Remote sensing for cotton farming

Cotton

Volumn 57, Issue , 2015, Pages 439-464

  Huang, Yanbo ^a   Thomson, Steven J ^a  

^a AGRICULTURAL RESEARCH SERVICE   (United States)

Author keywords

Advanced Very High Resolution Radiometer; Advanced wide field sensor; Airborne visible and near infrared; AVHRR; AVNIR; AWIFS; Bacillus thuringiensis; Bt; CCD; Charge coupled device; CIR; CLCV; Color infrared; Cotton leaf curl virus; Difference vegetation index; DVI; EC; Electrical conductivity; ET; Evapotranspiration; FCA; First canonical axis; First order sensitivity index; FOSI; Genetically modified; Glyphosate resistant; GM; GR; HAT; Herbicide resistant; Herbicide sensitive; Hours after treatment; HR; HS; Infrared transducer; IRT; LAI; Leaf area index; Leaf optical property spectra; Linear imaging self scanning system; LISA; LISS; Local indicator of spatial association; MCARI; Moderate Resolution Imaging Spectroradiometer; Modified chlorophyll absorption index; Modified triangular vegetation index; MODIS; MTVI; NDVI; Near infrared; NIR; Normalized difference vegetation index; Optimized soil adjusted vegetation index; OSAVI; PROSPECT; Radiance ratio; Ratio vegetation index; RDVI; Renormalized difference vegetation index: RR; RVI; SAR; SAVI; Self organized map; Simple ratio; Soil adjusted vegetation index; SOM; SR; Synthetic aperture radar; TCARI; Thematic mapper; Thermal infrared; TIR; TM; Total sensitivity index; Transformed chlorophyll absorption index; Transformed vegetation index; TSI; TVI; UAV; Unmanned aerial vehicle; WAGT; Weeks after glyphosate treatment

Indexed keywords

EID: 85102849974     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.2134/agronmonogr57.2013.0030     Document Type: Chapter

References (99)

1. Ahmed, A., A. Akhtar, B. Khalid, and A. Shamim. 2013. Correlation of meteorological parameters and remotely sensed normalized difference vegetation index (NDVI) with cotton leaf curl virus (CLCV). J. Phys.: Conf. Ser. 439:012044. doi:10.1088/1742-6596/439/1/012044
2. Ansari, M.S., R.K. Mahey, and S.S. Sidhu. 1999. Cotton yield prediction through spectral parameters. J. Ind. Soc. Remote Sensing. 27:185-192. doi:10.1007/BF02990831
3. Bai, J., J. Li, and S. Li. 2011. Monitoring the cotton growth conditions based on LAI from remote sensing and expert knowledge. Adv. Mater. Res. 317-319:1050-1057. doi:10.4028/ www.scientific.net/AMR.317-319.1050
4. Bai, J., J. Li, and S. Li. 2011. Monitoring the plant density of cotton with remotely sensed data. In: IFIP Advances in Information and Communication Technology. Computer and Computing Technologies in Agriculture IV 345. Springer, New York. p. 90-101. doi:10.1007/978-3-642-18336-2_11
5. Bai, J., J. Li, Q. Liu, X. Wang, and S. Li. 2012. Diagnosing cotton farmland quality using multi-temporal remotely sensed data. Sensor Letters. 10(1-2):475-483.
6. Barnes, E.M. 2004. Current implementation of site-specific technologies in U.S. cotton production. In: W. Gao, D.R. Shaw, editors, Remote sensing and modeling of ecosystems for sustainability. Proc. SPIE 4:12-19.
7. Bawden, F.C. 1933. Infra-red photography and plant virus diseases. Nature 132:168. doi:10.1038/132168a0
8. Brookes, G., and P. Barfoot. 2011. The income and production effects of biotech crops globally 1996-2009. Int. J. Biotechnol. 12:1-49. doi:10.1504/IJBT.2011.042680
9. Brooks, N.L. 2001. Characteristics and production costs of U.S. cotton farms. Stat. Bull. 9742. Economic Res. Serv., USDA, Washington, DC.
