Application of satellite, unmanned aircraft system, and ground-based sensor data for precision agriculture: A review

2017 ASABE Annual International Meeting

Volumn , Issue , 2017, Pages

  Rudd, Joshua D ^a   Roberson, Gary T ^a   Classen, John J ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

Agricultural aviation; Hyperspectral imagery; Multispectral imagery; Remote sensing; Satellite imagery

Indexed keywords

AGRICULTURE; FIGHTER AIRCRAFT; REMOTE SENSING; SATELLITE IMAGERY; SATELLITES; SPECTROSCOPY;

ENVIRONMENTAL CONDITIONS; HYPER-SPECTRAL IMAGERIES; MULTI-SPECTRAL IMAGERY; PRECISION AGRICULTURE; REMOTE SENSING PLATFORMS; SATELLITE CONSTELLATIONS; SPATIAL AND TEMPORAL RESOLUTIONS; UNMANNED AIRCRAFT SYSTEM;

UNMANNED AERIAL VEHICLES (UAV);

EID: 85035111844     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.13031/aim.201700272     Document Type: Conference Paper

References (32)

1. ASD Inc. (2017). FieldSpec: The gold standard in field spectroradiometers. Retrieved from https://www.asdi.com/products-and-services/fieldspec-spectroradiometers
2. Arthur, A. M., & Robinson, I. (2015). A critique of field spectroscopy and the challenges and opportunities it presents for remote sensing for agriculture, ecosystems, and hydrology. Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, 9637
3. Ballesteros, R., Ortega, J., Hernández, D., & Moreno, M. (2014). Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. part II: Application to maize and onion crops of a semi-arid region in spain. Precision Agriculture, 15(6), 593-614. doi:10.1007/s11119-014-9357-6
4. Basso, B., Fiorentino, C., Cammarano, D., & Schulthess, U. (2016). Variable rate nitrogen fertilizer response in wheat using remote sensing. Precision Agriculture, 17(2), 168-182. doi:10.1007/s11119-015-9414-9
5. Bausch, W. C., Halvorson, A. D., & Cipra, J. (2008). Quickbird satellite and ground-based multispectral data correlations with agronomic parameters of irrigated maize grown in small plots. Biosystems Engineering, 101(3), 306-315. doi:10.1016/j.biosystemseng.2008.09.011
6. Berni, J., Zarco-Tejada, P. J., Suarez, L., & Fereres, E. (2009). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 722-738. doi:10.1109/TGRS.2008.2010457
7. Candiago, S., Remondino, F., Giglio, M. D., & Gattelli, M. (2015). Evaluating multispectral images and vegetation indices for precision farming applications from U AV images. Remote Sensing, 7, 4026-4047.
8. Cao, Q., Miao, Y., Wang, H., Huang, S., Cheng, S., Khosla, R., & Jiang, R. (2013). Non-destructive estimation of rice plant nitrogen status with CropCircle multispectral active canopy sensor. Field Crops Research, (154), 133-144.
9. Caturegli, L., Casucci, M., Lulli, F., Grossi, N., Gaetani, M., Magni, S.,⋯ Volterrani, M. (2015). GeoEye-1 satellite versus ground-based multispectral data for estimating nitrogen status of turfgrasses. International Journal of Remote Sensing, 36(8), 2238. doi:10.1080/01431161.2015.1035409
10. Cuca, B., Barazzetti, L., Brumana, R., & Previtali, M. (2015). An insight into space and remote sensing technologies concerning agriculture and landscape analysis. Third International Conference on Remote Sensing and Geoinformation of the Environment
11. Digital Globe. (2013). WorldView-3. Retrieved from http://content.satimagingcorp. com.s3.amazonaws.com/static/satellite-sensor-specification/WorldView-3-PDF-Download.pdf
12. DJI. (2017). Phantom 4 pro. Dà-Jiang Innovations Science and Technology Co., Ltd. Retrieved from https://store.dji.com/shop/phantom-series?from=menu-products
13. Erdle, K., Mistele, B., & Schmidhalter, U. (2011). Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Research, 124(1), 74-84. doi:10.1016/j.fcr.2011.06.007
14. Equipment World. (2017). Drones. Retrieved from http://www.equipmentworld.com/drones
