YOLOv8n-ESG: A Framework for Enhanced Strawberry Growth Stage Recognition

Accurate strawberry ripeness detection plays a vital role in quality assurance and market competitiveness enhancement within agricultural production. This study proposes an enhanced YOLOv8n-ESG model for efficient in-field strawberry maturity classification. The methodology categorizes strawberry growth stages into two phases (unripe vs. ripe), addressing quality deterioration risks from improper harvesting timing. Our technical improvements to the baseline YOLOv8n architecture include: 1) backbone network convolution layer optimization, 2) C2f module refinement, 3) attention mechanism integration, 4) loss function modification, and 5) data augmentation implementation. Experimental results demonstrate the model achieves 89.8% precision, 90.5% recall, and 94.8% mAP50 in complex scenarios, effectively mitigating misdiagnosis and missed detection issues. The proposed system enables growers to optimize harvesting schedules through precise ripeness identification, contributing to intelligent agricultural technology development. These advancements in visual recognition systems offer practical solutions for improving postharvest quality control and strengthening market position in perishable fruit supply chains.