Título
Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases
Autor
CARLOS ERIC GALVAN TEJADA
CARLOS ALBERTO OLVERA OLVERA
HUIZILOPOZTLI LUNA GARCIA
Nivel de Acceso
Acceso Abierto
Materias
Resumen o descripción
Tomato plants are highly affected by diverse diseases. A timely and accurate diagnosis
plays an important role to prevent the quality of crops. Recently, deep learning (DL), specifically
convolutional neural networks (CNNs), have achieved extraordinary results in many applications,
including the classification of plant diseases. This work focused on fine-tuning based on the
comparison of the state-of-the-art architectures: AlexNet, GoogleNet, Inception V3, Residual Network
(ResNet) 18, and ResNet 50. An evaluation of the comparison was finally performed. The dataset
used for the experiments is contained by nine different classes of tomato diseases and a healthy
class from PlantVillage. The models were evaluated through a multiclass statistical analysis based
on accuracy, precision, sensitivity, specificity, F-Score, area under the curve (AUC), and receiving
operating characteristic (ROC) curve. The results present significant values obtained by the GoogleNet
technique, with 99.72% of AUC and 99.12% of sensitivity. It is possible to conclude that this
significantly success rate makes the GoogleNet model a useful tool for farmers in helping to identify
and protect tomatoes from the diseases mentioned.
Producción Científica de la Universidad Autónoma de Zacatecas UAZ
Fecha de publicación
12 de febrero de 2020
Tipo de publicación
Artículo
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Público en general
Repositorio Orígen
Repositorio Institucional Caxcán
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