Rice seed classification by hyperspectral imaging system: A real-world dataset and a credible algorithm

• Published in: Computers and electronics in agriculture Vol. 219; p. 108776
• Main Authors: Ge, Yufei, Song, Shaozhong, Yu, Shuang, Zhang, Xiaoli, Li, Xiongfei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2024
• Subjects: Benchmark dataset; Credible KNN; hyperspectral imaging; Rice seed varieties; Credible KNN; Benchmark dataset; Rice seed varieties; hyperspectral imaging
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2024.108776

Rice is one of the three major crops in the world. The hyperspectral identification of rice seeds is of great significance to the development of agriculture and hence has made remarkable achievements. However, the lack of publicly available benchmark datasets with a large number of rice seeds hinders the generalization of models and limits the advancement of the field. To address this issue, this study proposes a publicly accessible dataset for rice seed classification by hyperspectral imaging system. The proposed dataset contains 6 categories of nearly 10,000 seeds each and is characterized by (1) a large scale on the number of seeds per category and the total seed number; (2) a greater adherence to real-world scenarios; and (3) high intra-class diversity and high inter-class similarity. Based on the proposed dataset, an Instance Difficulty-weighted K-Nearest Neighbor algorithm (IDKNN) is further proposed. It is a credible algorithm, emphasizing individual seed accuracy rather than overall performance. Additionally, we explore the effects of different Regions Of Interest (ROI). Experimental results constitute the baseline performance of the proposed dataset. Compared with 10 representative algorithms, the IDKNN algorithm achieves the best performance, especially on the ROI of the entire seed. The dataset can be downloaded from https://github.com/fliery/RSHI60T. •A benchmark dataset for rice seed hyperspectral classification is constructed.•A credible KNN provides a new perspective of applications.•A mean–variance spectral representation is introduced.