A simple method for measuring pollen germination rate using machine learning.

Abstract

The pollen germination rate decreases under various abiotic stresses, such as high-temperature stress, and it is one of the causes of inhibition of plant reproduction. Thus, measuring pollen germination rate is vital for understanding the reproductive ability of plants. However, measuring the pollen germination rate requires much labor when counting pollen. Therefore, we used the Yolov5 machine learning package in order to perform transfer learning and constructed a model that can detect germinated and non-germinated pollen separately. Pollen images of the chili pepper, Capsicum annuum, were used to create this model. Using images with a width of 640 pixels for training constructed a more accurate model than using images with a width of 320 pixels. This model could estimate the pollen germination rate of the F2 population of C. chinense previously studied with high accuracy. In addition, significantly associated gene regions previously detected in genome-wide association studies in this F2 population could again be detected using the pollen germination rate predicted by this model as a trait. Moreover, the model detected rose, tomato, radish, and strawberry pollen grains with similar accuracy to chili pepper. The pollen germination rate could be estimated even for plants other than chili pepper, probably because pollen images were similar among different plant species. We obtained a model that can identify genes related to pollen germination rate through genetic analyses in many plants.