SOIL ORGANIC MATTER DETERMINATION USING WIRELESS SENSOR NETWORKS

Received: 10-11-2015

Accepted: 08-03-2016

DOI:

Views

4

Downloads

0

Section:

KỸ THUẬT VÀ CÔNG NGHỆ

How to Cite:

Linh, N. (2024). SOIL ORGANIC MATTER DETERMINATION USING WIRELESS SENSOR NETWORKS. Vietnam Journal of Agricultural Sciences, 14(3), 439–450. http://testtapchi.vnua.edu.vn/index.php/vjasvn/article/view/1411

SOIL ORGANIC MATTER DETERMINATION USING WIRELESS SENSOR NETWORKS

Nguyen Van Linh (*) 1

  • 1 Faculty of Engineering, Vietnam National University of Agriculture
  • Keywords

    Gaussian process, spatial prediction, soil organic matter, wireless sensor networks

    Abstract


    The paper addresses the problem of predicting soil organic matter content in an agricultural field using information collected by a low-cost network of mobile, wireless and noisy sensors that can take discrete measurements in the environment. In this context, it is proposed that the spatial phenomenon of organic matter in soil to be monitored is modeled using Gaussian processes. The proposed model then enables the wireless sensor network to estimate the soil organic matter at all unobserved locations of interest. The estimated values at predicted locations are highly comparable to those at corresponding points on a realistic image that is aerially taken by a very expensive and complex remote sensing system.

    References

    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., and Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38: 393-422.

    Bajwa, S. G. and Tian, L. F. (2005). Soil fertility characterization in agricultural fields using hyperspectral remote sensing. Transactions of the ASAE, 48(6): 2399-2406.

    Bannari, A., Pacheco, K. S., McNairn, H., and Omari, K. (2006). Estimating and mapping crop residues cover on agricultural lands using hyperspectral and ikonos data. Remote Sensing of Environment, 104: 447-459.

    Butler, Z., Corke, P., Peterson, R., and Rus, D. (2004). Virtual fences for controlling cows. In Proc. IEEE International Conference on Robotics and Automation, New Orlean, LA, USA, pp. 4429-4436.

    Chen, F., Kissel, D. E., West, L. T., and Adkins, W. (2000). Field-scale mapping of surface soil organic carbon using remotely sensed imagery. Soil Science Society of America Journal, 64: 746-753.

    Chiles, J. P. and Delfiner, P. (1999). Geostatistics: Modelling spatial uncertainty. Wiley.

    Cressie, N. A. (1991). Statistics for spatial data. Wiley.

    Davcev, D. and Gomez, J. M. (2009). ICT Inovations. Springer.

    Diggle, P. J. and Ribeiro, P. J. (2007). Model-based geostatistics. Springer, New York, USA.

    Goodman, M. (1959). A technique for the identi_cation of farm crops on aerial photographs. Photogrammetric Engineering, 25: 131-137.

    Graham, R. and Cortes, J. (2010). Spatial statistics and distributed estimation by robotic sensor network. In Proc. IEEE American Control Conference, Baltimore, MD, USA, pp. 2422-2427.

    Harmon, T., Kvien, C., Mulla, D., Hoggenboom, G., Judy, J., and Hook, J. (2005). Precision agriculture scenario. In Proc. NSF Workshop on Sensors for Environmental Observatories, Baltimore, MD, USA.

    Hokozono, S. and Hayashi, K. (2012). Variability in environmental impacts during conversion from conventional to organic farming: a comparison among three rice production systems. Journal of Cleaner Production, 28: 101-112.

    Hummel, J. W., Sudduth, K. A., and Hollinger, S. E. (2001). Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor. Computers and Electronics in Agriculture, 32: 149-165.

    Johannsen, C. J. and Barney, T. W. (1981). Remote sensing applications for resource management. Journal of Soil and Water Conservation, 36: 128-131.

    Kim, K., Kim, J., Ban, K., Kim, E., and Jang, M. (2011). U-it based greenhouse environment monitoring system. In Proc. FTRA International Conference on Multimedia and Ubiquitous Engineering, Crete, Greece, pp. 203-206.

    Kongapai, P. (2007). Application of remote sensing and geographic information system for estimation of soil organic matter in Nakhon Pathom Province. Master Thesis, Mahidol University, Thailand.

    Kuorilehto, M., Kohvakka, M., Suhonen, J., Hamalainen, P., Hannikainen, M., and Hamalainen, T. D. (2007). Ultra-low energy wireless sensor networks in practice: Theory, realization, and deployment. John Wiley and Sons.

    Langendoen, K., Baggio, A., and Visser, O. (2006). Experiences from a pilot sensor network deployment in precision agriculture. In Proc. International Parallel and Distributed Processing Symposium, Rhodes Island, pp. 8-6

    Matheron, G. (1973). The intrinsic random functions and their application. Advances in Applied Probability, 5: 439-468.

    Matthias, A. D., Fimbres, A., Sano, E. E., Post, D. F., Accioly, L., Batchily, A. K., and Ferreira, L. G. (2000). Surface roughness effects on soil albedo. Soil Science Society of America Journal, 64: 1035-1041.

    Mulla, D., Beatty, M., and Sekely, A. (2001). Evaluation of remote sensing and targeted soil sampling for variable rate application of nitrogen. In Proc. 5th International Conference on Precision Agriculture, America.

    Nellis, M. D., Price, K. P., and Rundquist, D. (2009). Remote sensing of cropland agriculture. Papers in Natural Resources, pp. 217-245.

    Rasmussen, C. E. and Williams, C. K. I. (2006). Gaussian processes for machine learning. The MIT Press, Cambridge, Massachusetts, London, England.

    Ruiz-Garcia, L., Lunadei, L., Barreiro, P., and Robla, I. (2009). A review of wireless sensor technologies and applications in agriculture and food industry: state of the art and current trends. Sensors, 9(6): 4728-4750.

    Schepers, A. (2002). Comparison of GIS approaches that integrate soil and crop variables to delineate management zones for precision agriculture. Masters Thesis, Department of Geography, University of Nebraska-Lincoln.

    Sikka, P., Corke, P., Valencia, P., Crossman, C., Swain, D., and Bishop-Hurley, G. (2006). Wireless ad hoc sensor and actuator networks on the farm. In Proc. International Conference on Information Processing in Sensor Networks, Nashville, USA, pp. 492-499.

    Sullivan, D. G., Shaw, J. N., Rickman, D., Mask, P. L., and Luvall, J. (2005). Using remote sensing data to evaluate surface soil properties in alabamaultisols. Soil Science, 170: 954-968.

    Walker, J. P., Houser, P. R., and Willgoose, G. R. (2004). Active microwave remote sensing for soil moisture measurement: A field evaluation on using ers-2. Hydrological Process, 1811: 1975-1997.

    Williams, C. K. I. and Rasmussen, C. E. (1996). Gaussian processes for regression. Advances in Neural Information Processing Systems, 8: 514-520.

    Wu, D. D., Olson, D. L., and Birge, J. R. (2013). Risk management in cleaner production. Journal of Cleaner Production, 53: 1-6.