David Camilo Corrales, MSc

Juan Carlos Corrales-Muñoz, PhD

Apolinar Figueroa-Casas, PhD


The climate change has caused threats to agricultural production; the extremes of temperature and humidity, and other abiotic stresses are contributing factors to the etiology of disease and pest on crops. About the matter, recent research efforts have focused on predicting disease and pest crops using techniques such as supervised learning algorithms. Therefore in this paper, we present an overview of supervised learning algorithms commonly used in agriculture for the detection of pests and diseases in crops such as corn, rice, coffee, mango, peanut, and tomato, among others, with the aim of selecting the algorithms that give the best performance for the agricultural sector.



supervised learning, classifier, crop, disease, pest, agriculture

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How to Cite
Corrales, D., Corrales-Muñoz, J., & Figueroa-Casas, A. (2015). Towards detecting crop diseases and pest by supervised learning. Ingenieria Y Universidad, 19(1), 207-228. https://doi.org/10.11144/Javeriana.iyu19-1.tdcd
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