Using artificial neural networks for detecting damage on tobacco leaves caused by blue mold

Himer Avila-George; Topacio Valdez-Morones; Humberto Pérez-Espinosa; Brenda Acevedo-Juárez; Wilson Castrox

doi:10.14569/ijacsa.2018.090873

Using artificial neural networks for detecting damage on tobacco leaves caused by blue mold

Himer Avila-George
, Topacio Valdez-Morones
, Humberto Pérez-Espinosa
, Brenda Acevedo-Juárez
, Wilson Castrox

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

Worldwide, the monitoring of pests and diseases plays a fundamental role in the agricultural sustainability; making necessary the development of new tools for early pest detection. In this sense, we present a software application for detecting damage in tobacco (Nicotiana tabacum L.) leaves caused by the fungus of blue mold (Peronospora tabacina Adam). This software application processes tobacco leaves images using a pattern recognition technique known as Artificial Neural Network. For the training and testing stages, a total of 40 images of tobacco leaves were used. The experimentation carried out shows that the developed model has accuracy higher than 97% and there is no significant difference with a visual analysis carried out by experts in tobacco crop.

Original language	English
Pages (from-to)	579-583
Number of pages	5
Journal	International Journal of Advanced Computer Science and Applications
Volume	9
Issue number	8
DOIs	https://doi.org/10.14569/ijacsa.2018.090873
State	Published - 2018
Externally published	Yes

Keywords

Artificial neural networks
Image processing
Nicotiana tabacum L.
Peronospora tabacina Adam

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.14569/ijacsa.2018.090873

Cite this

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title = "Using artificial neural networks for detecting damage on tobacco leaves caused by blue mold",

abstract = "Worldwide, the monitoring of pests and diseases plays a fundamental role in the agricultural sustainability; making necessary the development of new tools for early pest detection. In this sense, we present a software application for detecting damage in tobacco (Nicotiana tabacum L.) leaves caused by the fungus of blue mold (Peronospora tabacina Adam). This software application processes tobacco leaves images using a pattern recognition technique known as Artificial Neural Network. For the training and testing stages, a total of 40 images of tobacco leaves were used. The experimentation carried out shows that the developed model has accuracy higher than 97\% and there is no significant difference with a visual analysis carried out by experts in tobacco crop.",

keywords = "Artificial neural networks, Image processing, Nicotiana tabacum L., Peronospora tabacina Adam",

author = "Himer Avila-George and Topacio Valdez-Morones and Humberto P{\'e}rez-Espinosa and Brenda Acevedo-Ju{\'a}rez and Wilson Castrox",

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T1 - Using artificial neural networks for detecting damage on tobacco leaves caused by blue mold

AU - Avila-George, Himer

AU - Valdez-Morones, Topacio

AU - Pérez-Espinosa, Humberto

AU - Acevedo-Juárez, Brenda

AU - Castrox, Wilson

PY - 2018

Y1 - 2018

N2 - Worldwide, the monitoring of pests and diseases plays a fundamental role in the agricultural sustainability; making necessary the development of new tools for early pest detection. In this sense, we present a software application for detecting damage in tobacco (Nicotiana tabacum L.) leaves caused by the fungus of blue mold (Peronospora tabacina Adam). This software application processes tobacco leaves images using a pattern recognition technique known as Artificial Neural Network. For the training and testing stages, a total of 40 images of tobacco leaves were used. The experimentation carried out shows that the developed model has accuracy higher than 97% and there is no significant difference with a visual analysis carried out by experts in tobacco crop.

AB - Worldwide, the monitoring of pests and diseases plays a fundamental role in the agricultural sustainability; making necessary the development of new tools for early pest detection. In this sense, we present a software application for detecting damage in tobacco (Nicotiana tabacum L.) leaves caused by the fungus of blue mold (Peronospora tabacina Adam). This software application processes tobacco leaves images using a pattern recognition technique known as Artificial Neural Network. For the training and testing stages, a total of 40 images of tobacco leaves were used. The experimentation carried out shows that the developed model has accuracy higher than 97% and there is no significant difference with a visual analysis carried out by experts in tobacco crop.

KW - Artificial neural networks

KW - Image processing

KW - Nicotiana tabacum L.

KW - Peronospora tabacina Adam

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DO - 10.14569/ijacsa.2018.090873

M3 - Artículo

AN - SCOPUS:85061433814

SN - 2158-107X

VL - 9

SP - 579

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JO - International Journal of Advanced Computer Science and Applications

JF - International Journal of Advanced Computer Science and Applications

IS - 8

ER -

Using artificial neural networks for detecting damage on tobacco leaves caused by blue mold

Abstract

Keywords

UN SDGs

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