AI in support of Plant Disease Management

Authors:
Anna Perini;Angelo Susi
Affiliations:
ITC-IRST, Via Sommarive 18, I-38050, Povo, Trento, Italy E-mail: {perini,susi}@itc.it;ITC-IRST, Via Sommarive 18, I-38050, Povo, Trento, Italy E-mail: {perini,susi}@itc.it
Venue:
AI Communications - Binding Environmental Sciences and AI
Year:
2005

Citing 11
Cited 0

Abductive inference models for diagnostic problem-solving

Abductive inference models for diagnostic problem-solving
Knowledge-based systems in agriculture

Knowledge-based systems in agriculture
C4.5: programs for machine learning

C4.5: programs for machine learning
Integration of AI-, database- and telecommunication-techniques for the plant protection expert system PRO PLANT

IEA/AIE '94 Proceedings of the 7th international conference on Industrial and engineering applications of artificial intelligence and expert systems
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Interactive Case-Based Planning for Forest Fire Management

Applied Intelligence
Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference

Machine Learning
CARMA: A Case-Based Range Management Advisor

Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelligence Conference
Active Sampling for Feature Selection

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Tropos: An Agent-Oriented Software Development Methodology

Autonomous Agents and Multi-Agent Systems
Knowledge Management in Environmental Decision Support Systems

AI Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe a Decision Support System for plant disease management used by technicians of the Advisory Service of Trentino region, Italy, and by the researchers in disease management techniques. We present the Artificial Intelligence (AI) methods and techniques that have been exploited during the development of the system. In particular, we discuss the role of an Agent Oriented analysis of the application domain, for requirements elicitation and system design, where AI supported the developers of the systems. In addition, Machine Learning techniques have been used to develop decision procedures to support the domain stakeholders in planning and executing actions for managing a plant disease. We illustrate results of the Machine Learning techniques application with reference to a critical apple pest. Notice that in this case AI techniques are part of the system itself. We also describe the system architecture and the main user functions.