Cocoon quality assessment and silk yield estimation YOLOv8 and EfficientNet-B0

Abstract

Silk production depends heavily on accurate cocoon grading, yet manual inspection is slow, inconsistent, and varies between operators. This creates problems in quality control and affects the final yield of raw silk. To address this, we present an automated system that uses computer vision to detect, separate, and grade silk cocoons without human involvement. The system combines a you only look once version 8 (YOLOv8) model for segmenting individual cocoons from tray images and an EfficientNetB0 classifier for identifying defects across six categories, including one qualified class and five defect types. After detection and grading, the pipeline also estimates the percentage of good cocoons and predicts silk yield based on standard industry measures. The model was trained on 3,068 cocoon samples and achieved 96.1% mean average precision (mAP) for segmentation and 97% accuracy for classification. The system can count cocoons, assess quality distribution, and provide batch-level yield estimates. This automated approach improves reliability, reduces manual effort, and offers consistent grading suitable for both farm-level and industrial environments. With low operating cost and simple deployment, the system supports modern, scalable, and data-driven sericulture.