Ground-based hyperspectral remote sensing for weed management in crop production
Keywords:
ground-based remote sensing, hyperspectral, crop injury, herbicide resistance, precision agricultureAbstract
Agricultural remote sensing has been developed and applied in monitoring soil, crop growth, weed infestation, insects, diseases and water status in farm fields to provide data and information to guide agricultural management practices. Precision agriculture has been implemented through prescription mapping of crop fields at different scales with the data remotely sensed from space-borne, airborne and ground-based platforms. Ground-based remote sensing techniques offer portability, flexibility and controllability in applications for precision agriculture. In weed management, crop injury from off-target herbicide spray drift and herbicide resistance in weeds are two important issues. For precision weed management, ground-based hyperspectral remote sensing techniques were developed for detection of crop injury from dicamba and differentiation between glyphosate resistant and sensitive weeds. This research presents the techniques for ground-based hyperspectral remote sensing for these two applications. Results illustrate the advantages of ground-based hyperspectral remote sensing for precision weed management. Keywords: ground-based remote sensing, hyperspectral, crop injury, herbicide resistance, precision agriculture DOI: 10.3965/j.ijabe.20160902.2137 Citation: Huang Y, Lee M A, Thomson S J, Reddy K N. Ground-based hyperspectral remote sensing for weed management in crop production. Int J Agric & Biol Eng, 2016; 9(2): 98-109.References
[1] Lan Y, Huang Y, Martin D E, Hoffmann W C. Development of an airborne remote sensing system for crop pest management: System integration and verification. Applied Engineering in Agriculture, 2009; 25(4): 607–615.
[2] Huang Y, Thomson S J, Lan Y, Maas S J. Multispectral imaging systems for airborne remote sensing to support agricultural production management. International Journal of Agricultural and Biological Engineering, 2010; 3(1): 50–62.
[3] Huang Y, Sui R, Thomson S J, Fisher D K. Estimation of cotton yield with varied irrigation and nitrogen treatments using aerial multispectral imagery. International Journal of Agricultural and Biological Engineering, 2013; 6(3): 37–41.
[4] Yao H, Huang Y. Remote sensing applications to precision farming. In: G. Wang and Q. Weng, editors, Remote sensing of natural resources. Chap 18. CRC Press, Boca Raton, FL. 2013; p. 333–352.
[5] Huang Y, Thomson S J. Remote sensing for cotton farming. In: Cotton, 2nd edition, Eds. D. D. Fang and R.G. Percy. American Society of Agronomy, Inc., Crop Science Society of America, and Soil Society of America, Inc. Madison, WI, USA, Agronomy Monograph, 2015; 57: 1–26.
[6] Huang Y, Thomson S J, Hoffman W C, Lan Y, Fritz B K. Development and prospect of unmanned aerial vehicles for agricultural production management. International Journal of Agricultural and Biological Engineering, 2013; 6(3): 1–10.
[7] Lamb D W, Brown R B. Remote sensing and mapping of weeds in crops –a review of airborne remote sensing. Journal of Agricultural Engineering Research, 2010; 78(2): 117–125.
[8] Thorp K, Tian L. A review on remote sensing of weeds in agriculture. Precision Agriculture, 2004; 5(5): 477–508.
[9] Deng W, Huang Y, Zhao C, Wang X. Discrimination of crop and weeds on visible and visible/near-infrared spectrums using support vector machines, artificial neural network and decision tree. Sensors and Transducers, 2014; 26: 26–34.
[10] Deng W, Huang Y, Zhao C, Chen L, Meng Z. Comparison of SVM, RBF-NN, and DT for crop and weed identification based on spectral measurement over corn fields. International Agricultural Engineering Journal, 2011; 20(1): 11–19.
[11] Deng W, Huang Y, Zhao C, Wang X. Identification of seedling cabbages and weeds using hyperspectral imaging. International Journal of Agricultural and Biological Engineering, 2015; 8(5): 65–72.
[12] Huang Y, Thomson S J, Ortiz B V, Reddy K N, Ding W, Zablotowicz R M, Bright Jr. J R. Airborne remote sensing assessment of the damage to cotton caused by spray drift from aerially applied glyphosate through spray deposition measurements. Biosystems Engineering, 2010; 7: 212–220.
[13] Huang Y, Reddy K N, Thomson S J, Yao H. Assessment of soybean injury from glyphosate using airborne multispectral remote sensing. Pesticide Management Science, 2015; 71: 545–552.
