YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation

Abstract

This research addresses the critical need for an efficient and precise identification of Capsicum spp. fruit varieties within the post-harvest contexts to enhance quality control and ensure consumer satisfaction. Employing the YOLOv8m convolutional neural network, the study identified eight distinct pepper varieties: Pimento, Bode, Cambuci, Chilli, Fidalga, Habanero, Jalapeno, and Scotch Bonnet. A dataset comprising 1476 annotated images was utilized and significantly expanded through data augmentation techniques, including rotation, flipping, and contrast adjustments. Comparative analysis reveals that training with the augmented dataset yielded significant improvements across key performance indicators, particularly in box precision, recall, and mean average precision (mAP50 and mAP95), underscoring the effectiveness of data augmentation. These findings underscore the considerable potential of CNNs to advance the AgriFood sector through increased automation and efficiency. While acknowledging the constraints of a controlled image dataset, subsequent research should prioritize expanding the dataset and conducting real-world testing to confirm the model’s robustness across various environmental factors. This study contributes to the field by illustrating the application of deep learning methodologies to enhance agricultural productivity and inform decision-making.

Keywords

• pepper classification
• YOLOv8m
• computer vision in food supply
• deep learning
• object detection