| dc.description.abstract | This study addresses the challenge of real-time strawberry defect detection in agricultural
settings by enhancing the You Only Look Once v8 (YOLOv8) object detection algorithm.
Strawberry quality is crucial for consumer satisfaction and market value, but manual
inspection for defects is labor-intensive and prone to error. Computer vision offers a
promising solution, and YOLOv8’s speed and accuracy make it a suitable candidate for
real-time deployment.
The study proposes an improved YOLOv8 model through tuning incorporating specialized
data augmentation techniques, as well as enhancing inference speed, to address the unique
characteristics of strawberry defects. The model was trained on a diverse dataset of
strawberry images capturing various defects. The effectiveness of the proposed approach
was evaluated through rigorous testing in real-world scenarios.
Results demonstrate a significant improvement in defect detection accuracy and speed
compared to the baseline YOLOv8 model. The enhanced YOLOv8 achieved a higher mean
Average Precision (mAP) and a faster inference time, enabling real-time defect detection
on a standard computing device.
To showcase the practicality of the proposed approach, a real-time demonstration system
was developed, allowing for seamless integration into existing strawberry sorting processes.
The demonstration system successfully identified defects in real-time, proving valuable
feedback to farmers for quality control.
This study’s contribution lies in its successful adaptation of the modified YOLOv8 model
for the specific task of strawberry defect detection, its rigorous real-world evaluation, and
the development of a practical demonstration system. The findings pave the way for the
widespread adoption of computer vision in the agricultural industry, leading to improve
product quality, reduced waste, and enhanced efficiency in strawberry production | en_US |
