Interpretable Multi-modal Plant Disease Detection Using CNN Fusion and LLM-Based Explanations with Meteorological Insights
Document Type
Article
Source of Publication
Lecture Notes on Data Engineering and Communications Technologies
Publication Date
5-1-2026
Abstract
Early detection of crop diseases can minimize losses for farmers and enhance production. Traditional approaches are mainly based on CNN methods that learn visual symptoms from plant leaf images. However, visual symptoms lack contextualized prediction and explanation power. To address these limitations, we propose a multimodal disease classification and explanation framework. To enhance classification accuracy, we combine visual embeddings extracted from DenseNet121 with meteorological features. To ensure farmer-friendly interpretability, a multi-modal disease explanation method is designed based on decision tree to extract meteorological causal rules, grad-cam to extract visual cues and LLM to generate textual explanations. The experiments demonstrated that the multi-modal predictions outperform single-modal baselines by achieving high accuracy (i.e., 0.987) and provide, furthermore, contextualized farmer-friendly explanations.
DOI Link
ISSN
Publisher
Springer Nature Switzerland
Volume
298
First Page
292
Last Page
303
Disciplines
Computer Sciences
Keywords
classification, multi-modal fusion, Plant disease, XAI
Scopus ID
Recommended Citation
Ben Abdallah, Emna; Grati, Rima; and Boukadi, Khouloud, "Interpretable Multi-modal Plant Disease Detection Using CNN Fusion and LLM-Based Explanations with Meteorological Insights" (2026). All Works. 8031.
https://zuscholars.zu.ac.ae/works/8031
Indexed in Scopus
yes
Open Access
no