A Process for Increasing the Samples of Coffee Rust Through Machine Learning Methods

View Sample PDF

Author(s): Jhonn Pablo Rodríguez (University of Cauca, Popayán, Colombia), David Camilo Corrales (Telematic Engineering Group, University of Cauca, Popayán, Colombia and Department of Computer Science and Engineering, Carlos III University of Madrid, Madrid, Spain)and Juan Carlos Corrales (Telematic Engineering Group, University of Cauca, Popayán, Colombia)
Copyright: 2022
Pages: 24
Source title: Research Anthology on Machine Learning Techniques, Methods, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-6291-1.ch023

Keywords: Artificial Intelligence / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

Purchase

View A Process for Increasing the Samples of Coffee Rust Through Machine Learning Methods on the publisher's website for pricing and purchasing information.

Abstract

This article describes how coffee rust has become a serious concern for many coffee farmers and manufacturers. The American Phytopathological Society discusses its importance saying this: “…the most economically important coffee disease in the world…” while “…in monetary value, coffee is the most important agricultural product in international trade…” The early detection has inspired researchers to apply supervised learning algorithms on predicting the disease appearance. However, the main issue of the related works is the small number of samples of the dependent variable: Incidence Percentage of Rust, since the datasets do not have a reliable representation of the disease, which will generate inaccurate predictions in the models. This article provides a process about coffee rust to select appropriate machine learning methods to increase rust samples.

The IRMA Community

Research IRM

A Process for Increasing the Samples of Coffee Rust Through Machine Learning Methods

Purchase

Abstract

Related Content

IRMA Sponsors