Authors:
Luís Alves
1
;
Rodrigo Rocha Silva
2
and
Jorge Bernardino
3
Affiliations:
1
ISEC and Polytechnic of Coimbra, Portugal
;
2
São Paulo State Technological College and University of Coimbra, Brazil
;
3
ISEC, Polytechnic of Coimbra and University of Coimbra, Portugal
Keyword(s):
Classification, Data Mining, Weka, Random Forest, IBk, NaiveBayes, SMO.
Related
Ontology
Subjects/Areas/Topics:
Applications and Case-studies
;
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge Acquisition
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Currently, the advancements in computer technology allows progress of the agricultural sector. Producers and service providers are exploring the value of information and its importance in increasing the productivity and profitability of a farm. This paper intends to evaluate various classification algorithms of data mining to predict various diseases in vineyards and olive groves. We propose using machine learning to predict diseases based on symptoms and weather data. The accuracy of classification algorithms like Random Forest, IBK, Naïve Bayes and SMO have been compared using Weka Software. Using our proposal, it is expected to reduce the incidence of diseases by more than 75%.