Estimation model of leaf nitrogen content based on GEP and leaf spectral reflectance. (March 2022)

• Record Type: Journal Article
• Authors: Yang, Lechan; Deng, Song; Ma, Shouming; Xiao, Fangxiong
• Is Part Of: Computers & electrical engineering. Volume 98(2022)
• Publication Date: 2022-03
• Subjects: Gene expression programming -- Leaf nitrogen content -- Regression -- Hyperspectral band; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107648
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: High-precision estimation of leaf nitrogen content is essential for monitoring and evaluating the quality of crop products. So, it is necessary to find out the bands that affect the estimation of leaf nitrogen content from the more original hyperspectral bands, which is very important to improve the accuracy of leaf nitrogen estimation. However, the calculation complexity based on multiple regression is relatively high and the multiple regression model needs to rely on prior knowledge. This paper proposes an estimation model of leaf nitrogen content based on Gene Expression Programming (GEP) and leaf spectral reflectance. The experimental results show that compared with the other regression algorithms, the first derivative local band model obtained based on the GEP has better estimation accuracy, and the selected local band is less affected by moisture. Graphical abstract: Highlights: We present a GEP-based leaf nitrogen content estimation model. We solve the first derivative for full and local bands affect leaf nitrogen content. Experimental results show that the proposed model has advantages in R 2 and RMSE.