Application of convolutional neural networks for detection of the late blight Phytophthora infestans in potato Solanum tuberosum

Application of convolutional neural networks for detection of the late blight Phytophthora infestans in potato Solanum tuberosum

The presence of late blight in potato crops directly affects plant growth and tuber development; therefore, early detection of the disease is important. Currently, the application of convolutional neural networks is an opportunity oriented to the identification of patterns in precision agriculture, including the study of late blight in potato crops. This study describes a deep learning model capable of recognizing late blight in potato crops by means of leaf image classification. The PlantVillage augmented dataset was used in the application of this model for training. The proposed model has been evaluated from performance metrics such as precision, sensitivity, F1 score, and accuracy; to verify the effectiveness of the model in the identification and classification of late blight and compared in performance with architectures such as AlexNet, ZFNet, VGG16, and VGG19. The experimental results obtained with the selected data set showed that the proposed model achieves an accuracy of 90 % and an F1 score of 91 %. Therefore, it is concluded that the proposed model is a useful tool for farmers in the identification of late blight and scalable to mobile platforms due to the number of parameters that comprise it.

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Main Authors: Lozada-Portilla, William Alexander, Suarez-Barón, Marco Javier, Avendaño-Fernández, Eduardo
Format: Digital revista
Language:spa
Published: Universidad de Ciencias Aplicadas y Ambientales U.D.C.A 2021
Online Access:https://revistas.udca.edu.co/index.php/ruadc/article/view/1917
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record_format ojs
institution UDCA CO
collection OJS
country Colombia
countrycode CO
component Revista
access En linea
databasecode rev-ruadc-co
tag revista
region America del Sur
libraryname Biblioteca de la UDCA de Colombia
language spa
format Digital
author Lozada-Portilla, William Alexander
Suarez-Barón, Marco Javier
Avendaño-Fernández, Eduardo
spellingShingle Lozada-Portilla, William Alexander
Suarez-Barón, Marco Javier
Avendaño-Fernández, Eduardo
Application of convolutional neural networks for detection of the late blight Phytophthora infestans in potato Solanum tuberosum
author_facet Lozada-Portilla, William Alexander
Suarez-Barón, Marco Javier
Avendaño-Fernández, Eduardo
author_sort Lozada-Portilla, William Alexander
title Application of convolutional neural networks for detection of the late blight Phytophthora infestans in potato Solanum tuberosum
title_short Application of convolutional neural networks for detection of the late blight Phytophthora infestans in potato Solanum tuberosum
title_full Application of convolutional neural networks for detection of the late blight Phytophthora infestans in potato Solanum tuberosum
title_fullStr Application of convolutional neural networks for detection of the late blight Phytophthora infestans in potato Solanum tuberosum
title_full_unstemmed Application of convolutional neural networks for detection of the late blight Phytophthora infestans in potato Solanum tuberosum
title_sort application of convolutional neural networks for detection of the late blight phytophthora infestans in potato solanum tuberosum
description The presence of late blight in potato crops directly affects plant growth and tuber development; therefore, early detection of the disease is important. Currently, the application of convolutional neural networks is an opportunity oriented to the identification of patterns in precision agriculture, including the study of late blight in potato crops. This study describes a deep learning model capable of recognizing late blight in potato crops by means of leaf image classification. The PlantVillage augmented dataset was used in the application of this model for training. The proposed model has been evaluated from performance metrics such as precision, sensitivity, F1 score, and accuracy; to verify the effectiveness of the model in the identification and classification of late blight and compared in performance with architectures such as AlexNet, ZFNet, VGG16, and VGG19. The experimental results obtained with the selected data set showed that the proposed model achieves an accuracy of 90 % and an F1 score of 91 %. Therefore, it is concluded that the proposed model is a useful tool for farmers in the identification of late blight and scalable to mobile platforms due to the number of parameters that comprise it.
