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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

A Machine Learning Model for the Diagnosis of Coffee Diseases

Author 1: Fredy Martinez
Author 2: Holman Montiel
Author 3: Fernando Martinez

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.01304110

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 4, 2022.

  • Abstract and Keywords
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Abstract: The growing and marketing of coffee is an impor-tant source of economic resources for many countries, especially those with economies dependent on agricultural production, as is the case of Colombia. Although the country has done a lot of research to develop the sector, the truth is that most of its cultivation is carried out by small coffee families without a high degree of technology, and without major resources to access it. The quality of the coffee bean is highly sensitive to diverse diseases related to environmental conditions, fungi, bacteria, and insects, which directly and strongly affect the economic income of the entire production chain. In many cases the diseases are transmitted rapidly, causing great economic losses. A quick and reliable diagnosis would have an immediate effect on reducing losses. In this sense, this research advances the development of an embedded system based on machine learning capable of performing on-site diagnoses by untrained personnel but taking advantage of the know-how of expert coffee growers. Such a system seeks to instrument the visual characteristics of the most common plant diseases on low-cost, robust, and highly reliable hardware. We identified a deep network architecture with high performance in disease categorization and adjusted the hyperparameters of the model to maximize its characterization capacity without incurring overfitting problems. The prototype was evaluated in the laboratory on real plants for recognized disease cases, tests that matched the performance of the model validation dataset.

Keywords: Cercospora Coffeicola; convolutional neural network; coffee leaf miner; coffee leaf rust; deep learning; image processing; phoma leaf spot

Fredy Martinez, Holman Montiel and Fernando Martinez, “A Machine Learning Model for the Diagnosis of Coffee Diseases” International Journal of Advanced Computer Science and Applications(IJACSA), 13(4), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01304110

@article{Martinez2022,
title = {A Machine Learning Model for the Diagnosis of Coffee Diseases},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01304110},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01304110},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {4},
author = {Fredy Martinez and Holman Montiel and Fernando Martinez}
}


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