Coffee Rust in Ceylon: Why English People Drink Tea

Hemileia coffeicola. [Descriptions of Fungi and Bacteria].

IMI Descriptions of Fungi and Bacteria ◽

10.1079/dfb/20056400002 ◽

1964 ◽

Author(s):

G. F. Laundon

Keyword(s):

Geographical Distribution ◽

Coffea Arabica ◽

Northern Nigeria ◽

Coffee Rust ◽

Sao Tome And Principe ◽

The Common

Abstract A description is provided for Hemileia coffeicola. Information is included on the disease caused by the organism, its transmission, geographical distribution, and hosts. HOSTS: On Coffea arabica; also on C. abeokutae, C. aruwimiensis, C. canephora, and C. liberica. DISEASE: Coffee rust. Producing a dusty coating of orange-coloured urediospores completely covering the undersides of the leaves, in contrast to the common coffee rust caused by H. vastatrix, which produces rounded blotches. There is at first no accompanying discoloration of the upper surface of the leaf; however, infected leaves eventually turn yellow and become desiccated. GEOGRAPHICAL DISTRIBUTION: Sao Tome and Principe Islands, Cameroon Republic, Northern Nigeria and Uganda.

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Viability of an Alarm Predictor for Coffee Rust Disease Using Interval Regression

Trends in Applied Intelligent Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-642-13025-0_36 ◽

2010 ◽

pp. 337-346 ◽

Cited By ~ 7

Author(s):

Oscar Luaces ◽

Luiz Henrique A. Rodrigues ◽

Carlos Alberto Alves Meira ◽

José R. Quevedo ◽

Antonio Bahamonde

Keyword(s):

Rust Disease ◽

Coffee Rust

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Becoming ‘Wild’ at the Intersection of Knowledges: Coffee Rust Crisis in Costa Rica

Ethnos ◽

10.1080/00141844.2019.1640761 ◽

2019 ◽

pp. 1-21

Author(s):

Isabelle Hugøy ◽

Cecilie Vindal Ødegaard

Keyword(s):

Costa Rica ◽

Coffee Rust

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

International Journal of Agricultural and Environmental Information Systems ◽

10.4018/ijaeis.2018040103 ◽

2018 ◽

Vol 9 (2) ◽

pp. 32-52

Author(s):

Jhonn Pablo Rodríguez ◽

David Camilo Corrales ◽

Juan Carlos Corrales

Keyword(s):

Machine Learning ◽

Agricultural Product ◽

American Phytopathological Society ◽

Learning Methods ◽

Coffee Rust ◽

Machine Learning Methods ◽

The World ◽

Supervised Learning Algorithms ◽

Coffee Farmers ◽

Reliable Representation

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.

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