publisher Universidad de Ciencias Aplicadas y Ambientales U.D.C.A
publishDate 2021
url https://revistas.udca.edu.co/index.php/ruadc/article/view/1917
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spelling rev-ruadc-co-article-19172024-04-12T14:40:57Z Application of convolutional neural networks for detection of the late blight Phytophthora infestans in potato Solanum tuberosum Aplicación de redes neuronales convolucionales para la detección del tizón tardío Phytophthora infestans en papa Solanum tuberosum Lozada-Portilla, William Alexander Suarez-Barón, Marco Javier Avendaño-Fernández, Eduardo Agricultura de precisión Aprendizaje profundo Redes neuronales convolucionales Tizón tardío Convolutional neural networks Deep learning Late blight Precision agriculture The presence of late blight in potato crops directly affects plant growth and tuber development; therefore, early detection of the disease is important. Currently, the application of convolutional neural networks is an opportunity oriented to the identification of patterns in precision agriculture, including the study of late blight in potato crops. This study describes a deep learning model capable of recognizing late blight in potato crops by means of leaf image classification. The PlantVillage augmented dataset was used in the application of this model for training. The proposed model has been evaluated from performance metrics such as precision, sensitivity, F1 score, and accuracy; to verify the effectiveness of the model in the identification and classification of late blight and compared in performance with architectures such as AlexNet, ZFNet, VGG16, and VGG19. The experimental results obtained with the selected data set showed that the proposed model achieves an accuracy of 90 % and an F1 score of 91 %. Therefore, it is concluded that the proposed model is a useful tool for farmers in the identification of late blight and scalable to mobile platforms due to the number of parameters that comprise it. La presencia del tizón tardío o gota en el cultivo de papa afecta directamente el crecimiento de la planta y el desarrollo del tubérculo, por ello, es importante la detección temprana de la enfermedad. Actualmente, la aplicación de redes neuronales convolucionales es una oportunidad orientada a la identificación de patrones en la agricultura de precisión, incluyendo el estudio del tizón tardío, en el cultivo de papa. Este estudio describe un modelo de aprendizaje profundo capaz de reconocer el tizón tardío en el cultivo de papa, por medio de la clasificación de imágenes de las hojas. Se utilizó, en la aplicación de este modelo, el conjunto de datos aumentado de PlantVillage, para entrenamiento. El modelo propuesto ha sido evaluado a partir de métricas de rendimiento, como precisión, sensibilidad, puntaje F1 y exactitud. Para verificar la efectividad del modelo en la identificación y la clasificación del tizón tardío y comparado en rendimiento con arquitecturas. como AlexNet, ZFNet, VGG16 y VGG19. Los resultados experimentales obtenidos con el conjunto de datos seleccionado mostraron que el modelo propuesto alcanza una exactitud del 90 % y un puntaje F1, del 91 %. Por lo anterior, se concluye que el modelo propuesto es una herramienta útil para los agricultores en la identificación del tizón tardío y escalable a plataformas móviles, por la cantidad de parámetros que lo comprenden. Universidad de Ciencias Aplicadas y Ambientales U.D.C.A 2021-11-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion text/xml application/pdf https://revistas.udca.edu.co/index.php/ruadc/article/view/1917 10.31910/rudca.v24.n2.2021.1917 Revista U.D.C.A Actualidad & Divulgación Científica; Vol. 24 No. 2 (2021): Revista U.D.C.A Actualidad & Divulgación Científica. Julio-Diciembre Revista U.D.C.A Actualidad & Divulgación Científica; Vol. 24 Núm. 2 (2021): Revista U.D.C.A Actualidad & Divulgación Científica. Julio-Diciembre Revista U.D.C.A Actualidad & Divulgación Científica; v. 24 n. 2 (2021): Revista U.D.C.A Actualidad & Divulgación Científica. Julio-Diciembre 2619-2551 0123-4226 10.31910/rudca.v24.n2.2021 spa https://revistas.udca.edu.co/index.php/ruadc/article/view/1917/2236 https://revistas.udca.edu.co/index.php/ruadc/article/view/1917/2237 Derechos de autor 2021 William Alexander Lozada-Portilla, Marco Javier Suarez-Barón, Eduardo Avendaño-Fernández http://creativecommons.org/licenses/by-nc/4.0

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