YOLOv8-BiFPN and adjusted germination index for real-time monitoring of ozone-treated microgreen seeds
Journal
Computers and Electronics in Agriculture
Date Issued
2025-12
Author(s)
Ping-Yi Chou
Abstract
Ozone seed cleaning is a promising non-chemical method for microgreen production, yet its effects on germination require precise, real-time evaluation. This study addresses this need by developing an innovative framework that integrates a YOLOv8-BiFPN model and an adjusted germination index (AGI). Hourly imaging of ozone-treated red cabbage and broccoli seeds over 48 h yielded a mean average precision (mAP50-95) of 0.86. Low-dose ozone (96–100 min·mg·m−3) increased broccoli germination efficiency by 29 %, whereas high-dose ozone (224–232 min·mg·m−3) suppressed it. The AGI effectively normalized seeding density variations, enhancing cross-treatment comparisons. This resource-efficient framework supports sustainable seed cleaning evaluation and advances precision agriculture in resource-constrained environments.
Subjects
Deep learning
Seed germination analysis
Microgreens
Smart agriculture
Microgreens production
Type
journal article