Improving pest management with remote sensing

Outlooks on Pest Management

Volumn 17, Issue 5, 2006, Pages 197-201

  Shaw, David R ^a,c,d   Willers, Jeffrey L ^b,e  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

^b AGRICULTURAL RESEARCH SERVICE   (United States)

^c Department of Weed Science ^*  (United States)

^d Weed Science Society of America   (United Kingdom)

^e USDA ARS Genetics and Precision Agriculture Research Unit   (United States)

Author keywords

Diseases; Geographic information systems; Insects; Nematodes; Precision agriculture; Remote sensing; Site specific pest management; Weeds

Indexed keywords

HEXAPODA; INSECTA; NEMATODA;

EID: 33750220302     PISSN: 17431026     EISSN: None     Source Type: Journal    
DOI: 10.1564/17oct03     Document Type: Review

References (27)

1. Blazquez CH & Edwards GJ. 1986. Spectral reflectance of healthy and diseased watermelon leaves. Annals of Applied Biology 108, 2439.
2. Burris E, Costello R, Padgett B, Overstreet C & Wolcott M. 2003. Using remote sensing and GPS in Nematode Control. Louisiana Agriculture 46(2), 11-2.
3. Carrière Y, Ellsworth PC, Dutilleul P, Ellers-Kirk C, Barkley V & Antilla L. 2006. A GIS-based approach for areawide pest management: the scales of Lygus hesperus movements to cotton from alfalfa, weeds, and cotton. Entomologia Experimentalis et Applicata 118, 203-10.
4. Dupont JK, Campenella R, Seal MR, Willers JL & Hood KB. 2000. Spatially variable insecticide applications through remote sensing. Proc. Beltwide Cotton Conf., Vol. 2, 426-9.
5. Edmondson, RN. 2004. Past developments and future opportunities in the design and analysis of crop experiments. J. Agricultural Science 143, 27-33.
6. Gotway CA, Bullock DG, Pierce FJ, Stroup WW, Hergert GW & Eskridge KM. 1997. Experimental design issues and statistical evaluation techniques for site-specific management. In: The state of site-specific management for agriculture (F. J. Pierce & E. J. Sadler (eds)). Am. Soc. Agronomy, Crop Science Soc. America, and Soil Science Soc. America, Publishers. Madison, WI, USA.
7. th International Conference on Precision Agriculture and Other Precision Resources Management, ASA/SSSA/CSSA, Madison, Wisconsin, USA. (In press).
8. Guyer DE, Miles GE, Gaultney LD & Schreiber MM. 1993. Application of machine vision to shape analysis in leaf and plant identification. Transactions of the ASAE 36, 163-71.
9. Hanks JE & Beck JL. 1998. Sensor-controlled hooded sprayer for row crops. Weed Technology 12, 308-14.
10. Henry, W.B., D.R. Shaw & L.M. Bruce. 2004. Spectral reflectance curves to distinguish soybean from common cocklebur (Xanthium strumarium) and sicklepod (Cassia obtusifolia) grown from varying soil moisture. Weed Science 52, 788-96.
11. Hong, N., White, J. G, Gompertz, M. L. & Weisz, R. 2005. Spatial Analysis of Precision Agriculture Treatments in Randomized Complete Blocks: Guidelines for Covariance Model Selection. Agonomy J. 97, 1082-96.
12. Jensen JR. 1986. Introductory Digital Image Processing. A Remote Sensing Perspective. Prentice-Hall, Englewood Cliffs, NJ, USA.
13. Koger CH, Bruce LM, Shaw DR & Reddy KN. 2003. Wavelet analysis of hyperspectral reflectance data for detecting pitted morningglory (Ipomoea lacunosa) in soybean (Glycine max). Remote Sensing of the Environment 86, 108-19.
14. Lambert DM, Lowenberg-Deboer J & Bongovanni R. 2004. A comparison of four spatial regression models for yield monitor data: A case study from Argentina. Precision Agriculture 5, 579-600.
15. Lark RM & Wheeler HC. 2003. A Method to Investigate Within-field Variation of the Response of Combinable Crops to an Input. Agronomy J. 95, 1093-104.
16. Lawrence GW, Kelley AT, King RL, Vickery J, Lee HK & McLean KS. 2004. Remote sensing and precision nematicide applications for Rotylenchulus reniformis management in Mississippi cotton. Nematology Monographs and Perspectives 2, 1321.
17. Marshall EJP. 1989. Distribution patterns of plants associated with arable field edges. J. Applied Ecology 26, 247-57.
18. Medlin CR & Shaw DR. 2000. Economic comparison of broadcast and site specific herbicide applications in non-tran genic and glyphosatetolerant Glycine max. Weed Science 48, 65361.
19. Pearce, S & Zalucki MP. 2006. Do predators aggregate in response to pest density in agroecosystems? Assessing within-field spatial patterns. J Applied Ecology 43, 128-40.
20. Pringle MJ, Cook SE & McBratney AB. 2004a. Field-scale Experiments for site-specific crop management. Part I: Design considerations. Precision Agriculture 5, 617-24.
21. Pringle MJ, McBratney AB & Cook SE. 2004b. Field-scale Experiments for site-specific crop management. Part II: A geostatistical analysis. Precision Agriculture 5, 625-45.
22. Shaw DR & Kelley FS. 2005. Evaluating remote sensing for determining and classifying crop anomalies. Precision Agriculture 6, 421-30.
23. Whelan BM & McBratney AB. 2000. The "Null Hypothesis" of precision agriculture management. Precision Agriculture 2, 265-79.
24. Willers J, Seal MR & Luttrell R. 1999. Remote sensing, line-intercept sampling for tarnished plant bugs (Heteroptera: Miridae) in Mid-south cotton. J Cotton Science 3, 160-70.
25. th Appl. Stat. Agric. Conf., Manhattan, KS, USA, Apr. 25-28, 2004. Stat. Dept., Kansas St. Univ., Manhattan, KS.
26. Willers J, Jenkins J, Ladner W, Gerard P, Boykin D, Hood K, McKibben P, Samson S, & Bethel M. 2005. Site-specific approaches to cotton insect control, sampling and remote sensing analysis techniques. Precision Agriculture 6, 431-52.
27. Zhang M, Qin Z, & Liu X. 2005. Remote sensed spectral imagery to detect late blight in field tomatoes. Precision Agriculture 6, 489-508.