欧亚国际

最近，来自中国的科学家利用VideometerLab 4多光谱成像系统，发表了题为Research on Classification Method of Eggplant Seeds Based on Machine Learning and Multispectral Imaging Classification Eggplant Seed的文章。

Research on Classification Method of Eggplant Seeds Based on Machine Learning and Multispectral Imaging Classification Eggplant Seeds

Abstract

In this study, eggplant seeds of fifteen different varieties were selected for discriminant analyses with a multispectral imaging technique. Seventy-eight features acquired with the multispectral images were extracted from individual eggplant seeds, which were then classified using SVM and a one-dimensional convolutional neural network (1D-CNN), and the overall accuracy was 90.12% and 94.80%, respectively. A two-dimensional convolutional neural network (2D-CNN) was also adopted for discrimination of seed varieties, and an accuracy of 90.67% was achieved. This study not only demonstrated that multispectral imaging combining machine learning techniques could be used as a high-throughput and nondestructive tool to discriminate seed varieties but also revealed that the shape of the seed shell may not be exactly the same as the female parents due to the genetic and environmental factors.

基于机器学习和多光谱成像的茄子种子分类方法研究

在这项研究中，选择了15个不同品种的茄子种子进行多光谱成像技术的判别分析。从茄子种子中提取78个多光谱图像特征，然后使用支持向量机和一维卷积神经网络（1D-CNN）对其进行分类，总准确率分别为90.12%和94.80%。采用二维卷积神经网络（2D-CNN）对种子品种进行判别，准确率达90.67%。这项研究不仅证明了结合机器学习技术的多光谱成像可以作为一种高通量和非破坏性的工具来鉴别种子品种，而且还揭示了由于遗传和环境因素，种子外壳的形状可能与母本不完全相同。