1st International Congress on Environmental Modelling and Software - Lugano, Switzerland - June 2002
Keywords
integrated production, plant disease models, machine learning
Start Date
1-7-2002 12:00 AM
Abstract
Models which simulate the evolution of a plant disease during the season give important informationto assess the seriousness of the situation. This activity precedes the choice of an appropriate action to be implementedfor reducing the economical damage. Having effective models is a critical issue in modern agriculture,especially in Integrated Protection and Organic Farming, which rest on a set of plant disease managementpractices with low environmental impact.Considerable effort has gone in the study of models for the simulation of plant diseases evolution. Phenologymodels, population and epidemiological models have been developed for several, diffused diseases. Problemsare still open, especially when the aim is that of including these models into decision support systems at useof producers and agronomists. Phenology and population models have to be developed, choosing the mostpromising techniques. Moreover, requirements such as that of providing justification to the user of the resultscomputed by a model or making the user aware of the accuracy of the model results, become critical.In this paper we focus on models that address practical plant disease management issues and use mathematicaltechniques or Artificial Intelligence techniques (especially Machine Learning techniques). We describe relevantexamples for each approach pointing out how they deal with critical issues such as adapting a model to differentgeographical area, or validating and maintaining the model on a long period.
Plant disease models. Critical issues in development and use
Models which simulate the evolution of a plant disease during the season give important informationto assess the seriousness of the situation. This activity precedes the choice of an appropriate action to be implementedfor reducing the economical damage. Having effective models is a critical issue in modern agriculture,especially in Integrated Protection and Organic Farming, which rest on a set of plant disease managementpractices with low environmental impact.Considerable effort has gone in the study of models for the simulation of plant diseases evolution. Phenologymodels, population and epidemiological models have been developed for several, diffused diseases. Problemsare still open, especially when the aim is that of including these models into decision support systems at useof producers and agronomists. Phenology and population models have to be developed, choosing the mostpromising techniques. Moreover, requirements such as that of providing justification to the user of the resultscomputed by a model or making the user aware of the accuracy of the model results, become critical.In this paper we focus on models that address practical plant disease management issues and use mathematicaltechniques or Artificial Intelligence techniques (especially Machine Learning techniques). We describe relevantexamples for each approach pointing out how they deal with critical issues such as adapting a model to differentgeographical area, or validating and maintaining the model on a long period.
