State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China.
Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China.
Sensors (Basel). 2020 Nov 18;20(22):6575. doi: 10.3390/s20226575.
Rapid and accurate discrimination of alfalfa cultivars is crucial for producers, consumers, and market regulators. However, the conventional routine of alfalfa cultivars discrimination is time-consuming and labor-intensive. In this study, the potential of a new method was evaluated that used multispectral imaging combined with object-wise multivariate image analysis to distinguish alfalfa cultivars with a single seed. Three multivariate analysis methods including principal component analysis (PCA), linear discrimination analysis (LDA), and support vector machines (SVM) were applied to distinguish seeds of 12 alfalfa cultivars based on their morphological and spectral traits. The results showed that the combination of morphological features and spectral data could provide an exceedingly concise process to classify alfalfa seeds of different cultivars with multivariate analysis, while it failed to make the classification with only seed morphological features. Seed classification accuracy of the testing sets was 91.53% for LDA, and 93.47% for SVM. Thus, multispectral imaging combined with multivariate analysis could provide a simple, robust and nondestructive method to distinguish alfalfa seed cultivars.
快速准确地鉴别紫花苜蓿品种对生产者、消费者和市场监管者都至关重要。然而，传统的紫花苜蓿品种鉴别方法既耗时又费力。本研究评估了一种新方法的潜力，该方法使用多光谱成像结合目标多维图像分析来鉴别单粒紫花苜蓿种子。基于形态和光谱特征，应用三种多元分析方法（主成分分析（PCA）、线性判别分析（LDA）和支持向量机（SVM））来区分 12 个紫花苜蓿品种的种子。结果表明，形态特征和光谱数据的结合可以为不同品种的紫花苜蓿种子提供一种非常简洁的分类过程，而仅使用种子形态特征则无法实现分类。LDA 对测试集的种子分类准确率为 91.53%，SVM 为 93.47%。因此，多光谱成像结合多元分析可以提供一种简单、稳健和无损的方法来鉴别紫花苜蓿种子品种。
