AI-Driven Trichome Quantification for Robotic Phenotyping Systems

Abstract

This paper addresses the challenge of accurately quantifying basil (Ocimum basilicum) leaf glandular trichomes in the context of rapidly expanding urban vertical farming, where optimized monitoring of plant traits is essential for improving essential-oil yield and quality. To overcome the limitations of manual counting, we developed an automated approach using deep-learning object detection. Basil plants of the “Queen of Sheba” cultivar were grown under controlled LED lighting with varying spectral compositions, and microscope leaf images were acquired, annotated, and used to train YOLO models. Automated trichome detection revealed significant differences between their quantities on adaxial and abaxial leaf surfaces and demonstrated that different lighting treatments resulted in various trichome quantities. Moreover, correlations between essential oil yield and trichome quantity can be observed. Deep learning enabled fast, objective, and reproducible quantification of basil microstructures. The developed method represents a promising tool for integrating AI-based phenotyping into city farm monitoring and robotized automatization systems, supporting the optimization of cultivation parameters and receiving of high-yielding plants.