The ability to quantify fecundity is critically important to a wide range of experimental applications, particularly in widely-used model organisms such as Drosophila melanogaster. However, the standard method of manually counting eggs is time consuming and limits the feasibility of large-scale experiments. We develop a predictive model to automate the counting of eggs from images of eggs removed from the media surface and washed onto dark filter paper. Our method uses the simple relationship between the white area in an image and the number of eggs present to create a predictive model that performs well even at high egg densities where clumping can complicate the individual identification of eggs. A cross-validation approach demonstrates our method performs well, with a correlation between predicted and manually counted values of 0.88. We show how this method can be applied to a large data set where egg densities vary widely.

定量繁殖力的能力对于广泛的实验应用至关重要，尤其是在广泛使用的模型生物（例如果蝇）中。但是，手动计数卵的标准方法很耗时，并且限制了大规模实验的可行性。我们开发了一种预测模型，可以根据从介质表面去除并清洗到深色滤纸上的鸡蛋图像中的鸡蛋计数自动进行计数。我们的方法利用图像中白色区域与存在的卵数之间的简单关系来创建一个预测模型，即使在高卵密度下，结块也会使单个卵的识别复杂化，该模型也能很好地发挥作用。交叉验证方法证明了我们的方法效果很好，预测值与手动计数值之间的相关性为0.88。我们展示了如何将该方法应用于蛋密度变化很大的大型数据集。