. Lan, R. Lacey, W.C. Hoffmann, and Y. Huang. 2009b. Analysis of vegetation indices derived from aerial multispectral and ground hyperspectral data. Int J Agric & Biol Eng. 2(3): 1-8.
Zhang, H., X.G. Yao, X.B. Zhang, and K.F. Zheng. 2009a. A primary study on nitrogen content of rice leaf based on remote sensing at spiking stage. Journal of Nuclear Agricultural Sciences. 23(3): 364-368.
Zhang, H., Y. Lan, R. Lacey, W.C. Hoffmann, D.E. Martin, B. Fritz, and J.L. Jr. 2010. Ground-based spectral reflectance measurements for evaluating the efficacy of aerially- applied glyphosate treatments. Biosystems engineering. 107: 10-15.
Zhang, J.H., Wang, K., Bailey, J. S., and Wang, R. C. 2006. Predicting nitrogen status of rice using multispectral data at canopy scale. Pedosphere, 16, 108-117.
Zhang, N., and R. K. Taylor. 2001. Applications of a field
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Published
2014-04-28
How to Cite
Pei, W., Lan, Y., Xiwen, L., Zhiyan, Z., & Wang, Z. (2014). Integrated Sensor System for Rice Conditions Monitoring Based UGV. International Journal of Agricultural and Biological Engineering, 7(2), 75–81. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/1006
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Information Technology, Sensors and Control Systems
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