The application of remote sensing in precision agriculture

 
PIIS250020820001977-4-1
DOI
Publication type Article
Status Published
Authors
Occupation: PhD in Biological sciences
Affiliation: Agrophysical Research Institute
Address: Russian Federation
Journal nameVestnik of The Russian agricultural science
Edition№5-2018
Pages10-16
Abstract

The paper provides an overview of foreign literature on the remote sensing applications in precision agriculture. Remote sensing applications in precision agriculture began with sensors for soil organic matter content, and have quickly advanced to include hand held sensors to tractor or aerial or satellite mounted sensors. Wavelengths of electromagnetic radiation initially focused on a few key visible or near infrared bands, and nowadays electromagnetic wavelengths in use range from the ultraviolet to microwave portions of the spectrum. Spectral bandwidth has decreased dramatically with the advent of hyperspectral remote sensing, allowing improved analysis of crop stress, crop biophysical or biochemical characteristics and specific compounds. A variety of spectral indices have been widely implemented within various precision agriculture applications, rather than a focus on only normalized difference vegetation indices. Spatial resolution and temporal frequency of remote sensing imagery has increased significantly, allowing evaluation of soil and crop properties at fine spatial resolution at the expense of increased data storage and processing requirements. At present there is considerable interest in collecting remote sensing for operational management of soil and crop yields, as well as control over the spread of pests and weeds practically in real time. 

Keywordsprecision agriculture, remote sensing, spectral ranges, spatial resolution, data volume
Received27.10.2018
Publication date29.10.2018
Cite   Download pdf To download PDF you should sign in
Размещенный ниже текст является ознакомительной версией и может не соответствовать печатной

views: 172

Readers community rating: votes 0

1. Alchanatis, V., & Cohen, Y. (2010). Spectral and spatial methods of hyperspectral image analysis for esti-mation of biophysical and biochemical properties of agricultural crops. Ch. 13. In P.S. Thenkabail, J.G.Lyon, & A. Huete (Eds.), Hyperspectral remote sensing of vegetation (pp. 705). Boca Raton, FL: CRC Press.

2. Bakhsh, A., Jaynes, D. B., Colvin, T. S., & Kanwar, R. S. (2000). Spatio-temporal analysis of yield variability for a cornsoybean field in Iowa. Transactions of the ASAE, 43(1), 31-38.

3. Bauer, M. E., & Cipra, J. E. (1973). Identification of agricultural crops by computer processing of ERTS MSS data. LARS Technical Reports. Paper 20. http://docs.lib.purdue.edu/larstech/20. W. Lafayette, IN: Purdue Univ.

4. Bausch, W. C., & Khosla, R. (2010). QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize. Precision Agriculture, 11, 274-290.

5. Ben-Dor, E. (2010). Characterization of soil properties using reflectance spectroscopy. Ch. 22. In P.S. Thenkabail, J. G. Lyon, & A. Huete (Eds.), Hyperspectral remote sensing of vegetation (pp. 705). Boca Raton, FL: CRC Press.

6. Berni, J. A. J., Zarco-Tejada, P. J., Sua?rez, 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, 722-738.

7. Christy, C. D. (2008). Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy. Computers and Electronics in Agriculture, 61, 10-19.

8. Corwin, D. L., & Lesch, S. M. (2003). Application of soil electrical conductivity to precision agriculture: theory, principles, and guidelines. Agronomy Journal, 95, 455-471.

9. Crookston, K. (2006). A top 10 list of developments and issues impacting crop management and ecology during the past 50 years. Crop Science, 46, 2253-2262.

10. Doraiswamy, P. C., Moulin, S., Cook, P. W., & Stern, A. (2003). Crop yield assessment from remote sensing. Photogrammetric Engineering and Remote Sensing, 69, 665-674.

11. Goel, P. K., Prasher, S. O., Landry, J. A., Patel, R. M., Bonnell, R. B., Viau, A. A., et al. (2003). Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Computers and Electronics in Agriculture, 38, 99-124.

12. Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90, 337-352.

13. Holland, K. H., Schepers, J. S., Shanahan, J. F., & Horst, G. L. (2004). Plant canopy sensor with modulated polychromatic light. In D.J. Mulla (Ed.), Proc. 7th intl. conf. precision agriculture. (CDROM). Minneapolis, MN: Univ. Minnesota. Jinru Xue

14. and Baofeng Su (2017) Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors, Article ID 1353691, https://doi.org/10.1155/2017/1353691

15. Li, F., Miao, Y., Hennig, S. D., Gnyp, M. L., Chen, X., Jia, L., et al. (2010). Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precision Agriculture, 11, 335-357.

16. Link, A., Panitzki, M., & Reusch, S. (2002). Hydro Nsensor: tractor-mounted remote sensing for variable nitrogen fertilization. In P. C. Robert (Ed.), Precision agriculture [CD-ROM]. Proc. 6th int. conf. on precision agric (pp. 1012-1018). Madison, WI, USA: ASA, CSSA, and SSSA.

