ArticleScientific reports2025
Integrating advanced deep learning techniques for enhanced detection and classification of citrus leaf and fruit diseases.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Interpretable deep learning and radiomics for predicting the occurrence of brain metastases following prophylactic brain irradiation in small cell lung cancer.Translational lung cancer research · 2026Article
- A comparative analysis of single- and dual-backbone deep learning architectures with explainable AI for cherry leaf disease classification.Scientific reports · 2026Article
- Deep learning based instance segmentation of mandarin fruit slices for precision assessment and morphological quantification.Scientific reports · 2026Article
- A hybrid convolution and attention-based framework with visual explanation for fruit disease identification.Scientific reports · 2026Article
- LCWG-DETR: a wavelet-enhanced detection transformer for citrus disease detection improving visual perception in harvesting.Frontiers in plant science · 2026Article
- OptiNet-B3: a lightweight explainable deep learning model for multiclass classification of fruit and leaf diseases.Scientific reports · 2025Article
- DBA-ViNet: an effective deep learning framework for fruit disease detection and classification using explainable AI.BMC plant biology · 2025Article
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2 authors.
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Abstract
In this study, we evaluate the performance of four deep learning models, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, for the classification of citrus diseases from images. Extensive experiments were conducted on a dataset of 759 images distributed across 9 disease classes, including Black spot, Canker, Greening, Scab, Melanose, and healthy examples of fruits and leaves. Both InceptionV3 and DenseNet121 achieved a test accuracy of 99.12%, with a macro average F1-score of approximately 0.986 and a weighted average F1-score of 0.991, indicating exceptional performance in terms of precision and recall across the majority of the classes. ResNet50 and EfficientNetB0 attained test accuracies of 84.58% and 80.18%, respectively, reflecting moderate performance in comparison. These research results underscore the promise of modern convolutional neural networks for accurate and timely detection of citrus diseases, thereby providing effective tools for farmers and agricultural professionals to implement proactive disease management, reduce crop losses, and improve yield quality.
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