Abstract
Small farmers in developing countries face the problem of deciding where to cultivate and how to manage their crops. In under researched crops, they base many of their decisions on traditional knowledge and personal experience. We surmised that their decision making processes could be enriched by inductive or data-driven models which should provide a means to improve crop management practices. Bio-inspired machine learning techniques like artificial neural networks are promising modelling tools for accomplishing the aforementioned task due to their proven capabilities when dealing with noisy, incomplete, and heterogeneous data. Moreover, bio-inspired techniques appear to perform quite well without strong assumptions on the data. Last but not least, they provide innovative ways to process and visualize highly-dimensional information. In this chapter, we illustrate the benefits of this methodology by presenting two case studies on fruit crops in Colombia. The studies reported here are associated with two related but separate problems: First the association of crop productivity with growing conditions and management and; Secondly the identification of similar or analogue sites between which technology can readily be transferred.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Acosta, O., Perez, A., & Vaillant, F. (2009). Chemical characterization, antioxidant properties, and volatile constituents of naranjilla (Solanum quitoense Lam.) cultivated in Costa Rica. Archivos Latinoamericanos de Nutricion, 59(1), 88–94.
Alvarez, D. M., Estrada, M., & Cock, J. H. (2004). RASTA (Rapid Soil and Terrain Assessment). Palmira: Universidad Nacional De Colombia.
Barreto, M., & Pérez-Uribe, A. (2007). Improving the correlation hunting in a large quantity of SOM component planes. Artificial Neural Networks - ICANN 2007, Porto, Portugal. Lecture Notes in Computer Science, 379–388. Springer Berlin / Heidelberg.
Bell, T. L. (1987). Space-time stochastic model of rainfall for satellite remote-sensing studies. Journal of Geophysical Research-Atmospheres, 92(D8), 9631–9643.
Bioversity International. (2005a). Information sheet on Rubus glaucus in http://www.bioversityinternational.org/databases/new_world_fruits_database/search.html. Accessed August 10, 2009.
Bioversity International. (2005b). Information sheet on Solanum quitoense in http://www.bioversityinternational.org/databases/new_world_fruits_database/search.html. Accessed August 16, 2009.
Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford: Oxford University Press.
Estrada, L. E. (1992). Genetic potential of lulo (Solanum quitoense Lam.) and factors that limit its expression. Acta Horticulturae, 310, 171–182.
Farr, T. G., & Kobrick, M. (2000). Radar tropography mission produces a wealth of data. American Geophysical Union Eos, 81, 583–585.
Flórez, S. L., Lasprilla, D. M., Chaves, B., Fischer, G., & Magnitskiy, S. (2008). Growth of Lulo (Solanum quitoense Lam.) plants affected by salinity and substrate. Revista Brasileira de Fruticultura, 30, 402–408.
Franco, G., & Giraldo, M. J. (2002). El cultivo de la mora (5th ed.). Manizales: CORPOICA.
Franco, G., Bernal, J. E., Giraldo, M. J., Tamayo, J., Castaño, P., Tamayo, V., Gallego, J., Leomad, J., Botero, M. J., Rodríguez, J., Guevara, N., Morales, J., Londoño, M., Ríos, G., Rodríguez, J., Cardona, J., Zuleta, J., Castaño, J., & Ramírez, C. (2002). El cultivo del Lulo: Manual técnico Corporación Colombiana de Investigación Agropecuaria (CORPOICA). Manizales: Regional nueve. Agosto de 2002.
Fritzke, B. (1995). A growing neural gas learns topologies (Advances in neural information processing systems 7). Cambridge: MIT Press.
Fritzke, B. (1997). Unsupervised ontogenic networks. Fiesler, Beale, Editors, Handbook of neural computation. IOP Publishing Ltd. and Oxford University Press. Chapter 2.4.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965–1978.
Jiménez, D., Pérez-Uribe, A., Satizábal, H. F., Barreto, S., Miguel, A., Van Damme, P., & Tomassini, M. (2008). A survey of artificial neural network-based modeling in agroecology. In B. Prasad (Ed.), Softcomputing applications in industry (pp. 247–269). Berlin/Heidelberg: Springer.
