Maeda Gutiérrez, Valeria; Galván Tejada, Carlos; Zanella Calzada, Laura Alejandra; Celaya Padilla, José María; Galván Tejada, Jorge I.; Gamboa Rosales, Hamurabi; Luna García, Huizilopoztli; Magallanes Quintanar, Rafael; Guerrero Méndez, Carlos; Olvera Olvera, Carlos Alberto
Resumen:
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.