【摘要】 通过概述图像采集的2种方式，即RGB图像和光谱图像的获取，探讨图像预处理技术，包括图像去噪、增强及分割等步骤。在特征提取方面采用主成分分析(PCA)和线性判别分析(LDA)方法，能高效提取稻种的颜色、纹理和形状特征；探讨了机器学习和深度学习在稻种光谱图像及RGB图像处理中的实践应用，以及深度学习模型在稻种识别分类的性能优化和改进方法。研究发现，机器视觉技术在稻种识别领域展现出高效与准确性，未来可以期待开发出低成本的图像采集平台和更为轻量级的稻种识别软件，推动稻种数据共享，并持续探索新兴的深度学习技术，进一步优化稻种识别的效果。更多还原

【Abstract】 The article first outlines two methods of image acquisition, namely, the capture of RGB images and spectral images. Subsequently, it explores image preprocessing techniques, including steps such as image denoising, enhancement, and segmentation. In terms of feature extraction, principal component analysis(PCA)and linear discriminant analysis(LDA) methods are employed to efficiently extract the color, texture, and shape features of rice seeds. Additionally, the article discusses the practical applications of machine learning and deep learning in processing spectral and RGB images of rice seeds, as well as the performance optimization and improvement methods of deep learning models in rice seed recognition and classification. Overall, machine vision technology demonstrates its efficiency and accuracy in the field of rice seed recognition. In the future,the development of a low-cost image acquisition platform and more lightweight rice seed recognition software can be anticipated, promoting rice seed data sharing and continuously exploring emerging deep learning techniques to further optimize the effectiveness of rice seed recognition.更多还原