10. Campanella, R. 2000. Testing components toward a remote-sensing-based decision support system for cotton production. Photogramm. Eng. Remote Sensing 66(10):1219-1227.
11. Coleman, V.B., C.W. Johnson, and L.N. Lewis. 1974. Remote sensing in control of pink bollworm infestation in cotton. Calif. Agric. 28:10-12.
12. Colwell, R.N. 1956. Determining the prevalence of certain cereal crop diseases by means of aerial photography. Hilgardia 26:223-286. doi:10.3733/hilg.v26n05p223
13. Dalezios, N.R., A. Mplanta, and C. Doemnikiotis. 2011. Remotely sensed cotton evapotranspiration for irrigation water management in vulnerable agriculture of central Greece. J. Inf. Technol. Agric. 4(1):1-14.
14. Dill, G.M. 2005. Glyphosate-resistant crops: History, status, and future. Pest Manag. Sci. 61(3):219-224. doi:10.1002/ps.1008
15. Ding, W., K.N. Reddy, L.J. Krutz, S.J. Thomson, Y. Huang, and R.M. Zablotowicz. 2011. Biological response of soybean and cotton to aerial glyphosate drift. J. Crop Improv. 25:291-302. doi:10.1080/15427528.2011.559633
16. Dong, Q., E. Herman, and Z. Chen. 2008. Crop area assessment using remote sensing on the North China Plain. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 37(B8):957-962.
17. Doshi, R.A., R.L. King, and G.W. Lawrence. 2010. Classification of rotylenchulus reniformis numbers in cotton using remotely sensed hyperspectral data on self-organizing maps. J. Nematol. 42(3):179-193.
18. Dutta, S., S.A. Sharma, A.P. Khera, Ajai, M. Yadav, R.S. Hooda, K.E. Mothikumar, and M.L. Manchanda. 1994. Accuracy assessment in cotton acreage estimation using Indian remote sensing satellite data. ISPRS J. Photogramm. Remote Sens. 49(6):21-26. doi:10.1016/0924-2716(94)90011-6
19. Economic Research Service. 2014. Adoption of genetically engineered crops in the U.S., USDA, Washington, DC. http://www.ers.usda.gov/data-products/adoption-of-geneti-cally-engineered-crops-in-the-us.aspx (accessed date 25 May 2014)
20. Falkenberg, N.R., P Giovanni, J.T. Cothren, D.I. Leskovar, and C.M. Rush. 2007. Remote sensing of biotic and abiot ic stress for irrigation management of cotton. Agric. Water Manage. 87(1):23-31. doi:10.1016/j.agwat.2006.05.021
21. Farias de Oliveira, L. 2008. Reflectance sensors to predict mid-season nitrogen need of cotton. M.S. thesis, Univ. of Missouri-Columbia, Columbia.
22. Fulton, J., D. Sullivan, J. Shaw, M. Dougherty, and G. Bland. 2007. Site-specific management of cotton production using remote sensed thermal imagery within a conservation tillage system. Southern Conservation Agriculture Systems Conference, Quincy, FL, 25-27 June.
23. Garatuza-Payan, J., and C.J. Watts. 2005. The use of remote sensing for estimating ET of irrigated wheat and cotton in Northwest Mexico. Irrig. Drain. Syst. 19:301-320. doi:10.1007/ s10795-005-5192-2
24. Henry, W.B., D.R. Shaw, K.R. Reddy, L.M. Bruce, and H.D. Tamhankar. 2004. Remote sensing to detect herbicide drift on crops. Weed Technol. 18:358-368. doi:10.1614/WT-03-098
25. Huang, Y., S.J. Thomson, Y. Lan, and S.J. Maas. 2010a. Multispectral imaging systems for airborne remote sensing to support agricultural production management. Int. J. Agric. Biol. Eng. 3(1):50-62.