Jiménez, D., Cock, J., Satizábal, H. F., Barreto, M., Pérez-Uribe, A., Jarvis, A., & Van Damme, P. (2009). Analysis of Andean blackberry (Rubus glaucus) production models obtained by means of artificial neural networks exploiting information collected by small-scale growers in Colombia and publicly available meteorological data. Computers and Electronics in Agriculture, 69(2), 198–208.
Jiménez, D., Cock, J., Jarvis, A., Garcia, J., Satizábal, H. F., Van Damme, P., Pérez- Uribe, A., & Barreto-Sanz, M. (2011). Interpretation of commercial production information: A case study of Lulo (Solanum quitoense), an under-researched Andean fruit. Agricultural Systems, 104(3), 258–270.
Kohonen, T. (1995). Self-organizing maps. New York: Springer.
National Research Council. (1989). Lost crops of the Incas: Little known plants of the Andes with promise for worldwide cultivation. Washington, DC: National Academy Press.
Osorio, C., Duque, C., & Batista-Viera, F. (2003). Studies on aroma generation in Lulo (Solanum quitoense): Enzymatic hydrolysis of glycosides from leaves. Food Chemistry, 81(3), 333–340.
Pulido, S., Bojacá, C. R., Salazar, M., & Chaves, B. (2008). Node appearance model for Lulo (Solanum quitoense Lam.) in the high altitude tropics. Biosystems Engineering, 101(4), 383–387.
Rauber, A., Merkl, D., & Dittenbach, M. (2002). The growing hierarchical self-organizing map: Exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks, 13(6), 1331–1341.
Satizábal, H. F., & Pérez-Uribe, A. (2007). Relevance metrics to reduce input dimensions. Artificial Neural Networks – ICANN 2007. Lecture Notes in Computer Science, vol. 4688 (pp. 39–48). doi: 10.1007/978–3–540–74690–4_5.
Satizábal, H. F., Pérez-Uribe, A., & Tomassini, M. (2008). Prototype proliferation in the growing neural gas algorithm. Artificial Neural Networks – ICANN 2008. Lecture Notes in Computer Science, vol. 5164 (pp. 793–802). doi: 10.1007/978–3–540–87559–8_82.
Schultz, A., Wieland, R., & Lutze, G. (2000). Neural networks in agroecological modelling- stylish application or helpful tool? Computers and Electronics in Agriculture, 29(1–2), 73–97.
Sora, D. S., Fischer, G., & Florez, R. (2006). Refrigerated storage of mora de castilla (Rubus glaucus) fruits in modified atmosphere packaging. Agronomia Colombiana, 24(2), 306–316.
Vesanto, J., & Ahola, J. (1999). Hunting for correlations in data using the self-organizing map. Proceedings of the International ICSC Congress on Computational Intelligence Methods and Applications – CIMA. June 22–25 (pp. 279–285). Rochester: ICSC Academic Press.
Acknowledgments
This work is part of a cooperation project between Corporación Biotec, the International Center for Tropical Agriculture (CIAT), and the Haute École d’Ingénierie et de Gestion du canton de Vaud (HEIG-VD) named “precision agriculture and the construction of field-crop models for tropical fruits.” The economical support is given by several institutions in Colombia: the Ministerio de Agricultura y Desarrollo Rural (MADR), the Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), the Agencia Presidencial para la Acción Social y la Cooperación Internacional (ACCI), and the State Secretariat for Education and Research (SER) in Switzerland.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Paris
About this chapter
Cite this chapter
Satizábal, H., Barreto-Sanz, M., Jiménez, D., Pérez-Uribe, A., Cock, J. (2012). Enhancing Decision-Making Processes of Small Farmers in Tropical Crops by Means of Machine Learning Models. In: Bolay, JC., Schmid, M., Tejada, G., Hazboun, E. (eds) Technologies and Innovations for Development. Springer, Paris. https://doi.org/10.1007/978-2-8178-0268-8_18
Download citation
DOI: https://doi.org/10.1007/978-2-8178-0268-8_18
Published:
Publisher Name: Springer, Paris
Print ISBN: 978-2-8178-0267-1
Online ISBN: 978-2-8178-0268-8
eBook Packages: Business and EconomicsEconomics and Finance (R0)