<i> Palmer amaranth </i> ( <i> Amaranthus palmeri </i> S. Wats.) is a major weed problem of cotton ( <i> Gossypium hirsutum </i> L.) production systems in the southern United States. Hyperspectral remote sensing has shown promise as a tool for crop weed discrimination, and there is a growing interest in using this technology for identifying weeds in cotton production systems. Information is lacking on differentiating <i> Palmer amaranth </i> from cotton with an okra leaf structure based on canopy hyperspectral reflectance properties. Two greenhouse studies were conducted to compare canopy hyperspectral reflectance profiles of <i> Palmer amaranth </i> to canopy hyperspectral reflectance profiles of okra and super-okra leaf cotton and to identify optimal regions of the electromagnetic spectrum for their discrimination. Ground-based hyperspectral measurements of the plant canopies were obtained with a spectroradiometer (400 - 2350 nm range). Analysis of variance (ANOVA, <i> p </i> ≤ 0.05), Dunnett’s test ( <i> p </i> ≤ 0.05), and difference and sensitivity measurements were tabulated to determine the optimal wavebands for <i> Palmer amaranth </i> and cotton discrimination. Results were inconsistent for <i> Palmer amaranth </i> and okra leaf cotton separation. Optimal wavebands for distinguishing <i> Palmer amaranth </i> from super-okra leaf cotton were observed in the shortwave infrared region (2000 nm and 2180 nm) of the optical spectrum. Ground-based and airborne sensors can be tuned into the shortwave infrared bands identified in this study, facilitating application of remote sensing technology for <i> Palmer amaranth </i> discrimination from super-okra leaf cotton and implementation of the technology as a decision support tool in cotton weed management programs.
Andres, R.J., Bowman, D.T., Jones, D.C. and Kuraparthy, V. (2016) Major Leaf Shapes of Cotton: Genetics and Agronomic Effects in Crop Production. The Journal of Cotton Science, 20, 330-340.
Nichols, R.L., Bond, J., Culpepper, A.S., Dodds, D., Nandula, V., Main, C.L., et al. (2009) Glyphosate-Resistant Palmer amaranth (Amaranthus palmeri) Spreads in the Southern United States (U.S.). Resistant Pest Management Newsletter, 18, 8-10.
Ward, S.M., Webster, T.M. and Steckel, L.E. (2013) Palmer amaranth (Amaranthus palmeri): A Review. Weed Technology, 27, 12-27. https://doi.org/10.1614/WT-D-12-00113.1
Webster, T.M. and Nichols, R.L. (2012) Changes in the Prevalence of Weed Species in the Major Agronomic Crops of the Southern United States: 1994/1995 to 2008/2009. Weed Science, 60, 145-157. https://doi.org/10.1614/WS-D-11-00092.1
Morgan, G.D., Baumann, P.A. and Chandler, J.M. (2001) Competitive Impact of Palmer Amaranth (Amaranthus palmeri) on Cotton (Gossypium hirsutum) Development and Yield. Weed Technology, 15, 408-412. https://doi.org/10.1614/0890-037X(2001)015[0408:CIOPAA]2.0.CO;2
MacRae, A.W., Culpepper, A.S., Webster, T.M., Sosnoskie, L.M. and Kichler, J.M. (2008) Glyphosate-Resistant Palmer amaranth Competition with Roundup Ready Cotton. Proceedings of the 2008 Beltwide Cotton Conference, Nashville, 8-11 January 2008, 1696.
Keeley, P.E., Carter, C.H. and Thullen, R.J. (1987) Influence of Planting Date on Growth of Palmer amaranth (Amaranthus palmeri). Weed Science, 35, 199-204.