26. Huang, Y., S.J. Thomson, B.V. Ortiz, K.N. Reddy, W. Ding, R.M. Zablotowicz, and J.R. Bright, Jr. 2010b. Airborne remote sensing assessment of the damage to cotton caused by spray drift from aerially applied glyphosate through spray deposition measurements. Biosystems Eng. 107:212-220. doi:10.1016/j.biosystemseng.2010.08.003
27. Huang, Y., R. Sui, S.J. Thomson, and D.K. Fisher. 2013. Estimation of cotton yield with varied irrigation and nitrogen treatments using aerial multispectral imagery. Int. J. Agric. Biol. Eng. 6(2):37-41.
28. Huang, Y., S.J. Thomson, B.V. Ortiz, K.N. Reddy, W. Ding, R.M. Zablotowicz, and J.R. Bright, Jr. 2010c. Determination of cotton plant injury by aerial application of glyphosate using remote sensing and spray drift sampling. Proceedings of 2010 National Cotton Council, Beltwide Cotton Conference, New Orleans, LA, January 4-7.
29. Iqbal, J., P.R. Owens, and I. Ali. 2006. Application of remote sensing data to assess weed infestation in cotton. Agric. J. 1(4):186-191.
30. Jacquemoud, S., and F. Baret. 1990. PROSPECT: A model of leaf optical properties spectra. Remote Sens. Environ. 34(2):75-91. doi:10.1016/0034-4257(90)90100-Z
31. James, C. 2010. Global status of commercialized biotech/GM crops: 2010. Brief 42-2010. International Service for the Acquisition of Agri-Biotech Applications, Ithaca, NY. http:// www.isaaa.org/resources/publications/briefs/42/executivesummary/default.asp.
32. Jensen, J.R. 1996. Introductory digital image processing-A remote sensing perspective. Prentice Hall, Saddle River, NJ. p. 197-256.
33. Kirk, I.W. 2000. Aerial spray drift from different formulations of glyphosate. Trans. ASABE 43:555-559. doi:10.13031/2013.2735
34. Kumar, N., R. Niwas, M.L. Khichar, R.K. Saini, and B. Biswas. 2013. Effect of different growing environments on population dynamics of sucking pests in relation to various spectral indices in cotton. J. Indian Soc. Remote Sens. 41(2):309-317. doi:10.1007/ s12524-012-0218-3
35. Larson, J., R.K. Roberts, B.C. English, S.L. Larkin, M.C. Marra, S.W. Martin, K.W. Paxton, and J.M. Reeves. 2008. Factors affecting farmer's adoption of remotely sensed imagery for precision management in cotton production. Precis. Agric. 9:195-208. doi:10.1007/ s11119-008-9065-1
36. Lawrence, G.W., R.A. Doshi, R.L. King, K.S. Lawrence, and J. Caceres. 2007. Nematode management using remote sensing technology, self-organized maps and variable rate nematicide applications. Proceedings of World Cotton Research Conference 4. Lubbock, TX, September 10-14. International Cotton Advisory Committee, Washington, DC.
37. Lejeune, M., and T. Calmski. 2000. Canonical analysis applied to multivariate analysis of variance. J. Multivariate Anal. 72:100-119. doi:10.1006/jmva.1999.1852
38. Leon, C.T., D.R. Shaw, M.S. Cox, M.J. Abshire, B. Ward, and M.C. Wardlaw, III. 2003. Utility of remote sensing in predicting crop and soil characteristics. Precis. Agric. 4:359-384. doi:10.1023/A:1026387830942
39. Li, M., G. Zhao, M. Cai, Q. Zhao, and J. Tang. 2012. Extraction of cotton Information using remote sensing and precision management zoning at county scale. J. Nat. Resour. 27(11):1971-1980 (in Chinese).
