Confidence-Aware Multi-Model Image Classification for Early Disease Detection in Plants

Tianyi Zhong; Muhammad Azhar Iqbal; Xu Zhang

doi:10.2478/ata-2025-0020

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Confidence-Aware Multi-Model Image Classification for Early Disease Detection in Plants

Acta Technologica Agriculturae

Volume 28 (2025): Issue 3 (September 2025)

By: Tianyi Zhong, Muhammad Azhar Iqbal and Xu Zhang

Open Access

|Aug 2025

Abstract

Digital agriculture is essential for enhancing crop yields by integrating modern digital methods to prevent and manage crop diseases. To address this, a deep learning-based Confidence-Aware Multi-Model Image Classification (CAMIC) framework has been developed. CAMIC incorporates FD-Net (Foliar Disease Network) to enable early detection and identification of various plant foliar diseases. Performance testing on the public PlantVillage dataset demonstrated that CAMIC can achieve a high accuracy of up to 97.91%, outperforming existing transfer learning models like ResNet, Inception, Xception, MobileNet, and EfficientNet. This solution has also been implemented as an Android application following the client-server model paradigm.

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Articles in this issue

DOI: https://doi.org/10.2478/ata-2025-0020 | Journal eISSN: 1338-5267

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Language: English

Page range: 159 - 168

Published on: Aug 21, 2025

Published by: Slovak University of Agriculture in Nitra

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

digital plant protection,

deep learning,

foliar disease,

convolutional neural network,

transfer learning algorithms

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2025 Tianyi Zhong, Muhammad Azhar Iqbal, Xu Zhang, published by Slovak University of Agriculture in Nitra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 28 (2025): Issue 3 (September 2025)