[14] Huang Y, Thomson S J, Molin W T, Reddy K N, Yao H. Early detection of soybean plant injury from glyphosate by measuring chlorophyll reflectance and fluorescence. Journal of Agricultural Science, 2012; 4(5): 117–124.
[15] Yao H, Huang Y, Hruska Z, Thomson S J, Reddy K N. Using vegetative index and modified derivative for early detection of soybean plant injury from glyphosate. Computers and Electronics in Agriculture, 2012; 89: 145–157.
[16] Rouse J W, Haas R H, Schell J A, Deering D W. Monitoring vegetation systems in the great plains with ERTS. Proc 3rd ERTS Symposium NASA SP-351, Vol. 1, NASA, Washington, DC, 1973; 309–317.
[17] Gitelson A A, Kaufman Y J, Merzlyak M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ., 1996; 58: 289–298.
[18] Lee, M A, Huang, Y, Haibo, Y, Thomson, S J, Bruce, L M. Determining the effect of storage of cotton and soybean leaf samples for hyperspectral analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014; 7(6): 2562–2570.
[19] Lee, M A, Huang, Y, Yao, H, Thomson, S J, Bruce, L M. Effects of sample storage on spectral reflectance changes in corn leaves excised from the field. Journal of Agricultural Science, 2014; 6(8): 214–220.
[20] Heap I M. International Survey of Herbicide Resistant Weeds. www.weedscience.org. Accessed on [2015-03-10].
[21] Reddy K N, Huang Y, Lee M A, Nandula V K, Fletcher R S, Thomson S J, Zhao F. Glyphosate-resistant and glyphosate-susceptible Palmer amaranth (Amaranthus palmeri S. Wats.): hyperspectral reflectance properties of plants and potential for classification. Pest. Manag. Sci. 2014; 70: 1910–1917. doi: 10.1002/ps.3755.
[22] Lee, M A, Huang, Y, Nandula, V K, Reddy, K N. Differentiating glyphosate-resistant and glyphosate-sensitive Italian ryegrass using hyperspectral imagery. In Sensing for Agriculture and Food Quality and Safety VI, Moon S. Kim; Kuanglin Chao, Editors, Proceedings of SPIE Vol. 9108 (SPIE, Bellingham, WA 2014), 91080B.
[23] Zhao F, Guo Y, Huang Y, Reddy K N, Zhao Y, Molin W T. Detection of the onset of glyphosate-induced soybean plant injury through chlorophyll fluorescence signal extraction and measurement. Journal of Applied Remote Sensing, 2015; 9(1): 1–12.
[24] Van der Tol C, Verhoef W, Rosema A. A model for chlorophyll fluorescence and photosynthesis at leaf scale. Agric. Forest Meteorol, 2009; 149: 96–105.
[25] Meroni M, Rossini M, Guanter L, Alonso L, Rascher U, Colombo R, Moreno J. Remote sensing of solar-induced chlorophyll fluorescence: review of methods and applications. Remote Sens. Environ, 2009; 113: 2037–2051.
[2] Huang Y, Thomson S J, Lan Y, Maas S J. Multispectral imaging systems for airborne remote sensing to support agricultural production management. International Journal of Agricultural and Biological Engineering, 2010; 3(1): 50–62.
[3] Huang Y, Sui R, Thomson S J, Fisher D K. Estimation of cotton yield with varied irrigation and nitrogen treatments using aerial multispectral imagery. International Journal of Agricultural and Biological Engineering, 2013; 6(3): 37–41.
[4] Yao H, Huang Y. Remote sensing applications to precision farming. In: G. Wang and Q. Weng, editors, Remote sensing of natural resources. Chap 18. CRC Press, Boca Raton, FL. 2013; p. 333–352.
[5] Huang Y, Thomson S J. Remote sensing for cotton farming. In: Cotton, 2nd edition, Eds. D. D. Fang and R.G. Percy. American Society of Agronomy, Inc., Crop Science Society of America, and Soil Society of America, Inc. Madison, WI, USA, Agronomy Monograph, 2015; 57: 1–26.
[6] Huang Y, Thomson S J, Hoffman W C, Lan Y, Fritz B K. Development and prospect of unmanned aerial vehicles for agricultural production management. International Journal of Agricultural and Biological Engineering, 2013; 6(3): 1–10.
[7] Lamb D W, Brown R B. Remote sensing and mapping of weeds in crops –a review of airborne remote sensing. Journal of Agricultural Engineering Research, 2010; 78(2): 117–125.
[8] Thorp K, Tian L. A review on remote sensing of weeds in agriculture. Precision Agriculture, 2004; 5(5): 477–508.