40. Maas, S.J., G.J. Fitzgerald, and W.R. DeTar. 2000. Determining cotton leaf canopy temperature using multispectral remote sensing. Proceedings of Beltwide Cotton Conf. 1, San Antonio, TX. Cotton Foundation Publisher: Memphis, TN. p. 623-626.
41. Mallarino, A.P., and D.J. Wittry. 2004. Efficacy of grid and zone soil sampling approaches for site-specific assessment of phosphorus, potassium, pH, and organic matter. Precis. Agric. 5:131-144. doi:10.1023/B:PRAG.0000022358.24102.1b
42. Moran, S., A. Vidal, D. Troufleau, J. Qi, T.R. Clarke, PJ. Pinter, Jr., T.A. Mitchell, Y. Inoue, and C.M.U. Neale. 1997. Combining multifrequency microwave and optical data for crop management. Remote Sens. Environ. 61(1):96-109. doi:10.1016/S0034-4257(96)00243-X
43. Mukherjee, S., S. Shashtri, C.K. Singh, B. Kumari, R. Kumari, A. Ram, A. Mukherjee, and B. Singh. 2009. Remote sensing techniques to infer Bt cotton in a part of Bhatinda, Punjab, India. Gene Conserve. 643-675.
44. Mukherjee, S., S. Shashtri, C.K. Singh, B. Kumari, R. Kumari, A. Ram, A. Singh, A. Mukherjee, and B. Singh. 2008. Genetically modified cotton species detection by LISS-III satellite data. Nat. Preceedings http://dx.doi.org/10.1038/npre.2008.2659.2.
45. Mulla, D.J. 2013. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. 114:358-371. doi:10.1016/j. biosystemseng.2012.08.009
46. Musande, V., K. Anil, and K. Karbhari. 2012. Cotton crop discrimination using fuzzy classification approach. J. Indian Soc. Remote Sens. 40:589-597.
47. Neblette, C.B. 1927. Aerial photography for the study of plant diseases. Photo-Era Mag. 58:346.
48. Nixon, P.R., D.E. Escobar, and R.L. Bowen. 1987. A multispectral false-color video imaging system for remote sensing applications. Proceedings of 11th Biennial Workshop on Color Aerial Photography and Videography in the Plant Sciences and Related Fields, Am. Soc. for Photogramm. and Remote Sens., Bethesda, MD. p. 295-305, 340.
49. Nixon, P.R., S.D. Lyda, M.D. Heilman, and R.L. Bowen. 1975. Incidence and control of cotton root rot observed with color infrared photography. Pub. No. MP1241. Agric. Exp. Station, Texas A&M, College Station, TX.
50. Ortiz, B.V., S.J. Thomson, Y. Huang, K.N. Reddy, and W. Ding. 2011. Determination of differences in crop injury from aerial application of glyphosate using vegetation indices. Comput. Electron. Agric. 77:204-213. doi:10.1016/j.compag.2011.05.004
51. Payne, N.J. 1993. Spray dispersal from aerial silvicultural glyphosate applications. Crop Prot. 12(6):463-469. doi:10.1016/0261-2194(93)90009-8
52. Pinter, P.J., Jr., J.L. Hatfield, J.S. Schepers, E.M. Barnes, M.S. Moran, C.S.T. Daughtry, and D.R. Upchurch. 2003. Remote sensing for crop management. Photogramm. Eng. Remote Sensing 69(6):647-664. doi:10.14358/PERS.69.6.647
53. Pinter, P.J., Jr., and R.J. Reginato. 1981. Thermal infrared techniques for assessing plant water status. In: Irrigation Scheduling for Water and Energy Conservation in the 80's. Proceedings of the ASAE Irrigation Scheduling Conference. St. Joseph, MI. ASAE.
