Comparative Evaluation of Fine-Tuned Transfer Learning CNN Architectures for Automated Citrus Fruit Disease Classification

Early and accurate detection of citrus diseases is essential for maintaining fruit quality, minimising crop losses, and supporting sustainable agricultural production. Automated image-based diagnostic systems offer a scalable alternative to conventional manual inspection, which is often time-intensive, subjective, and susceptible to diagnostic variability. This study presents a transfer-learning-based deep learning framework for automated classification of citrus fruit diseases using a curated image collection derived from Food and Agriculture Organisation (FAO) resources. The dataset was organised into two classification groups: lemon and orange. The lemon dataset initially comprised 208 images across four classes: Healthy, Canker, Mold, and Scab, while the orange dataset contained 2,240 images across two classes. To address data scarcity and improve model generalisation, targeted data augmentation involving zooming (0.8–1.2), rotation (35°), and horizontal flipping was employed, increasing the datasets to 1,600 and 4,000 images for lemon and orange, respectively. Five ImageNet-pretrained convolutional neural network architectures—VGG16, ResNet50, InceptionV3, DenseNet121, and EfficientNetB0—were fine-tuned and evaluated using a stratified 70:30 training–testing protocol with five-fold cross-validation. Model performance was assessed using accuracy, precision, recall, and F1-score under a standardized experimental configuration. The results demonstrate that InceptionV3 achieved the highest classification accuracy of 90.0% on the four-class lemon dataset, while DenseNet121 obtained the best accuracy of 93.0% on the binary orange dataset. These findings indicate that appropriate transfer learning and targeted augmentation can substantially improve classification performance and model generalisation, particularly in limited-data agricultural imaging scenarios. The proposed benchmarking framework enables systematic, controlled comparisons of fine-tuned CNN architectures under consistent preprocessing, augmentation, training, and evaluation conditions. The resulting models can be integrated into real-time citrus disease diagnostic systems and deployed on resource-constrained platforms, including mobile and edge-computing devices, supporting scalable precision-agriculture applications.