Use of Trained Convolutional Neural Networks for Analysis of Symptoms Caused by Botrytis fabae Sard

Use of Trained Convolutional Neural Networks for Analysis of Symptoms Caused by Botrytis fabae Sard

This study evaluated the use of convolutional neural networks (CNN) in agricultural disease recognition, specifically for Botrytis fabae symptoms. An experimental bean culture was used to capture images of healthy and affected leaflets, which were then used to perform binary classification and sever...

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Bibliographic Details
Main Authors: David-E Álvarez-Sánchez, Anderson Arévalo, Iván Felipe Benavides, Claudia Salazar-González, Carlos Betancourth
Format: Article
Language:English
Published: Universidad de Nariño 2023-04-01
Series:Revista de Ciencias Agrícolas
Subjects:
artificial intelligence
deep learning
botrys fabae sard
severity scale
neural networks
agricultural disease
inceptionresnet
resnet101
grad-cam
agricultura inteligente
investigación en patología vegetal
Online Access:https://doi.org/10.22267/rcia.20234001.198

Internet

https://doi.org/10.22267/rcia.20234001.198

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