Current status and future directions of precision aerial application for site-specific crop management in the USA

Computers and Electronics in Agriculture

Volumn 74, Issue 1, 2010, Pages 34-38

  Lan, Yubin ^a   Thomson, Steven J ^b   Huang, Yanbo ^b   Hoffmann, W Clint ^a   Zhang, Huihui ^c  

^a AGRICULTURAL RESEARCH SERVICE   (United States)

^b AGRICULTURAL RESEARCH SERVICE   (United States)

^c TEXAS A AND M UNIVERSITY   (United States)

Author keywords

GPS GIS; Precision agriculture; Remote sensing; Site specific management; Variable rate aerial application

Indexed keywords

AERIAL APPLICATION; AVIATION INDUSTRY; BUFFER AREAS; COTTON GROWTH; CURRENT STATUS; FUTURE DIRECTIONS; GLOBAL POSITIONING; GPS/GIS; PEST MANAGEMENT; PRECISION AGRICULTURE; PRECISION AGRICULTURE TECHNOLOGY; PRESCRIPTION MAP; SITE SPECIFIC MANAGEMENT; SITE-SPECIFIC CROP MANAGEMENT; VARIABLE RATE;

GEOGRAPHIC INFORMATION SYSTEMS; REMOTE SENSING;

CROPS;

AGRICULTURAL PRACTICE; AGRICULTURAL TECHNOLOGY; AIRCRAFT; COST-BENEFIT ANALYSIS; GIS; GPS; PEST CONTROL; PRECISION AGRICULTURE;

UNITED STATES;

GOSSYPIUM HIRSUTUM;

EID: 77956916079     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2010.07.001     Document Type: Review

References (28)

1. Bajwa S.G., Mozaffari M. Effect of N availability on vegetative index of cotton canopy: a spatial regression approach. Trans. ASABE 2007, 50(5):1883-1892.
2. Bongiovanni, R.G., Lowenberg-DeBoer, J.M., 2000. Nitrogen Management in Corn Using Site-Specific Crop Response Estimates from a Spatial Regression Model. In: Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, MN. Center for Precision Agriculture, University of Minnesota, St. Paul, MN, 16-19 July 2000.
3. Bongiovanni R.G., Lowenberg-DeBoer J.M. Economic of nitrogen response variability over space and time: results from the 1999-2001 field trials in Argentina. Proceedings of the 6th International Conference on Precision Agriculture 2002.
4. Dobermann A., Ping J.L. Geostatistical integration of yield monitor data and remote sensing improves yield maps. Agron. J. 2004, 96(1):285-297.
5. Goel P.K., Prasher S.O., Landry J.A., Patel R.M., Viau A.A., Miller J.R. Estimation of crop biophysical parameters through airborne and field hyperspectral remote sensing. Trans. ASAE 2003, 46(4):1235-1246.
6. GopalaPillai S., Tian L. In-field variability detection and spatial yield modeling for corn using digital aerial imaging. Trans. ASAE 1999, 42(6):1911-1920.
7. Huang Y., Lan Y., Hoffmann W.C. Use of airborne multi-spectral imagery in pest management systems. Agr. Eng. Int. 2008, X. Manuscript IT 07 010.
8. Huang Y., Thomson S.J., Lan Y., Maas S.J. Multispectral imaging system for Airborne remote sensing to support site-specific agricultural management. Proceedings of the 3rd Asian Conference on Precision Agriculture (ACPA) 2009.
9. Inman D., Khosla R., Reich R., Westfall D.G. Normalized difference vegetation index and soil color-based management zones in irrigated maize. Agron. J. 2008, 100(1):60-66.
10. Jang G.S., Sudduth K.A., Hong S.Y., Kitchen N.R., Palm H.L. Relating image-derived vegetation indices to crop yield. Proceedings of the 20th Biennial Workshop on Aerial Photography, Videography, and High-Resolution Digital Imagery for Resource Assessment, CD-ROM 2005.
11. Lambert D.M., Lowenberg-DeBoer J.M. Spatial regression models for yield monitor data: a case study from Argentina. American Agricultural Economics Association Annual Meeting 2003.
12. Lan L., Huang Y., Martin D.E., Hoffmann W.C. Development of an airborne remote sensing system for crop pest management: system integration and verification. Trans. ASABE 2009, 25(4):607-615.
13. Misaghi F., Dayyanidardashti S., Mohammadi K., Ehsani M.R. Application of Artificial Neural Network and Geostatistical Methods in Analyzing Strawberry Yield Data. ASAE Paper Number: 041147 2004, ASAE, St. Joseph, MI.
14. Moran M.S., Inoue Y., Barnes E.M. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ. 1997, 61(3):319-346.
15. Pinter P.J., Hatfield J.L., Schepers J.S., Barnes E.M., Moran M.S., Daughtry C.S.T., Upchurch D.R. Remote sensing for crop management. Photogram. Eng. Remote Sens. 2003, 69(6):647-664.
16. Senay G.B., Ward A.D., Lyon J.G., Fausey N.R., Nokes S.E. Manipulation of high spatial resolution aircraft remote sensing data for use in site-specific farming. Trans. ASAE 1998, 41(2):489-495.
17. Smith L.A., Thomson S.J. GPS position latency determination and ground speed calibration for the Satloc Airstar M3. Appl. Eng. Agric. 2005, 21(5):769-776.
18. Stein A., Brouwer J., Bouma J. Methods for comparing spatial variability patterns of millet yield and soil data. Soil Sci. Soc. Am. J. 1997, 61:861-870.
19. Thomson S.J., Smith L.A., Hanks J.E. Evaluation of application accuracy and performance of a hydraulically operated variable-rate aerial application system. Trans. ASABE 2009, 52(3):715-722.
20. Thomson S.J., Huang Y., Hanks J.E., Martin D.E., Smith L.A. Improving flow response of a variable-rate aerial application system by interactive refinement. Comput. Electron. Agr. 2010, 73(1):99-104.
21. Uno Y., Prasher S.O., Lacroix R., Goel R.K., Karimi Y., Viau A., Patel R.M. Artificial neural networks to predict corn yield from compact airborne spectrographic imager data. Comput. Electron. Agr. 2005, 47(2):149-161.
22. Yang C., Anderson G.L. Airborne videography to identify spatial plant growth variability for grain sorghum. Precision Agric. 1999, 1(1):67-79.
23. Yang C., Everitt J.H. Relationships between yield monitor data and airborne multispectral multidate digital imagery for grain sorghum. Precision Agric. 2002, 3(4):373-388.
24. Yang C., Everitt J.H., Bradford J.M., Murden D. Airborne hyperspectral imagery and yield monitor data for mapping cotton yield variability. Precision Agric. 2004, 5(5):445-461.
25. Yang C., Everitt J.H., Bradford J.M., Murden D. Comparison of airborne multispectral and hyperspectral imagery for estimating grain sorghum yield. Trans. ASABE 2009, 52(2):641-649.
26. Yao H., Tian L., Wang G., Colonna I.A. Soil Nutrient Mapping Using Aerial Hypersepctral Imaging and Soil Sampling Data - A Geostatistical Approach. ASAE Paper Number: 031046 2003, ASAE, St. Joseph, MI.
27. Zarco-Tejada P.J., Ustin S.L., Whiting M.L. Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agron. J. 2005, 97(3):641-653.
28. Zhang H., Lan Y., Lacey R., Hoffmann W.C., Huang Y. Analysis of vegetation indices derived from aerial multispectral and ground hyperspectral data. Int. J. Agric. Biol. Eng. 2009, 2(3):33-40.