Chapter

Technologies and Innovations for Development

pp 265-277

Date:

Enhancing Decision-Making Processes of Small Farmers in Tropical Crops by Means of Machine Learning Models

  • Héctor SatizábalAffiliated withInstitute for Information and Communication Technologies (IICT), University of Applied Science Western Switzerland (HEIG-VD) Email author 
  • , Miguel Barreto-SanzAffiliated withFaculté des Hautes Etudes Commerciales (HEC), University of LausanneInstitute for Information and Communication Technologies (IICT), University of Applied Science Western Switzerland (HEIG-VD)
  • , Daniel JiménezAffiliated withDecision and Policy Analysis (DAPA), International Centre for Tropical Agriculture (CIAT)Faculty of BioScience Engineering: Agricultural Science, Ghent University
  • , Andrés Pérez-UribeAffiliated withInstitute for Information and Communication Technologies (IICT), University of Applied Science Western Switzerland (HEIG-VD)
  • , James CockAffiliated withDecision and Policy Analysis (DAPA), International Centre for Tropical Agriculture (CIAT)

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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.