4.2 Article

MODEL UPDATING OF HYPERSPECTRAL IMAGING DATA FOR VARIETY DISCRIMINATION OF MAIZE SEEDS HARVESTED IN DIFFERENT YEARS BY CLUSTERING ALGORITHM

期刊

Transactions of the ASABE
卷 59, 期 6, 页码 1529-1537

出版社

AMER SOC AGRICULTURAL & BIOLOGICAL ENGINEERS
DOI: 10.13031/trans.59.11697

关键词

Classification; Clustering algorithm; Cross-year prediction; Hyperspectral imaging; Maize seed; Model updating

资金

  1. National Natural Science Foundation of China [61271384, 61275155]
  2. Fundamental Research Funds for the Central Universities [JUSRP51510]
  3. 111 Project [B12018]
  4. Qing Lan Project

向作者/读者索取更多资源

Hyperspectral imaging technology is used to sort varieties of seeds. However, the overall performance of prediction models decreases when they are used to test the same variety of seeds from different years or seasons. Prediction accuracy is susceptible to the influence of time and thus depends on the training set used to build the model. In this study, a model updating procedure of hyperspectral imaging data for classification of maize seeds using a clustering algorithm was proposed to maintain the accuracy and robustness of the model. A total of 2000 seeds of four typical maize varieties grown in China in three different years were used for classification based on a least-squares support vector machine classifier. After determining and applying the model parameters, the updated model achieved an overall accuracy rate of 98.3%, which is higher than the 84.6% accuracy obtained using the non-updated model. The accuracy rate of the updated model was 94.8% when testing with the Kennard-Stone algorithm, which is commonly used for selecting datasets. The proposed model updating method can successfully update seed data for cross-year model building and thus improve the overall accuracy for predicting of maize seeds harvested in different seasons.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.2
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据