15. Federal Aviation Administration. (2016). Operation and certification of small unmanned aircraft systems. Retrieved from https://www.faa.gov/uas/media/RIN-2120-AJ60-Clean-Signed.pdf
16. GeoEye. (2007). GeoEye-1 fact sheet. Retrieved from http://content.satimagingcorp. com.s3.amazonaws.com/static/satellite-sensor-specification/GeoEye-1-PDF-Download.pdf
17. Holland Scientific. (2017). Crop circle ACS-430 active crop canopy sensor. Retrieved from http://hollandscientific.com/portfolio/crop-circle-acs-430
18. Huang, Y., Thomson, S. Brand, H., & Reddy, K. (2016). Development and evaluation of low-altitude remote sensing systems for crop production management. International Journal of Agricultural and Biological Engineering, 9(4), 1. doi:10.3965/j.ijabe.20160904.2010
19. Matese, A., Toscano, P., Di Gennaro, S., Genesio, L., Vaccari, F., Primicerio, J.,⋯ Gioli, B. (2015). Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sensing, 7, 2971-2990.
20. Planet. (2017). Planet imagery product specification. Retrieved from https://www.planet.com/docs/spec-sheets/sat-imagery
21. Primicerio, J., Di Gennaro, S. F., Fiorillo, E., Genesio, L., Lugato, E., Matese, A., & Vaccari, F. P. (2012). A flexible unmanned aerial vehicle for precision agriculture. Precision Agriculture, 13, 517-523.
22. Rouse, J. W., Haas, R. H., Schell, J. A., Deering D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Proc. of the Third ERTS Symposium, NASA: Washington, DC, USA, 10-14 December 1973; NASA SP-351; pp. 309-317.
23. Sahoo, R. N., Ray, S. S., & Manjunath, K. R. (2015). Hyperspectral remote sensing of agriculture. Current Science, 108 (5) Retrieved from http://www.crcnetbase.com/isbn/9781439845387
24. Shaver, T. M., Khosla, R., & Westfall, D. G. (2010). Evaluation of two ground-based active crop canopy sensors in maize: Growth stage, row spacing, and sensor movement speed. Soil Science Society of America Journal, 74(6), 2101-2108. doi:10.2136/sssaj2009.0421
25. Shi, Y., Murray, S. C., Rooney, W. L., Valasek, J., Olsenholler, J., Pugh, N. A.,⋯ Thomasson, J. A. (2016). Corn and sorghum phenotyping using a fixed-wing UAV-based remote sensing system. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping
26. Teke, M., Deveci, H. S., Haliloglu, O., Gurbuz, S. Z., & Sakarya, U. (2013). A short survey of hyperspectral remote sensing applications in agriculture. 2013 6th International Conf. on Recent Advances in Space Technologies (RAST). Istanbul. 171-176.
27. Thomasson, J. A., Shi, Y., Olsenholler, J., Valasek, J., Murray, S. C., & Bishop, M. P. (2016). Comprehensive U AV agricultural remote-sensing research at texas a&M university Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 9866
28. Tilling, A. K., O'Leary, G. J., Ferwerda, J. G., Jones, S. D., Fitzgerald, G. J., Rodriguez, D., & Belford, R. (2007). Remote sensing of nitrogen and water stress in wheat. Field Crops Research, 104(1), 77-85. doi:10.1016/j.fcr.2007.03.023
29. Torres-Sanchez, J., Pena, J. M., de Castro, A. I., & Lopez-Granados, F. (2014) Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from U AV. Computers and Electronics in Agriculture, 103(1), 104-113.
30. USGS. (2016). Landsat 8 (L8) data users handbook. United States Geologic Survey. Retrieved from https://landsat.usgs.gov/sites/default/files/documents/Landsat8DataUsersHandbook.pdf
31. Vellidis, G., Ortiz, B., Ritchie, G., Peristeropoulos, A., Perry, C., & Rucker, K. (2009). Using GreenSeeker® to drive variable rate application of plant growth regulators and defoliants on cotton. 55-72.
32. Yang, C., Greenberg, S. M., Everitt, J. H., Sappington, T. W., & Norman, J. W. (2003). Evaluation of cotton defoliation strategies using airborne multispectral imagery. Transactions of the ASAE, 46(3), 869. doi:10.13031/2013.13582