# Explainability of Deep Learning-Based Plant Disease Classifiers Through Automated Concept Identification

arXiv: https://arxiv.org/abs/2412.07408 · HTML: https://mlsanity.com/paper/2412.07408/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 32 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Built on by (most cited first, 2 of 2)

- [SugarcaneShuffleNet: A Very Fast, Lightweight Convolutional Neural Network for Diagnosis of 15 Sugarcane Leaf Diseases](https://mlsanity.com/paper/2508.17107/) (2025)
- [A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models](https://mlsanity.com/paper/2604.13240/) (2026)

## Abstract (arXiv)

While deep learning has significantly advanced automatic plant disease detection through image-based classification, improving model explainability remains crucial for reliable disease detection. In this study, we apply the Automated Concept-based Explanation (ACE) method to plant disease classification using the widely adopted InceptionV3 model and the PlantVillage dataset. ACE automatically identifies the visual concepts found in the image data and provides insights about the critical features influencing the model predictions. This approach reveals both effective disease-related patterns and incidental biases, such as those from background or lighting that can compromise model robustness. Through systematic experiments, ACE helped us to identify relevant features and pinpoint areas for targeted model improvement. Our findings demonstrate the potential of ACE to improve the explainability of plant disease classification based on deep learning, which is essential for producing transparent tools for plant disease management in agriculture.