The beet cyst nematode (BCN) Heterodera schachtii is a plant pest responsible for crop loss on a global scale. Here, we introduce a high-throughput system based on computer vision that allows quantifying BCN infestation and characterizing nematode cysts through phenotyping. After recording microscopic images of soil extracts in a standardized setting, an instance segmentation algorithm serves to detect nematode cysts in these samples. Going beyond fast and precise cyst counting, the image-based approach enables quantification of cyst density and phenotyping of morphological features of cysts under different conditions, providing the basis for high-throughput applications in agriculture and plant breeding research.

翻译：甲状腺细胞线虫(BCN) heteroderera schachtiii是一种造成全球范围作物损失的植物害虫。在这里,我们引入了一个基于计算机视觉的高通量系统,该系统可以量化乙型氯化萘的侵扰,并通过胸腔描述线虫细胞特征。在将土壤提取的微粒图像记录在标准化的环境下之后,一个实例分割算法可以检测这些样品中的线虫细胞。除了快速精确的细胞计数之外,基于图像的方法可以量化不同条件下的细胞密度和细胞形态特征,为农业和植物育种研究的高通量应用提供基础。