Fine hyperspectral classification of rice varieties based on self-attention mechanism

• Published in: Ecological informatics 2023-07, Vol.75, p.102035, Article 102035
• Main Authors: Meng, Ying, Yuan, Wangshu, Aktilek, Erkinbek Uulu, Zhong, Zhuozhi, Wang, Yue, Gao, Rui, Su, Zhongbin
• Format: Article
• Language: English
• Subjects: Convolutional neural networks; Hyperspectral techniques; Rice varieties; Self-attention mechanism
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2023.102035

The accurate identification of rice varieties using rapid and nondestructive hyperspectral technology is of practical significance for rice cultivation and agricultural production. This paper proposes a convolutional neural network classification model based on a self-attention mechanism (self-attention-1D-CNN) to improve accuracy in distinguishing between crop species in fields using canopy spectral information. After experimental materials were planted in the research area, portable equipment was used to collect the canopy hyperspectral data for rice during the booting stage. Five preprocessing methods and three extraction methods were used to process the data. A comparison of the classification accuracy of different classification models showed that the self-attention-1D-CNN proposed in this study achieved the best classification with an accuracy of 99.93%. The research demonstrated the feasibility of using hyperspectral technology for the fine classification of rice varieties, and the feasibility of using the CNN model as a potential classification method for near-ground crop monitoring and classification. •A Self-attention-1D-CNN model was proposed for fine classification with the accuracy of 99.93%.•A high-precision, multi-variety classification task was achieved on rice canopy scale.•Near-ground crop classification was completed using only spectral reflectance information.•The feasibility of hyperspectral technique (rapid, nondestructive) for rice variety classification was explored.