54. Pinter, P.J., Jr., M.E. Stanghellini, R.J. Reginato, S.B. Idso, A.D. Jenkins, and R.D. Jackson. 1979. Remote detection of biological stress in plants with infrared thermography. Science 205:585-587. doi:10.1126/science.205.4406.585
55. Plant, R.E., D.S. Munk, B.R. Roberts, R.N. Vargas, R.L. Travis, D.W. Rains, and R.B. Hutmacher. 2001. Application of remote sensing to strategic questions in cotton management and research. J. Cotton Sci. 5:30-41.
56. Prabhakar, M., Y.G. Prasad, M. Thirupathi, G. Sreedevi, B. Dharajothi, and B. Ven-kateswarlu. 2011. Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae). Comput. Electron. Agric. 79:189-198. doi:10.1016/j.compag.2011.09.012
57. Qi, Y., Z. Duan, and X. Lv. 2011. Research of cotton canopy characteristic information by hyperspectral remote sensing data. Cotton Sci. 23(2):167-171.
58. Qi, Y., D. Wang, G. Chen, C. Huang, Z. Duan, Y. Chen, J. Yuan, and K. Yang. 2005. Research of crop canopy characteristic information using hyperspectral remote sensing data. Cotton Sci. 17(6):371-375.
59. Read, J.J., L. Tarpley, J.M. McKinion, and K.R. Reddy. 2002. Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton. J. Environ. Qual. 31:1442-1452. doi:10.2134/jeq2002.1442
60. Read, J.J., J. Iqbal, J.A. Thomasson, J.L. Willers, and J.N. Jenkins. 2004. Remote sensing in dryland cotton: Relation to yield potential and soil properties. In: Proceedings of SPIE 5153, ecosystems' dynamics, agricultural remote sensing and modeling, and site-specific agriculture, 5 Jan. p. 61-72. doi:10.1117/12.505875.
61. Ramarao, N. 2003. Conformity analysis of cotton crop using remote sensing and GIS. In: Proceedings of Map Asia Conference. Geospatial Media and Communications Pvt Ltd, Noida, Uttar Pradesh, India. p. 1-11.
62. Reddy, K.N., W. Ding, R.M. Zablotowicz, S.J. Thomson, Y. Huang, and L.J. Krutz. 2010. Biological responses to glyphosate drift from aerial application in non-glyphosateresistant corn. Pest Manag. Sci. 66:1148-1154. doi:10.1002/ps.1996
63. Reddy, K.N., and V.K. Nandula. 2012. Herbicide resistant crops: History, development and current technologies. Indian J. Agron. 57(1):1-7.
64. Reisig, D.D., and L.D. Godfrey. 2006. Remote sensing for detection of cotton aphid-(Homop-tera: Aphididae) and spider mite-(Acari: Tetranychidae) infested cotton in the San Joaquin Valley. Environ. Entomol. 35:1635-1646. doi:10.1603/0046-225X(2006)35[1635:R SFDOC]2.0.CO;2
65. Reisig, D.D., and L.D. Godfrey. 2010. Remotely sensing arthropod and nutrient stressed plants: A case study with nitrogen and cotton aphid (Hemiptera: Aphididae). Environ. Entomol. 39:1255-1263. doi:10.1603/EN09218
66. Richardson, A.J., M.M. Crawford, J.H. Everitt, and K.R. Summy. 1992. Monitoring the Rio Grande Valley cotton stalk destruction program with satellite imagery. Geocarto Int. 7(2):15-20. doi:10.1080/10106049209354362
67. Richardson, A.J., A.H. Gerbermann, K.R. Summy, and G.L. Anderson. 1993. Remote sensing techniques for monitoring the Rio Grande Valley cotton stalk destruction program. Geocarto Int. 8(3):15-22. doi:10.1080/10106049309354416
68. Ritchie, G.L., C.W. Bednarz, J. Whitaker, and C. Mills. 2005. Cotton irrigation timing using remote sensing. In: P. Dugger and D. Richter, editors, Proceedings of the Beltwide Cotton Conferences, New Orleans, LA, 4-7 Jan. 2005. National Cotton Counc. Am., Memphis, TN. p. 1956-1960.
