Enhancing interpretability and fidelity in convolutional neural networks through domain-informed knowledge integration

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AuteurAGBANGBA, CODJO EMILE
AuteurTOHA, RODEO OSWALD Y
AuteurBELLO, ABDOU WAHIDI
AuteurADETOLA, JAMAL
Date d'ajout2026-06-02T16:06:57Z
Date de disponibilite2026-06-02T16:06:57Z
Date de publication2024
ResumeThis study addresses the need for robust disease detection methods in vegetable crops by introducing a novel initialization method for convolutional neural networks (CNNs). Rather than creating a new CNN architecture, our approach focuses on infusing expert knowledge from phytopathology directly into the model’s foundation. This innovative initialization ensures that the CNN possesses a contextual understanding of intricate disease patterns specific to tomatoes. Additionally, our study redefines the role of heatmaps as a dynamic metric for assessing model fidelity in real-time. Unlike traditional post hoc applications, heatmaps are integrated into the model evaluation process, providing insights into decision-making processes and alignment with expert-derived expectations. This dual innovation aims to enhance transparency and fidelity in CNNs, offering a nuanced and effective solution for disease detection in agriculture. The study contributes to advancing artificial intelligence applications in agriculture by providing accurate predictions and a deeper understanding of the underlying decision mechanisms crucial for crop health management.
Autre identifiantBECDB-17759
URIhttps://dspace.uac.bj/handle/123456789/14692
Languefr
Fait partie deAdvances and Applications in Statistics
Sujetconvolutional neural networks (cnns)
Sujetphytopathology
Sujetdisease detection in tomatoes
Sujetheatmaps
Sujetmodel interpretability and fidelity
TitreEnhancing interpretability and fidelity in convolutional neural networks through domain-informed knowledge integration
TypeArticle

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