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Engineering >> 2020, Volume 6, Issue 5 doi: 10.1016/j.eng.2019.10.015

Remote Sensing and Precision Agriculture Technologies for Crop Disease Detection and Management with a Practical Application Example

Aerial Application Technology Research Unit, Agricultural Research Service, US Department of Agriculture, College Station, Texas 77845, USA

Received: 2018-09-16 Revised: 2019-07-17 Accepted: 2019-10-08 Available online: 2020-03-24

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Abstract

Remote sensing technology has long been used to detect and map crop diseases. Airborne and satellite imagery acquired during growing seasons can be used not only for early detection and within-season management of some crop diseases, but also for the control of recurring diseases in future seasons. With variable rate technology in precision agriculture, site-specific fungicide application can be made to infested areas if the disease is stable, although traditional uniform application is more appropriate for diseases that can spread rapidly across the field. This article provides a brief overview of remote sensing and precision agriculture technologies that have been used for crop disease detection and management. Specifically, the article illustrates how airborne and satellite imagery and variable rate technology have been used for detecting and mapping cotton root rot, a destructive soilborne fungal disease, in cotton fields and how site-specific fungicide application has been implemented using prescription maps derived from the imagery for effective control of the disease. The overview and methodologies presented in this article should provide researchers, extension personnel, growers, crop consultants, and farm equipment and chemical dealers with practical guidelines for remote sensing detection and effective management of some crop diseases.

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References

[ 1 ] Taubenhaus JJ, Ezekiel WN, Neblette CB. Airplane photography in the study of cotton root rot. Phytopathology 1929;19(6):1025–9. link1

[ 2 ] Colwell RN. Determining the prevalence of certain cereal crop diseases by means of aerial photography. Hilgardia 1956;26(5):223–86. link1

[ 3 ] Myers VI. Remote sensing applications in agriculture. In: Colwell RN, editor. Manual of remote sensing. Bethesda: American Society of Photogrammetry and Remote Sensing; 1983. p. 2111–228. link1

[ 4 ] Ryerson RA, Curran PJ, Stephens PR. Applications: agriculture. In: Philipson WR, editor. Manual of photographic interpretation. Bethesda: American Society for Photogrammetry and Remote Sensing; 1997. p. 365–97. link1

[ 5 ] Nixon PR, Escobar DE, Bowen RL. A multispectral false-color video imaging system for remote sensing applications. In: Proceedings of the 11th Biennial Workshop on Color Aerial Photography and Videography in the Plant Sciences and Related Fields; 1987 Apr 27–May 1; Weslaco, TX, USA. Bethesda: American Society for Photogrammetry and Remote Sensing; 1987. p. 295–305,340. link1

[ 6 ] Cook CG, Escobar DE, Everitt JH, Cavazos I, Robinson AF, Davis MR. Utilizing airborne video imagery in kenaf management and production. Ind Crops Prod 1999;9(3):205–10. link1

[ 7 ] Fletcher RS, Skaria M, Escobar DE, Everitt JH. Field spectra and airborne digital imagery for detecting Phytophthora foot rot infections in citrus trees. HortScience 2001;36(1):94–7. link1

[ 8 ] Zhang M, Qin Z, Liu X. Remote sensed spectral imagery to detect late blight in field tomatoes. Precis Agric 2005;6(6):489–508. link1

[ 9 ] Yang C, Fernandez CJ, Everitt JH. Mapping Phymatotrichum root rot of cotton using airborne three-band digital imagery. Trans ASABE 2005;48(4):1619–26. link1

[10] Huang W, Lamb DW, Niu Z, Zhang Y, Liu L, Wang J. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precis Agric 2007;8(4–5):187–97. link1

[11] MacDonald SL, Staid M, Staid M, Cooper ML. Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards. Comput Electron Agric 2016;130:109–17. link1

[12] Lu J, Zhou M, Gao Y, Jiang H. Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves. Precis Agric 2018;19(3):379–94. link1

