Abstract
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.
Highlights
Alfalfa (Medicago sativa L.), a perennial legume species, is one of the most important crops in semi-arid and arid areas due to its contribution to animal production and cultivated pastures [1].Alfalfa has enormous value as a livestock feed, but it plays an essential role in reducing soil erosion and nutrient loss, enhancing soil carbon sequestration, and increasing soil nitrogen fertility [2].alfalfa has been an important component of sustainable agricultural systems for many years [3,4].With the development of breeding techniques, many cultivars of alfalfa have been brought into the market [5]
Twelve alfalfa cultivars as Abi700, Boja, Maverick, Ranger, Sutter, uc-1465, Fado, Vernal, Zhongmu1, Zhongmu3, Dongmu1 and Zhonglan2 were provided by the Germplasm Bank of Cold
The results revealed that the linear discrimination analysis (LDA) model based on morphological features data had a classification accuracy of 43.63% and 42.22% for training and independent testing datasets, respectively (Supplementary Table S3)
Summary
Alfalfa (Medicago sativa L.), a perennial legume species, is one of the most important crops in semi-arid and arid areas due to its contribution to animal production and cultivated pastures [1]. With the development of breeding techniques, many cultivars of alfalfa have been brought into the market [5]. Different cultivars of alfalfa vary in growth performance, nutrition characteristics, and stress tolerance. Proper cultivars usually show better adaptation to local environment and growth conditions, appropriate use of certified seeds plays a vital role in quality and quantity guarantee of alfalfa production. Guaranteeing the purity of alfalfa seeds with effective cultivar discrimination and sorting is increasingly vital for the generated profit of farmers and the healthy development of the seed industry
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