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Related Experiment Video

Updated: May 19, 2026

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot

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Published on: January 9, 2026

Deep learning-based high-throughput phenotyping for tiller quantification in interspecific bentgrass hybrids using

Dennis W Ferm¹, Yonghyun Kim¹, Jinyoung Y Barnaby¹

¹Floral and Nursery Plants Research Unit, U.S. National Arboretum, Agricultural Research Services, United States Department of Agriculture, Beltsville, MD, United States.

Frontiers in Plant Science

|May 18, 2026

Summary

Automated tiller counting using deep learning significantly improves turfgrass phenotyping. The YOLOv8 model offers a fast and accurate solution for tiller quantification in breeding programs.

Keywords:

YOLOv8 bentgrass hybrids computer vision deep learning faster R-CNN high-throughput phenotyping tiller quantification turfgrass breeding

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Area of Science:

Plant Science
Computational Biology
Agricultural Technology

Background:

Tiller production is vital for turfgrass density and performance.
Manual tiller counting is a bottleneck for large-scale breeding programs.

Purpose of the Study:

To develop and compare automated methods for tiller quantification in turfgrass.
To address the limitations of manual tiller counting in breeding programs.

Main Methods:

Evaluated 770 plants from an interspecific bentgrass hybrid population.
Developed and compared three automated tiller quantification methods: edge-based segmentation, Faster R-CNN, and YOLOv8.
Assessed accuracy, robustness under occlusion, and computational efficiency using an annotated image dataset.

Main Results:

The YOLOv8 deep learning model achieved the highest accuracy (R² = 0.97).
YOLOv8 processed images significantly faster than Faster R-CNN.
Edge-based and Faster R-CNN methods showed reduced performance in dense canopies.

Conclusions:

One-stage deep learning detection (YOLOv8) outperforms complex two-stage models for phenotyping fine, occluded structures.
The developed workflow provides a reliable, high-throughput solution for tiller counts.
This framework is transferable for integrating image-derived phenotypes into grass breeding pipelines.