Differentiation of alfalfa and sweet clover seeds via multispectral imaging
Differentiation of alfalfa and sweet clover seeds via multispectral imaging
复制标题
通过多光谱成像区分苜蓿和甜三叶草种子
DOI：
10.15258/sst.2020.48.1.11
复制
发表时间：
2020-04-01
影响因子：
1.4
通讯作者：
Wang, Yanrong
中科院分区：
文献类型：
--
作者：
Hu, Xiaowen;Yang, Lingjie;Wang, Yanrong
用常规方法很难从苜蓿种子批次中去除甜三叶草种子，影响种子批次的纯度，造成苜蓿干草生产和种子产量的损失。但是，苜蓿种子群中甜三叶草种子污染的鉴别比较困难
It is hard to remove sweet clover seeds from alfalfa seed lots by conventional methods, affecting the purity of seed lots and resulting losses in for alfalfa hay production as well as seed yield. However, the discrimination of sweet clover seed contaminates in alfalfa seed lots is difficult
without special training. In this study, multispectral imaging with object-wise multivariate image analysis was evaluated for its potential to separate sweet clover and alfalfa seeds. Principal component analysis (PCA), linear discrimination analysis (LDA), partial least squares discriminant
analysis (PLSDA), AdaBoost and support vector machine (SVM) methods were applied to classify seeds of sweet clover and alfalfa according to their morphological features and spectral traits or a combination thereof. The results showed that an excellent classification could be achieved based
on a combination of morphological features and spectral data in a tested data set. Seed classification accuracy was up to 99.58% in a validation set with the LDA model, which was better than the PLSDA (68.19%), AdaBoost (96.95%) and SVM (98.47%) models. Thus, multispectral imaging together
with chemometric multivariate analysis is a promising technique to identify sweet clover seeds in alfalfa seed lots with high efficiency.