Enhancing Plant Disease Detection: Incorporating Advanced CNN Architectures for Better Accuracy and Interpretability

Enhancing Plant Disease Detection: Incorporating Advanced CNN Architectures for Better Accuracy and Interpretability

Abstract Convolutional Neural Networks (CNNs) have proven effective in automated plant disease diagnosis, significantly contributing to crop health monitoring. However, their limited interpretability hinders practical deployment in real-world agricultural settings. To address this, we explore advanc...

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Bibliographic Details
Main Authors: Alfonso González-Briones, Sebastián López Florez, Pablo Chamoso, Luis F. Castillo-Ossa, Emilio S. Corchado
Format: Article
Language:English
Published: Springer 2025-05-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Convolutional neural network (CNN )
EfficientNet
ResNet-50
EXplainable artificial intelligence (XAI)
Online Access:https://doi.org/10.1007/s44196-025-00835-2
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