Quick Detection of Potato Pollen Viability Based on Deep Learning
[Objective]Traditional methods for detecting potato pollen viability rely on visual counting,which can be inefficient and inaccurate.In this study,a method for quickly detecting pollen viability was proposed based on PaddlePaddle deep learning framework by comparing different models.[Method]First,the pollens were stained with 2,3,5-triphenyltetrazolium chloride(TTC)and imaged using a microscope.The images were annotated by Photoshop(PS).Viable and total pollens were labeled respectively,then the label images were converted into single-channel images.Three models,SegFormer,U-Net and DeepLabV3,were used for training to distinguish viable pollens and total pollens.Finally,a Python OpenCV program was used to count the pollen number and calculate pollen viability.[Result]Compared with other models,SegFormer demonstrated the best performance in various evaluation indexes of the two datasets.Compared with manual recognition,the OpenCV program enabled fast and batch counting with less error.[Conclusion]Potato pollen viability can be detected quickly and accurately by image processing technology.This method was used to quickly identify the pollen viability of 200 F2 individuals,providing a soild foundation for the collection of pollen viability in potato.
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