Applications of neuro-fuzzy classification, evaluation and forecasting techniques in agriculture

Aim of the present article is to show the results obtained from the application of neuro-fuzzy methodology in the solution of agriculture problems like the Bactrocera Oleae (olive fly) infestation in the Liguria region olive grows. The research is focused to create an informatic decisional instrument to support experts in the applications of Integrated Pest Management strategies against the Bactrocera Oleae infestation. The system will suggest types of treatments for each monitored farm in order to optimize the quality of the olive oil and improve the economic and environmental impact of these treatments. Statistical and forecast analyses on data sets referred to agronomic case studies, like the growth of olive fly, are actually made using standard and model approaches like analytical; these dates instead present characteristics (big variability and non-linearity) which make them complex to be treat mathematically. Agronomic research needs to introduce new analysis techniques of taken dates and information, for example neuro-fuzzy methodologies that allow a large use of infestation dates with a good flexibility degree. 1. BIOLOGICAL INTRODUCTION to the PROBLEM The definition of an agrarian product quality is linked to its geographic and chemicalphysical characteristics but for a good quality classification other variability factors like growing and transformation techniques shall be considered. The olive production and the oil quality are strongly influenced by the olive fly infestation and by the used defence techniques, therefore it is mandatory to carry out studies on the growing cycle of Bactrocera Oleae, annual behaviour of infestation, monitoring and control methodologies. When the olive has overtaked the phenological phase of hard stone, the female lays eggs and after few days the larva comes out; larva presents 3 different growing stages (L1,L2,L3) that grow up eating the olive. When the larva is mature comes out from olive, falls to the ground and becomes a pupa. The olive fly in Liguria developes 3 complete generations per year and sometimes can begin a fourth if the climatic conditions are favourable; the beginning of infestation is ESANN'2001 proceedings European Symposium on Artificial Neural Networks Bruges (Belgium), 25-27 April 2001, D-Facto public., ISBN 2-930307-01-3, pp. 403-408