Hybrid and constructive neural networks applied to a prediction problem in agriculture

The application of artificial neural networks to the solution of problems in agriculture is rarely seen. Motivated by their great potential for dealing with nonlinear prediction tasks, two neural network architectures are independently used to implement alternative tools with the goal of predicting soya production: a hybrid architecture, based on a composition of a Kohonen self-organizing map and a multilayer perceptron, and a constructive architecture, based on projection pursuit learning. Whenever a low harvest is anticipated by the prediction tool, from a set of data extracted at the beginning of the life cycle of the plant, the ultimate purpose is to employ techniques for the correction of the soil composition, aiming at reversing the scene. The output to be predicted is a nonlinear function of a high number of input variables, which prevents the adoption of conventional prediction strategies. The two prediction tools presented here can be directly applied to all prediction problems of similar complexity in other research areas.