Salas, R.A., Burgos, N.R., Tranel, P.J., Singh, S., Glasgow, L., Scoot, R.C., et al. (2016) Resistance to PPO-Inhibiting Herbicide in Palmer amaranth from Arkansas. Pest Management Science, 72, 864-869. https://doi.org/10.1002/ps.4241
Lindsay, K., Popp, M., Norsworthy, J., Bagavathiannan, M., Powles, S. and Lacoste, M. (2017) PAM: Decision Support for Long-Term Palmer amaranth (Amaranthus palmeri) Control. Weed Technology, 31, 915-927. https://doi.org/10.1017/wet.2017.69
Thorp, K. and Tian, L. (2004) A Review on Remote Sensing of Weeds in Agriculture. Precision Agriculture, 5, 477-508. https://doi.org/10.1007/s11119-004-5321-1
Smith, A.M. and Blackshaw, R.E. (2003) Weed-Crop Discrimination Using Remote Sensing:a Detached Leaf Experiment. Weed Technology, 17, 811-820. https://doi.org/10.1614/WT02-179
Koger, C.H., Shaw, D.R., Reddy, K.N. and Bruce, L.M. (2004) Detection of Pitted Morningglory (Ipomoea lacunosa) with Hyperspectral Remote Sensing. II. Effects of Vegetation Ground Cover and Reflectance Properties. Weed Science, 52, 230-235. https://doi.org/10.1614/WS-03-083R1
Peña-Barragñn, J.M., López-Granados, F., Jurado-Expósito, M. and Garciá-Torres, L. (2006) Spectral Discrimination of Ridolfia segetum and Sunflower as Affected by Phenological Stage. Weed Research, 46, 10-21. https://doi.org/10.1111/j.1365-3180.2006.00488.x
Fletcher, R.S., Reddy, K.N. and Turley, R.B. (2016) Spectral Discrimination of Two Pigweeds from Cotton with Different Leaf Colors. American Journal of Plant Sciences, 7, 2138-2150. https://doi.org/10.4236/ajps.2016.715190
Garfagnoli, F., Ciampalini, A., Moretti, S., Chiarantini, L. and Vettori, S. (2013) Quantitative Mapping of Clay Minerals Using Airborne Imaging Spectroscopy: New Data on Mugello (Italy) from SIM-GA Prototypal Sensor. European Journal of Remote Sensing, 46, 1-17. https://doi.org/10.5721/EuJRS20134601
Danner, M., Locherer, M., Hank, T. and Richter, K. (2015) Spectral Sampling with the ASD FieldSpec 4 Theory, Measurement, Problems, Interpretation. EnMAP Field Guides Technical Report, GFZ Data Services.
Savitzky, A. and Golay, M.J.E. (1964) Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 36, 1627-1639. https://doi.org/10.1021/ac60214a047
Lehnert, L.W., Meyer, H. and Bendix, J. (2018) HSDAR: Manage, Analyze, and Simulate Hyperspectral Data in R. R Package Version 0.7.1.
R Core Team (2017) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/
Steele, R.G.D. and Torrie, J.H. (1960) Principles and Procedures of Statistics. McGraw-Hill, New York.
Clewer, A.G. and Scarisbrick, D.H. (2001) Practical Statistics and Experimental Design for Plant and Crop Science. John Wiley and Sons, New York.
Carter, G.A., Dell, T.R. and Cibula, W.G. (1996) Spectral Reflectance Characteristics and Digital Imagery of a Pine Needle Blight in the Southeastern United States. Canadian Journal of Forestry Research, 26, 402-407. https://doi.org/10.1139/x26-045
Jensen, J.R. (2000) Remote Sensing of the Environment: An Earth Resource Perspective. 2nd Edition, Prentice-Hall, Upper Saddle River.
Campbell, J.B. (2000) Introduction to Remote Sensing. 3rd Edition, The Guilford Press, New York.
Sahoo, R.N., Ray, S.S. and Manjunath, K.R. (2015) Hyperspectral Remote Sensing of Agriculture. Current Science, 108, 848-859.
Thenkabail, P.S., Gumma, M.K., Teluguntla, P. and Mohammed, I.A. (2014) Hyperspectral Remote Sensing of Vegetation and Agricultural Crops. Photogrammetric Engineering and Remote Sensing, 80, 697-709.
Govender, M., Dye, P.J., Weiersbye, I.M., Witkowski, E.T.F. and Ahmed, F. (2009) Review of Commonly Used Remote Sensing and Ground-Based Technologies to Measure Plant Water Stress. Water SA, 35, 741-752. https://doi.org/10.4314/wsa.v35i5.49201
Andries, J.A., Jones, J.E., Sloane, L.W. and Marshall, J.G. (1969) Effects of Okra Leaf Shape on Boll Rot, Yield, and Other Important Characters of Upland Cotton, Gossypium hirsutum L. Crop Science, 9, 705-710. https://doi.org/10.2135/cropsci1969.0011183X000900060009x
Wells, R., Meredith, W.R. and Williford, J.R. (1986) Canopy Photosynthesis and Its Relationship to Plant Productivityin Near-Isogenic Cotton Lines Differing in Leaf Morphology. Plant Physiology, 82, 635-640. https://doi.org/10.1104/pp.82.3.635
Zhu, W., Liu, K. and Wang, X.D. (2008) Heterosis in Yield, Fiber Quality, and Photosynthesis of Okra Leaf Oriented Hybrid Cotton (Gossypium hirsutum L.). Euphytica, 164, 283-291. https://doi.org/10.1007/s10681-008-9732-3