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：高通量表型分析（HTP）在种子领域的应用（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、生态适应性、产量等）及构成更复杂性状的参数进行综合评估。种子表型分析的关键环节之一是谷物产量估算，这是种子生产行业赖以运营的基础。尽管市面上已有机械化的种子粒计数器，但此类设备通常价格高昂，小型种子生产企业往往难以承受。以YOLO（You Only Look Once）为代表的目标追踪神经网络模型的发展，使得计算机科学家能够设计出低成本的谷物产量估计算法。然而，神经网络模型的核心瓶颈在于其需要大量标注训练数据后方能投入使用。我们证明，合成图像可作为训练神经网络执行目标追踪（包括目标分类与检测任务）的可行替代方案。此外，我们提出了一种种子粒计数器：该装置采用低成本机械料斗，结合经过训练的YOLOv8神经网络模型以及基于StrongSORT和ByteTrack的目标追踪算法，通过视频数据实现谷物产量估算。实验结果表明，使用StrongSORT算法时，对大豆和小麦的种子粒计数准确率分别达到95.2%和93.2%；采用ByteTrack算法时，对应准确率分别为96.8%和92.4%。