Authors: Fenghua燳u | Honggang燴hang | Juchi燘ai | Shuang燲iang | Tongyu燲u
Source: International journal of agricultural and biological engineering (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Method for the hyperspectral inversion of the phosphorus content of rice leaves in cold northern China Phosphorus plays a vital role in the growth and development of rice in the cold northern regions, affecting the yield and quality of rice. The phosphorus content of leaves can indicate the nutritional status of rice. Rapid and accurate acquisition of the phosphorus content in leaves is the basis for ensuring healthy rice growth and maintaining stable and high rice yield. Hyperspectral technology can reflect the shape of rice leaves and then evaluate the phosphorus content in the leaves, so hyperspectral technology has the potential to estimate the phosphorus content in plant leaves quickly and accurately. The hyperspectral data of the rice leaves were pretreated using the SG smoothing method. The spectral characteristics of pretreated spectral data were extracted using principal component analysis (PCA) and linear discriminant analysis (LDA). Extreme learning machine (ELM) and Bat algorithm optimized extreme learning machine (BA-ELM) were constructed to retrieve the phosphorus content in rice leaves. The results show that there are seven feature vectors produced by the two methods, and the feature vectors selected by the two methods are used as inputs, respectively. The verification sets R2 and RMSE of the two models constructed using the feature reflectivity chosen by the LDA algorithm as input were between 0.603 and 0.604, and 0.025 and 0.032, respectively. Under the condition of the same inversion model, the model constructed by using the reflectivity of the features selected by the PCA algorithm as input has a better prediction effect, and the verification set R2 of the two models was between 0.685-0.765, and RMSE was between 0.022-0.038. In addition, when using the features selected by these two algorithms to model, comparing the prediction results of the two models, it was found that the accuracy of the BA-ELM was higher than that of ELM. Its determination coefficient R2 and RMSE of the verification set were 0.765 and 0.022, respectively. Because of this, the ELM optimized by principal component analysis and BA has certain advantages in the hyperspectral inversion of phosphorus content in rice leaves in cold regions, and can provide some reference for rapid and accurate detection of phosphorus content in rice leaves. Keywords: rice, hyperspectral data, phosphorus content, bat algorithm, inversion model DOI: 10.25165/j.ijabe.20241706.8464 Citation: Yu F H, Zhang H G, Bai J C, Xiang S, Xu T Y. Method for the hyperspectral inversion of the phosphorus content of rice leaves in cold northern China. Int J Agric & Biol Eng, 2024; 17(6): 256–263. Hyperspectral imaging ; Inversion (geology) ; China ; Phosphorus ; Environmental science ; Agronomy ; Mathematics ; Horticulture ; Geology ; Biology ; Geography ; Chemistry ; Remote sensing ; Archaeology ; Paleontology Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Water Quality Monitoring and Analysis