17. Long, D. S., Engel, R. E., & Siemens, M. C. (2008). Measuring grain protein concentration with in-line near infrared reflectance spectroscopy. Agronomy Journal, 100, 247-252.

18. Mamo, M., Malzer, G. L., Mulla, D. J., Huggins, D. J., & Strock, J. (2003). Spatial and temporal variation in economically optimum N rate for corn. Agronomy Journal, 95, 958-964.

19. Miao, Y., Mulla, D. J., Randall, G. W., Vetsch, J. A., & Vintila, R. (2007). Predicting chlorophyll meter readings with aerial hyperspectral remote sensing for in-season site-specific nitrogen management of corn. In J. V. Stafford (Ed.), Precision agriculture '07 (pp. 635-641). The Netherlands: Wageningen Acad. Publ.

20. Miglani, A., Ray, S., Pandey, R., & Parihar, J. (2008). Evaluation of EO-1 Hyperion data for agricultural applications. Journal of Indian Society of Remote Sensing, 36, 255-266.

21. Mondal, P., & Basu, M. (2009). Adoption of precision agriculture technologies in India and in some developing countries: scope, present status and strategies. Progress in Natural Science, 19, 659-666.

22. Mulla D.J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114, 358Ts371.

23. O'Shaughnessy, S. A., & Evett, S. R. (2010). Developing wireless sensor networks for monitoring crop canopy temperature using a moving sprinkler system as a platform. Applied Engineering in Agriculture, 26, 331-341.

24. Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Mullen, R. W., Freeman, K. W., et al. (2002). Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agronomy Journal, 94, 815-820.

25. Samborski, S. M., Tremblay, N., & Fallon, E. (2009). Strategies to make use of plant sensors-based diagnostic information for nitrogen recommendations. Agronomy Journal, 101, 800-816.

26. Scharf, P. C., Shannon, D. K., Palm, H. L., Sudduth, K. A., Drummond, S. T., Kitchen, N. R., et al. (2011). Sensor-based nitrogen applications out-performed producer-chosen rates for corn in on-farm demonstrations. Agronomy Journal, 103, 1683-1691.

27. Shanahan, J. F., Kitchen, N. R., Raun, W. R., & Schepers, J. S. (2008). Responsive in-season nitrogen management for cereals. Computers and Electronics in Agric ture, 61, 51-62.

28. Sripada, R. P., Heiniger, R. W., White, J. G., & Weisz, R. (2006). Aerial color infrared photography for determining late-season nitrogen requirements in corn. Agronomy Journal, 97, 1443-1451.

29. Sripada, R. P., Schmidt, J. P., Dellinger, A. E., & Beegle, D. B. (2008). Evaluating multiple indices from a canopy reflectance sensor to estimate corn N requirements. Agronomy Journal, 100, 1553-1561.

30. Stafford, J. V., Ambler, B., Lark, R. M., & Catt, J. (1996). Mapping and interpreting the yield variation in cereal crops. Computers and Electronics in Agriculture, 14, 101-119.

31. Sudduth, K. A., Kitchen, N. R., Wiebold, W. J., Batchelor, W. D., Bollero, G. A., Bullock, D. G., et al. (2005). Relating apparent electrical conductivity to soil properties across the north-central USA. Computers and Electronics in Agriculture, 46, 263-283.

32. Thenkabail, P. S., Lyon, J. G., & Huete, A. (2010). Hyperspectral remote sensing of vegetation and agricultural crops: knowledge gain and knowledge gap after 40 years of research. Ch. 28. In P. S. Thenkabail, J. G. Lyon, & A. Huete (Eds.), Hyperspectral remote sensing of vegetation (pp. 705). Boca Raton, FL: CRC Press.

33. Viscarra Rossel, R. A., Walvoort, D. J. J., McBratney, A. B., Janik, L. J., & Skjemstad, J. O. (2006). Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131, 59-75.

34. Wu, C., Wang, L., Niu, Z., Gao, S., & Wu, M. (2010). Nondestructive estimation of canopy chlorophyll content using Hyperion and Landsat/TM images. International Journal of Remote Sensing, 31, 2159- 2167.

35. Yao, H. L., Tang, L., Tian, Brown, R. L., Bhatnagar, D., & Cleveland, T. E. (2010). Using hyperspectral data in precision farming applications. Ch. 25. In P. S. Thenkabail, J. G. Lyon, & A. Huete (Eds.), Hyperspectral remote sensing of vegetation (pp. 705). Boca Raton, FL: CRC Press.

36. Zarco-Tejada, P. J., Miller, J. R., Morales, A., Berjo?n, A., & Aguera, J. (2004). Hyperspectral indices and model simulation for chlorophyll estimation in opencanopy tree crops. Remote Sensing of Environment, 90, 463-476.

Система Orphus

Loading...
Up