Background Plant parasitic weeds belonging to the genus Striga are a major threat for food production in Sub-Saharan Africa and Southeast Asia. The parasite's life cycle starts with the induction of seed germination by host plant-derived signals, followed by parasite attachment, infection, outgrowth, flowering, reproduction, seed set and dispersal. Given the small seed size of the parasite (< 200 μm), quantification of the impact of new control measures that interfere with seed germination relies on manual, labour-intensive counting of seed batches under the microscope. Hence, there is a need for high-throughput assays that allow for large-scale screening of compounds or microorganisms that adversely affect Striga seed germination. Results Here, we introduce DiSCount (Digital Striga Counter): a computer vision tool for automated quantification of total and germinated Striga seed numbers in standard glass fibre filter assays. We developed the software using a machine learning approach trained with a dataset of 98 manually annotated images. Then, we validated and tested the model against a total dataset of 188 manually counted images. The results showed that DiSCount has an average error of 3.38 percentage points per image compared to the manually counted dataset. Most importantly, DiSCount achieves a 100 to 3000-fold speed increase in image analysis when compared to manual analysis, with an inference time of approximately 3 s per image on a single CPU and 0.1 s on a GPU. Conclusions DiSCount is accurate and efficient in quantifying total and germinated Striga seeds in a standardized germination assay. This automated computer vision tool enables for high-throughput, large-scale screening of chemical compound libraries and biological control agents of this devastating parasitic weed. The complete software and manual are hosted at https://gitlab.com/lodewijk-track32/discount_paper and the archived version is available at Zenodo with the DOI 10.5281/zenodo.3627138. The dataset used for testing is available at Zenodo with the DOI 10.5281/zenodo.3403956.

中文翻译：

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DiSCount：用于自动量化独脚金种子萌发的计算机视觉。

背景 独脚金属植物寄生杂草是撒哈拉以南非洲和东南亚粮食生产的主要威胁。寄生虫的生命周期从宿主植物衍生信号诱导种子萌发开始，然后是寄生虫附着、感染、生长、开花、繁殖、种子固定和传播。鉴于寄生虫的种子尺寸小（< 200 μm），对干扰种子发芽的新控制措施的影响进行量化依赖于在显微镜下手动、劳动密集型的种子批次计数。因此，需要允许大规模筛选对独脚金种子萌发产生不利影响的化合物或微生物的高通量测定。结果在这里，我们介绍了DiSCount（数字独脚金计数器）：一种计算机视觉工具，用于在标准玻璃纤维过滤器测定中自动量化总和发芽独脚金种子数量。我们使用机器学习方法开发了该软件，该方法使用 98 个手动注释图像的数据集进行训练。然后，我们针对 188 个手动计数图像的总数据集验证和测试了该模型。结果表明，与手动计数的数据集相比，DiSCount 每张图像的平均误差为 3.38 个百分点。最重要的是，与手动分析相比，DiSCount 的图像分析速度提高了 100 到 3000 倍，在单个 CPU 上每张图像的推理时间约为 3 秒，在 GPU 上为 0.1 秒。结论 DiSCount 在标准化发芽试验中准确有效地定量总和发芽独脚金种子。这种自动化的计算机视觉工具能够对这种破坏性寄生杂草的化合物库和生物控制剂进行高通量、大规模筛选。完整的软件和手册位于 https://gitlab.com/lodewijk-track32/discount_paper，存档版本可在 Zenodo 获得，DOI 为 10.5281/zenodo.3627138。Zenodo 提供了用于测试的数据集，DOI 为 10.5281/zenodo.3403956。