Self-Organizing Maps Applied to Soil Conservation in Mediterranean Olive Groves

Abstract

Soil degradation and hot climate explain the poor yield of olive groves in North Algeria. Edaphic and climatic data were collected from olive groves and analyzed by Self-Organizing Maps (SOMs). SOM is a non-supervised neural network that projects high-dimensional data onto a low-dimension topological map, while preserving the neighborhood. In this paper, we show how SOMs enable farmers to determine clusters of olive groves, to characterize them, to study their evolution and to decide what to do to improve the nutritional quality of oil. SOM can be integrated in the Intelligent Farming System to boost conservation agriculture.