Training instance segmentation neural network with synthetic datasets for crop seed phenotyping

In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. The trained model showed 96% recall and 95% average Precision against the real-world test dataset. We show that our approach is effective also for various crops including rice, lettuce, oat, and wheat. Constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs for deploying deep learning-based analysis in the agricultural domain. Toda et al. train a neural network algorithm for crop seed segmentation using synthetically generated datasets. The model achieves very high precision and is effective for a variety of seeds like barley, rice, and lettuce. Their approach will reduce human labor costs needed to prepare training datasets for similar algorithms for agricultural applications.