A Lightweight Shallow CNN-Based Approach for Ring-Neck Disease Detection in Avocado Fruits

Ring-neck disease in avocado cultivation is a critical issue due to its significant impact on production, including economic losses caused by premature fruit drop, postharvest rejection, and export limitations, among other factors. To enable the automated detection of ring-neck symptoms, this study proposes a computer vision-based framework. As an initial step, a dataset of 622 Fuerte avocado images was established and annotated, consisting of 361 healthy samples and 261 samples affected by ring-neck. During the experimental stage, convolutional neural network (CNN) models with different architectures, comprising four and five convolutional layers, were developed and systematically evaluated. In addition, a region-of-interest (ROI)-based strategy was employed to focus the analysis on the fruit peduncle, thereby enhancing the detection of ring-neck symptoms. The performance of the proposed model was benchmarked against widely adopted deep learning architectures, namely VGG-16, VGG-19, ResNet-18, MobileNetV3, and GhostNet. The experimental evaluation demonstrated the superiority of the proposed approach, achieving an F1-score of 0.9610, compared with 0.5667 for VGG-16, 0.7164 for VGG-19, 0.0952 for ResNet-18, 0.6176 for MobileNetV3, and 0.7733 for GhostNet. Furthermore, statistical significance was assessed using the McNemar test, which confirmed that the observed performance improvements over the benchmark models were statistically significant, thereby supporting the robustness and reliability of the proposed approach.