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

Zhong, Tianyi; Iqbal, Muhammad Azhar; Zhang, Xu

doi:10.2478/ata-2025-0020

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

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