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.

翻译：种子高通量表型分析（亦称种子表型分析）是对种子复杂性状（如生长、发育、耐受性、抗性、生态学、产量等）以及构成更复杂性状的参数进行综合评估的技术。种子表型分析的关键环节之一是谷物产量估算，种子生产行业依赖此项评估开展业务。尽管目前市场上有机械式籽粒计数器，但其价格通常高昂，有时超出小规模种子生产企业的承受范围。随着YOLO（You Only Look Once）等目标跟踪神经网络模型的发展，计算机科学家得以设计出能够低成本估算谷物产量的算法。神经网络模型的关键瓶颈在于，在执行任务前需要大量带标注的训练数据。我们证明，使用合成图像可以作为训练目标跟踪神经网络（包括目标分类与检测任务）的可行替代方案。此外，我们提出一种籽粒计数器，该装置采用低成本机械给料斗、经训练的YOLOv8神经网络模型，并结合StrongSORT与ByteTrack的目标跟踪算法，通过视频分析估算谷物产量。实验表明：使用StrongSORT算法对大豆和小麦的籽粒计数准确率分别为95.2%和93.2%；使用ByteTrack算法对大豆和小麦的计数准确率分别为96.8%和92.4%。