[9] Deng W, Huang Y, Zhao C, Wang X. Discrimination of crop and weeds on visible and visible/near-infrared spectrums using support vector machines, artificial neural network and decision tree. Sensors and Transducers, 2014; 26: 26–34.
[10] Deng W, Huang Y, Zhao C, Chen L, Meng Z. Comparison of SVM, RBF-NN, and DT for crop and weed identification based on spectral measurement over corn fields. International Agricultural Engineering Journal, 2011; 20(1): 11–19.
[11] Deng W, Huang Y, Zhao C, Wang X. Identification of seedling cabbages and weeds using hyperspectral imaging. International Journal of Agricultural and Biological Engineering, 2015; 8(5): 65–72.
[12] Huang Y, Thomson S J, Ortiz B V, Reddy K N, Ding W, Zablotowicz R M, Bright Jr. J R. Airborne remote sensing assessment of the damage to cotton caused by spray drift from aerially applied glyphosate through spray deposition measurements. Biosystems Engineering, 2010; 7: 212–220.
[13] Huang Y, Reddy K N, Thomson S J, Yao H. Assessment of soybean injury from glyphosate using airborne multispectral remote sensing. Pesticide Management Science, 2015; 71: 545–552.
[14] Huang Y, Thomson S J, Molin W T, Reddy K N, Yao H. Early detection of soybean plant injury from glyphosate by measuring chlorophyll reflectance and fluorescence. Journal of Agricultural Science, 2012; 4(5): 117–124.
[15] Yao H, Huang Y, Hruska Z, Thomson S J, Reddy K N. Using vegetative index and modified derivative for early detection of soybean plant injury from glyphosate. Computers and Electronics in Agriculture, 2012; 89: 145–157.
[16] Rouse J W, Haas R H, Schell J A, Deering D W. Monitoring vegetation systems in the great plains with ERTS. Proc 3rd ERTS Symposium NASA SP-351, Vol. 1, NASA, Washington, DC, 1973; 309–317.
[17] Gitelson A A, Kaufman Y J, Merzlyak M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ., 1996; 58: 289–298.
[18] Lee, M A, Huang, Y, Haibo, Y, Thomson, S J, Bruce, L M. Determining the effect of storage of cotton and soybean leaf samples for hyperspectral analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014; 7(6): 2562–2570.
[19] Lee, M A, Huang, Y, Yao, H, Thomson, S J, Bruce, L M. Effects of sample storage on spectral reflectance changes in corn leaves excised from the field. Journal of Agricultural Science, 2014; 6(8): 214–220.
[20] Heap I M. International Survey of Herbicide Resistant Weeds. www.weedscience.org. Accessed on [2015-03-10].
[21] Reddy K N, Huang Y, Lee M A, Nandula V K, Fletcher R S, Thomson S J, Zhao F. Glyphosate-resistant and glyphosate-susceptible Palmer amaranth (Amaranthus palmeri S. Wats.): hyperspectral reflectance properties of plants and potential for classification. Pest. Manag. Sci. 2014; 70: 1910–1917. doi: 10.1002/ps.3755.
[22] Lee, M A, Huang, Y, Nandula, V K, Reddy, K N. Differentiating glyphosate-resistant and glyphosate-sensitive Italian ryegrass using hyperspectral imagery. In Sensing for Agriculture and Food Quality and Safety VI, Moon S. Kim; Kuanglin Chao, Editors, Proceedings of SPIE Vol. 9108 (SPIE, Bellingham, WA 2014), 91080B.
[23] Zhao F, Guo Y, Huang Y, Reddy K N, Zhao Y, Molin W T. Detection of the onset of glyphosate-induced soybean plant injury through chlorophyll fluorescence signal extraction and measurement. Journal of Applied Remote Sensing, 2015; 9(1): 1–12.
[24] Van der Tol C, Verhoef W, Rosema A. A model for chlorophyll fluorescence and photosynthesis at leaf scale. Agric. Forest Meteorol, 2009; 149: 96–105.
[25] Meroni M, Rossini M, Guanter L, Alonso L, Rascher U, Colombo R, Moreno J. Remote sensing of solar-induced chlorophyll fluorescence: review of methods and applications. Remote Sens. Environ, 2009; 113: 2037–2051.
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2016-03-31
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Huang, Y., Lee, M. A., Thomson, S. J., & Reddy, K. N. (2016). Ground-based hyperspectral remote sensing for weed management in crop production. International Journal of Agricultural and Biological Engineering, 9(2), 98–109. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/2137
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Information Technology, Sensors and Control Systems
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