Quick Detection of Potato Pollen Viability Based on Deep Learning

doi:10.13560/j.cnki.biotech.bull.1985.2024-0511

Abstract

Abstract:

【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.

Key words: potato, pollen viability, image processing, deep learning, SegFormer, OpenCV

XIA Shi-xuan, GENG Ze-dong, ZHU Guang-tao, ZHANG Chun-zhi, LI Da-wei. Quick Detection of Potato Pollen Viability Based on Deep Learning[J]. Biotechnology Bulletin, 2024, 40(9): 123-130.

Figures/Tables 6

References 32

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数据集 Dataset	模型 Model	交并比 IoU	精确率 Precision	召回率 Recall	F1分数 F1 score
有活力花粉	SegFormer	35.25	85.95	38.26	51.11
	U-Net	34.34	78.62	38.28	49.92
	DeepLabV3	31.34	78.19	35.04	47.35
所有花粉	SegFormer	75.65	82.67	88.64	84.78
	U-Net	58.50	74.56	72.58	73.08
	DeepLabV3	59.08	78.26	69.97	73.70

数据集 Dataset	模型 Model	交并比 IoU	精确率 Precision	召回率 Recall	F1分数 F1 score
有活力花粉	SegFormer	35.25	85.95	38.26	51.11
	U-Net	34.34	78.62	38.28	49.92
	DeepLabV3	31.34	78.19	35.04	47.35
所有花粉	SegFormer	75.65	82.67	88.64	84.78
	U-Net	58.50	74.56	72.58	73.08
	DeepLabV3	59.08	78.26	69.97	73.70