  High-throughput phenotyping (HTP) of seeds, also known as seed phenotyping,
is the comprehensive assessment of complex seed traits such as growth,
development, tolerance, resistance, ecology, yield, and the measurement of
parameters that form more complex traits. One of the key aspects of seed
phenotyping is cereal yield estimation that the seed production industry relies
upon to conduct their business. While mechanized seed kernel counters are
available in the market currently, they are often priced high and sometimes
outside the range of small scale seed production firms' affordability. The
development of object tracking neural network models such as You Only Look Once
(YOLO) enables computer scientists to design algorithms that can estimate
cereal yield inexpensively. The key bottleneck with neural network models is
that they require a plethora of labelled training data before they can be put
to task. We demonstrate that the use of synthetic imagery serves as a feasible
substitute to train neural networks for object tracking that includes the tasks
of object classification and detection. Furthermore, we propose a seed kernel
counter that uses a low-cost mechanical hopper, trained YOLOv8 neural network
model, and object tracking algorithms on StrongSORT and ByteTrack to estimate
cereal yield from videos. The experiment yields a seed kernel count with an
accuracy of 95.2\% and 93.2\% for Soy and Wheat respectively using the
StrongSORT algorithm, and an accuray of 96.8\% and 92.4\% for Soy and Wheat
respectively using the ByteTrack algorithm.
