Leveraging model explainability and fine-grained cutmix augmentation for robust detection of apricot diseases in UAV images
Ahmad, Jamil Gueaieb, Wail Saddik, Abdulmotaleb El De Masi, Giulia Karray, Fakhri
Ahmad, Jamil
Gueaieb, Wail
Saddik, Abdulmotaleb El
De Masi, Giulia
Karray, Fakhri
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Machine Learning
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Journal article
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English
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Abstract
Apricots (Prunus Armeniaca) are valuable stone fruits cultivated worldwide in temperate regions, generating $500 million in annual exports. However, disease and pests significantly threaten apricot production, impacting quality and yield. Brown rot and shot hole are the two major diseases affecting apricot yield worldwide. Early detection and targeted management strategies are critical to prevent their spread. Unfortunately, the lack of diverse labeled datasets hinders the performance of deep learning models for in-field disease detection. In this regard, we propose an innovative approach that leverages deep convolutional generative adversarial networks (DCGAN) and model-guided Cutmix (MGC) data augmentation to synthesize images of diseased apricots. The proposed synthesis process is driven by counterexamples, which are samples that the model fails to detect correctly. The use of DCGAN for background and conditional DCGAN for foreground generation allows fine-grained control over the synthesis of new samples. Further, the MGC uses SHapley Additive exPlanations of the object detection model to analyze its weaknesses and guide on-demand sample generation by replicating the counterexample’s label distribution, aspect ratio, scale, and brightness/contrast. This expands and diversifies our custom apricot disease dataset, initially containing 1500 images. This addresses dataset imbalances and exposes the model to a dynamically augmented dataset, iteratively improving its performance. The proposed method was extensively evaluated on images of healthy and diseased apricots captured by unmanned aerial vehicles in different environmental conditions. MGC outperformed traditional augmentation methods with even smaller dataset, achieving 4 % better mean average precision (mAP) score with Apricot-3K dataset. Additionally, the proposed method achieved 1–4 % improvements in mAP with many state-of-the-art object detection models when trained using the proposed framework on the Apricot-10K augmented dataset.
Citation
J. Ahmad, W. Gueaieb, A.E. Saddik, G. De Masi, F. Karray, "Leveraging model explainability and fine-grained cutmix augmentation for robust detection of apricot diseases in UAV images," <i>Expert Systems with Applications</i>, vol. 296, pp. 128946-128946, 2025, https://doi.org/10.1016/j.eswa.2025.128946.
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Expert Systems with Applications
Conference
Keywords
Apricot disease, Deep convolutional generative adversarial, Model guided cutmix, networks, SHapley Additive exPlanations, UAV imaging
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Elsevier