Abstract:

Broomrape （Orobanche spp.） is an exceedingly pernicious parasitic weed that is difficult to eradicate using conventional methods. Inducing “suicidal germination” in broomrape seeds through the application of germination stimulants is a crucial control method. However， the current method to evaluate broomrape seed germination based on human visual inspection using a microscope is time-consuming and produces inconsistent results. To address these issues， we propose a broomrape seed germination recognition algorithm based on convolutional neural networks. First， we cultivated broomrape seeds and collected images of germinated and ungerminated seeds under a microscope to construct a broomrape image library. Then， we developed a convolutional neural network， named OB-Net， to extract features from broomrape images and recognize seed germination. Through comparative analysis and optimization， we carefully selected the hyperparameters of the OB-Net model. Our experimental results demonstrated that the model achieved a recognition accuracy of 95.2%. Comparative analysis with existing mainstream network models confirmed that the proposed OB-Net model exhibited the highest accuracy and fastest detection speed in recognizing germinated broomrape seeds. The broomrape seed germination recognition method proposed in this study offers effective theoretical support for further research on other seeds and germination stimulants.

Key words: broomrape seeds, germination recognition, convolutional neural networks, feature extraction