Abstract
This paper proposes an image processing/machine learning system for estimating the amount of biomass in a field. This piece of information is precious in agriculture as it would allow a controlled adjustment of water and fertilizer. This system consists of a flying robot device which captures multiple images of the area under interest. Subsequently, this set of images is processed by means of a combined action of digital elevation models and multispectral images in order to reconstruct a three-dimensional model of the area. This model is then processed by a machine learning device, i.e. a support vector regressor with multiple kernel functions, for estimating the biomass present in the area. The training of the system has been performed by means of three modern meta-heuristics representing the state-of-the-art in computational intelligence optimization. These three algorithms are based on differential evolution, particle swarm optimization, and evolution strategy frameworks, respectively. Numerical Results derived by empirical simulations show that the proposed approach can be of a great support in precision agriculture. In addition, the most promising results have been attained by means of an algorithm based on the differential evolution framework.
This research is supported by the Academy of Finland, Akatemiatutkija 130600, Algorithmic Design Issues in Memetic Computing. A special thank to Antti-Juhani Kaijanaho for the useful discussions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V.: Support vector regression machines. In: Advances in Neural Information Processing Systems, pp. 155–161 (1997)
Epitropakis, M.G., Tasoulis, D.K., Pavlidis, N.G., Plagianakos, V.P., Vrahatis, M.N.: Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Transactions on Evolutionary Computation 15(1), 99–119 (2011)
Fay, M.P., Proschan, M.A., et al.: Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Statistics Surveys 4, 1–39 (2010)
Gimelfarb, G.L., Haralick, R.: Terrain reconstruction from multiple views. Computer Analysis of Images and Patterns (1997)
Hansen, N., Müller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Computation 11(1), 1–18 (2003)
Maclin, R., Opitz, D.: Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research 1(11), 169–198 (1999)
Montes De Oca, M., Stutzle, T., Birattari, M., Dorigo, M.: Frankenstein’s PSO: A Composite Particle Swarm Optimization Algorithm. IEEE Transactions on Evolutionary Computation 13(5), 1120–1132 (2009)
Moran, M.S., Inoue, Y., Barnes, E.M.: Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment 61(3), 319–346 (1997)
Schapire, R.E.: The strength of weak learnability. Machine Learning 5(2), 197–227 (1990)
Scharstein, D.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47(1), 131–140 (2002)
Serrano, L., Filella, I.: Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science 40(3), 723–731 (2000)
Thenkabail, P.S., Smith, R.B., De Pauw, E.: Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment 71(2), 158–182 (2000)
Tirronen, V., Weber, M.: Sparkline Histograms for Comparing Evolutionary Optimization Methods. In: Proceedings of 2nd International Joint Conference on Computational Intelligence. pp. 269–274 (2010)
Vapnik, V.N.: The Nature of Statistical Learning Theory, Statistics for Engineering and Information Science, vol. 8. Springer, Heidelberg (1995)
Zebedin, L., Klaus, A., Grubergeymayer, B., Karner, K.: Towards 3D map generation from digital aerial images. ISPRS Journal of Photogrammetry and Remote Sensing 60(6), 413–427 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salo, H., Tirronen, V., Neri, F. (2012). Evolutionary Regression Machines for Precision Agriculture. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-29178-4_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29177-7
Online ISBN: 978-3-642-29178-4
eBook Packages: Computer ScienceComputer Science (R0)