Disease Identification in Crop Plants based on Convolutional Neural Networks
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Fecha
2023Autor(es)
Iparraguirre-Villanueva, Orlando
Guevara-Ponce, Victor
Torres-Ceclén, Carmen
Ruiz-Alvarado, John
Castro-Leon, Gloria
Roque-Paredes, Ofelia
Zapata-Paulini, Joselyn
Cabanillas-Carbonell, Michael
Metadatos
Mostrar el registro completo del ítemResumen
“The identification, classification and treatment of
crop plant diseases are essential for agricultural production.
Some of the most common diseases include root rot, powdery
mildew, mosaic, leaf spot and fruit rot. Machine learning (ML)
technology and convolutional neural networks (CNN) have
proven to be very useful in this field. This work aims to identify
and classify diseases in crop plants, from the data set obtained
from Plant Village, with images of diseased plant leaves and their
corresponding Tags, using CNN with transfer learning. For
processing, the dataset composing of more than 87 thousand
images, divided into 38 classes and 26 disease types, was used.
Three CNN models (DenseNet-201, ResNet-50 and Inception-v3)
were used to identify and classify the images. The results showed
that the DenseNet-201 and Inception-v3 models achieved an
accuracy of 98% in plant disease identification and classification,
slightly higher than the ResNet-50 model, which achieved an
accuracy of 97%, thus demonstrating an effective and promising
approach, being able to learn relevant features from the images
and classify them accurately. Overall, ML in conjunction with
CNNs proved to be an effective tool for identifying and
classifying diseases in crop plants. The CNN models used in this
work are a very good choice for this type of tasks, since they
proved to have a very high performance in classification tasks. In
terms of accuracy, all three models are very accurate in image
classification, with an accuracy of over 96% with large data sets“
Colecciones
- Web of Science (WOS) [236]