69. Robertson, W., C. Jayroe, W. Baker, D. Plunkett, and T. Kirkpatrick. 2002. Remote sensing technologies used as management tools in cotton production. AAES Res. Ser. 502, Arkansas Soil Fertility Studies. Arkansas Agric. Exp. Station, Univ. of Arkansas, Fayetteville. p. 70-74.
70. Roboson, A., and J. Medway. 2009. Remote sensing applications for cotton. Australian Cottongrower 30(4):40-43.
71. Rowland, C. D. (2000). Crop tolerance to non-target and labeled herbicide applications. MS thesis. Department of Plant and Soil Sciences, Mississippi State University, Mississippi State.
72. Shifa, M.S., M.S. Naweed, M. Omar, M.Z. Jhandir, and T. Ahmed. 2011. Classification of cotton and sugarcane plants on the basis of their spectral behavior. Pak. J. Bot. 43(4):2119-2125.
73. Singh, S.K., S. Dutta, and N. Dharaiya. 2013. Estimation of crop evapotranspiration of cotton using remote sensing technique. Int. J. Environ. Eng. Manage. 4(5):523-528.
74. Stafford, J.V. 2000. Implementing precision agriculture in the 21st century. J. Agric. Eng. Res. 76:267-275. doi:10.1006/jaer.2000.0577
75. Steddom, K., D. Jones, and C. Rush. 2005. A picture is worth a thousand words. Am. Phy-topathol. Soc. http://www.apsnet.org/publications/apsnetfeatures/Documents/2005/ RemoteSensing.pdf. (accessed date 15 April 2014).
76. Sudbrink, D.L., Jr., F.A. Harris, J.T. Robbins, P.J. English, and J.L. Willers. 2003. Evaluation of remote sensing to identify variability in cotton plant growth and correlation with larval densities of beet armyworm and cabbage looper (Lepidoptera: Noctuidae). Fla. Entomol. 86(3):290-294. doi:10.1653/0015-4040(2003)086[0290:EORSTI]2.Q.CO;2
77. Sui, R., and J.A. Thomasson. 2006. Ground-based sensing system for cotton nitrogen status determination. Trans. ASABE 49(6):1983-1991. doi:10.13031/2013.22279
78. Tarpley, L., K.R. Reddy, and G.F. Sassenrath-Cole. 2000. Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration. Crop Sci. 40:1814-1819. doi:10.2135/cropsci2000.4061814x
79. Taubenhaus, J.J., W.N. Ezekiel, and C.B. Neblette. 1929. Airplane photography in the study of cotton root rot. Phytopathology 19:1025-1029.
80. Thomson, S.J., C.M. Ouellet-Plamondon, S.L. DeFauw, Y. Huang, D.K. Fisher, and PJ. English. 2012. Potential and challenges in use of thermal imaging for humid region irrigation system management. J. Agric. Sci. 4(4):103-116.
81. Tubana, B.S. 2010. Improving nitrogen use efficiency in cotton: A remote sensor-based approach. In: Proceedings of the 13th Annual National Conservation Systems Cotton and Rice Conference, Tunica, MS. Cotton Incorporated, Cary, NC. p. 32-34.
82. Vincini, M., E. Frazzi, and P.D. Alessio. 2008. A broad-band leaf chlorophyll vegetation index at the canopy scale. Precis. Agric. 9:303-319. doi:10.1007/s11119-008-9075-z
83. Wang, Q., K. Wang, S. Li, C. Xiao, J. Dai, and L. Fang. 2012. Cotton planting area extraction based on multi-temporal HJ remote sensing. Cotton Sci. 24(6):503-510 (in Chinese).
