Optimization of ShuffleNetV2 Using Self-Knowledge Distillation for Cocoa Fruit Disease Classification

Timely cocoa fruit disease diagnosis is critical for field management, yet manual inspection is subjective and inconsistent, while many accurate deep learning models remain too computationally demanding for practical on-device use. This study aims to optimize cocoa fruit disease classification by applying self-knowledge distillation (Self-KD) to a lightweight ShuffleNetV2 architecture without increasing inference complexity. Using a three-class dataset (healthy, pod borer, and black pod rot) with preprocessing and class balancing, ShuffleNetV2 was selected as the baseline and trained with Self-KD, improving accuracy from 96.84% to 98.34% along with consistent gains in precision, recall, and F1-score. These results indicate that Self-KD provides a learning-level optimization that enhances robustness and prediction stability in lightweight CNNs, which is especially relevant for edge AI deployment in agricultural environments. Therefore, the proposed approach supports efficient, scalable, and sustainability-oriented AI (Green/Sustainable AI) for smart farming, with potential transferability to other crops that exhibit similar visual symptom patterns.