ConvNext as a Basis for Interpretability in Coffee Leaf Rust Classification

Adrian Chavarro, Diego Renza and Ernesto Moya-Albor ()
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Adrian Chavarro: Facultad de Ingeniería, Universidad Militar Nueva Granada, Carrera 11 101-80, Bogotá 110111, Colombia
Diego Renza: Facultad de Ingeniería, Universidad Militar Nueva Granada, Carrera 11 101-80, Bogotá 110111, Colombia
Ernesto Moya-Albor: Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México 03920, Mexico

Mathematics, 2024, vol. 12, issue 17, 1-18

Abstract: The increasing complexity of deep learning models can make it difficult to interpret and fit models beyond a purely accuracy-focused evaluation. This is where interpretable and eXplainable Artificial Intelligence (XAI) come into play to facilitate an understanding of the inner workings of models. Consequently, alternatives have emerged, such as class activation mapping (CAM) techniques aimed at identifying regions of importance for an image classification model. However, the behavior of such models can be highly dependent on the type of architecture and the different variants of convolutional neural networks. Accordingly, this paper evaluates three Convolutional Neural Network (CNN) architectures (VGG16, ResNet50, ConvNext-T) against seven CAM models (GradCAM, XGradCAM, HiResCAM, LayerCAM, GradCAM++, GradCAMElementWise, and EigenCAM), indicating that the CAM maps obtained with ConvNext models show less variability among them, i.e., they are less dependent on the selected CAM approach. This study was performed on an image dataset for the classification of coffee leaf rust and evaluated using the RemOve And Debias (ROAD) metric.

Keywords: class activation mapping (CAM); deep learning; model evaluation; model interpretation; rust classification; RemOve And Debias (ROAD); eXplainable Artificial Intelligence (XAI); green; environment (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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