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

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.