[13] Du Q, French JV, Skaria M, Yang C, Everitt JH. Citrus pest stress monitoring using airborne hyperspectral imagery. In: Proceedings of the International Geoscience and Remote Sensing Symposium; 2004 Sep 20–24; Anchorage, AK, USA. New York: IEEE; 2004. p. 3981–4. link1

[14] Yang C, Fernandez CJ, Everitt JH. Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot. Biosyst Eng 2010;107 (2):131–9. link1

[15] Kumar A, Lee WS, Ehsani RJ, Albrigo LG, Yang C, Mangan RL. Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques. J Appl Remote Sens 2012;6(1):063542. link1

[16] Li H, Lee WS, Wang K, Ehsani R, Yang C. ‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging. Precis Agric 2014;15(2):162–83. link1

[17] Chen X, Ma J, Qiao H, Cheng D, Xu Y, Zhao Y. Detecting infestation of take-all disease in wheat using Landsat Thematic Mapper imagery. Int J Remote Sens 2007;28(22):5183–9. link1

[18] Franke J, Menz G. Multi-temporal wheat disease detection by multi-spectral remote sensing. Precis Agric 2007;8(3):161–72. link1

[19] Santoso H, Gunawan T, Jatmiko RH, Darmosarkoro W, Minasny B. Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery. Precis Agric 2011;12(2):233–48. link1

[20] Yuan L, Pu R, Zhang J, Wang J, Yang H. Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale. Precis Agric 2016;17 (3):332–48. link1

[21] Li X, Lee WS, Li M, Ehsani R, Mishra AR, Yang C, et al. Feasibility study on huanglongbing (citrus greening) detection based on WorldView-2 satellite imagery. Biosyst Eng 2015;132:28–38. link1

[22] Garcia-Ruiz F, Sankaran S, Maja JM, Lee WS, Rasmussen J, Ehsani R. Comparison of two aerial imaging platforms for identification of huanglongbing-infected citrus trees. Comput Electron Agric 2013;91:106–15. link1

[23] Albetis J, Duthoit S, Guttler F, Jacquin A, Goulard M, Poilvé H, et al. Detection of Flavescence dorée grapevine disease using unmanned aerial vehicle (UAV) multispectral imagery. Remote Sens 2017;9(4):308. link1

[24] Mattupalli C, Moffet CA, Shah KN, Young CA. Supervised classification of RGB aerial imagery to evaluate the impact of a root rot disease. Remote Sens 2018;10(6):917. link1

[25] Yang C, Odvody GN, Thomasson JA, Isakeit T, Nichols RL. Change detection of cotton root rot infection over 10-year intervals using airborne multispectral imagery. Comput Electron Agric 2016;123:154–62. link1

[26] Yang C, Odvody GN, Thomasson JA, Isakeit T, Minzenmayer RR, Drake DR, et al. Site-specific management of cotton root rot using airborne and high resolution satellite imagery and variable rate technology. Trans ASABE 2018;61 (3):849–58. link1

[27] Escobar DE, Everitt JH, Noriega JR, Davis MR, Cavazos I. A true digital imaging system for remote sensing applications. In: Proceedings of the 16th Biennial Workshop on Color Photography and Videography in Resource Assessment; 1997 Apr 29–May 1, Weslaco, TX, USA. Bethesda: American Society for Photogrammetry and Remote Sensing; 1997. p. 470–84. link1

[28] Yang C. A high-resolution airborne four-camera imaging system for agricultural applications. Comput Electron Agric 2012;88:13–24. link1

[29] Yang C, Odvody GN, Fernandez CJ, Landivar JA, Minzenmayer RR, Nichols RL. Evaluating unsupervised and supervised image classification methods for mapping cotton root rot. Precis Agric 2015;16(2):201–15. link1

[30] Bramley RGV. Lessons from nearly 20 years of precision agriculture research, development, and adoption as a guide to its appropriate application. Crop Pasture Sci 2009;60(3):197–217. link1

[31] Schimmelpfennig D, Ebel R. On the doorstep of the information age: recent adoption of precision agriculture. Washington, DC: USDA Economic Research Service; 2011. link1

[32] Zhang Q. Precision agriculture technology for crop farming. Boca Raton: CRC Press; 2016. link1

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