Locality Preserved Selective Projection Learning for Rice Variety Identification Based on Leaf Hyperspectral Characteristics

• Published in: Agronomy (Basel) 2023-09, Vol.13 (9), p.2401
• Main Authors: Long, Chen-Feng, Wen, Zhi-Dong, Deng, Yang-Jun, Hu, Tian, Liu, Jin-Ling, Zhu, Xing-Hui
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural production; Algorithms; Discriminant analysis; Feature selection; Hybridization; Identification methods; Identification systems; leaf hyperspectral characteristics; Learning; Leaves; Machine learning; Nondestructive testing; Phenotypes; Rice; rice variety identification; Seeds; selective projection learning; Support vector machines
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy13092401

Rice has an important position in China as well as in the world. With the wide application of rice hybridization technology, the problem of mixing between individual varieties has become more and more prominent, so the variety identification of rice is important for the agricultural production, the phenotype collection, and the scientific breeding. Traditional identification methods are highly subjective and time-consuming. To address this issue, we propose a novel locality preserved selective projection learning (LPSPL) method for non-destructive rice variety identification based on leaf hyperspectral characteristics. The proposed LPSPL method can select the most discriminative spectral features from the leaf hyperspectral characteristics of rice, which is helpful to distinguish different rice varieties. In the experiments, support vector machine (SVM) is adopted to conduct the rice variety identification based on the selected spectral features. The experimental results show that the proposed method here achieves higher identification rates, 96% for the early rice and 98% for the late rice, respectively, which are superior to some state-of-the-art methods.