84. Willers, J.L., J.N. Jenkins, W.L. Ladner, P.D. Gerard, D.L. Boykin, K.B. Hood, P.L. McKibben, S.A. Samson, and M.M. Bethel. 2005. Site-specific approaches to cotton insect control. Sampling and remote sensing analysis techniques. Precis. Agric. 6:431-452. doi:10.1007/s11119-005-3680-x
85. Willers, J.L., J.M. McKinion, and J.N. Jenkins. 2006. Remote sensing, sampling and simulation applications in analyses of insect dispersion and abundance in cotton. USDA Forest Serv. Proc. RMRS-P-42CD, p. 879-885.
86. Yang, C., C.J. Fernandez, and J.H. Everitt. 2005a. Mapping Phymatotrichum root rot of cotton using airborne three-band digital imagery. Trans. ASAE 48(4):1619-1626. doi:10.13031/2013.19176
87. Yang, C., C.J. Fernandez, and J.H. Everitt. 2010. Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot. Biosystems Eng. 107:131-139. doi:10.1016/j.biosystemseng.2010.07.011
88. Yang, C., S.M. Greenberg, and J.H. Everitt. 2005b. Evaluation of herbicide-based cotton regrowth control using remote sensing. Trans. ASAE 48(5):1987-1994. doi:10.13031/2013.19991
89. Yang, C., S.M. Greenberg, J.H. Everitt, and J.W. Norman, Jr. 2006. Assessing cotton stalk destruction with herbicides using remote sensing technology. J. Cotton Sci. 10:136-145.
90. Yang, C., G.N. Odvody, C.J. Fernandez, J.A. Landivar, R.R. Minzenmayer, R.L. Nichols, and J.A. Thomasson. 2014. Monitoring cotton root rot progression within a growing season using airborne multispectral imagery. J. Cotton Sci. 18:85-93.
91. Yao, H., and Y. Huang. 2013. 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. p. 333-352.
92. Yones, M.S., S. Arafat, A.F. Abou Hadid, H.A. Abd Elrahman, and H.F. Dahi. 2012. Determination of the best timing for control application against cotton leaf worm using remote sensing and geographical information techniques. Egyptian J. Remote Sens. Space Sci. 15:151-160. doi:10.1016/j.ejrs.2012.05.004
93. Zarco-Tejada, P.J., S.L. Ustin, and M.L. Whiting. 2005. Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agron. J. 97(3):641-653. doi:10.2134/agronj2003.0257
94. Zhang, H., Y. Lan, C. Suh, J. Westbrook, W.C. Hoffmann, C. Yang, and Y. Huang. 2013. Fusion of remotely sensed data from airborne and ground-based sensors to enhance detection of cotton plants. Comput. Electron. Agric. 93:55-59. doi:10.1016/j.compag.2013.02.001
95. Zhang, J., Y. Cheng, F. Zhang, D. Yue, X. Guo, H. Dong, J. Wang, and H. Tang. 2012. Multitemporal remote sensing image-based extraction on the crop planting information. Int. J. Remote Sens. Appl. 2(4):31-40.
96. Zhang, N., M. Wang, and N. Wang. 2002. Precision agriculture-a worldwide overview. Comput. Electron. Agric. 36:113-132. doi:10.1016/S0168-1699(02)00096-0
97. Zhao, D., K.R. Reddy, V.G. Kakani, J.J. Read, and S. Koti. 2005. Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton. Agron. J. 97:89-98. doi:10.2134/agronj2005.0089
98. Zhao, F., Y. Guo, Y. Huang, K.N. Reddy, M.A. Lee, R.S. Fletcher, and S.J. Thomson. 2014b. Early detection of crop injury from herbicide glyphosate by leaf biochemical parameter inversion. Int. J. Appl. Earth Obs. Geoinf. 31:78-85. doi:10.1016/j.jag.2014.03.010
99. Zhao, F., Y. Huang, Y. Gao, K.N. Reddy, M.A. Lee, R.S. Fletcher, and S.J. Thomson. 2014a. Early detection of crop injury from glyphosate on soybean and cotton using plant leaf hyperspectral data. Remote Sens. 6:1538-1563. doi:10.3390/rs6021538