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

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

This research addresses the critical need for an efficient and precise identification of <i>Capsicum</i> spp. fruit varieties within the post-harvest contexts to enhance quality control and ensure consumer satisfaction. Employing the YOLOv8m convolutional neural network, the study identi...

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Bibliographic Details
Main Authors: Madalena de Oliveira Barbosa, Fernanda Pereira Leite Aguiar, Suely dos Santos Sousa, Luana dos Santos Cordeiro, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
pepper classification
YOLOv8m
computer vision in food supply
deep learning
object detection
Online Access:https://www.mdpi.com/2076-3417/15/13/7024
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Summary:This research addresses the critical need for an efficient and precise identification of <i>Capsicum</i> 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.
ISSN:2076-3417

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