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

Universidad de Guadalajara
Unidad de Transferencia Tecnológica Tepic
CONACYT-CICESE
Universidad Privada del Norte
Centro de Investigaciones e Innovaciones de la Agroindustria Peruana (CIIAP)

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

UN SDGs

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

SDG 2 Zero Hunger
SDG 8 Decent Work and Economic Growth
SDG 12 Responsible Consumption and Production

Keywords

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

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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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doi = "10.14569/ijacsa.2018.090873",

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volume = "9",

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issn = "2158-107X",

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number = "8",

}

TY - JOUR

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

U2 - 10.14569/ijacsa.2018.090873

DO - 10.14569/ijacsa.2018.090873

M3 - Artículo

AN - SCOPUS:85061433814

SN - 2158-107X

VL - 9

SP - 579

EP - 583

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

UN SDGs

Keywords

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