Method for the hyperspectral inversion of the phosphorus content of rice leaves in cold northern China

Fenghua Yu, Honggang Zhang, Juchi Bai, Shuang Xiang, Tongyu Xu

Abstract


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.

Keywords


rice, hyperspectral data, phosphorus content, bat algorithm, inversion model

Full Text:

PDF

References


Ramoelo A, Skidmore A K, Cho M A, Mathieu R, Heitkonig I M A, Dudeni-Tlhone N, et al. Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data. ISPRS Journal of Photogrammetry and Remote Sensing, 2013; 82: 27–40.

Siedliska A, Baranowski P, Pastuszka-Woźniak J, Zubik M, Krzyszczak J. Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance. BMC Plant Biology, 2021; 21: 28.

de Oliveira K M, Furlanetto R H, Rodrigues M, dos Santos G L A A, Reis A S, Crusiol L G T, et al. Assessing phosphorus nutritional status in maize plants using leaf-based hyperspectral measurements and multivariate analysis. International Journal of Remote Sensing, 2022; 43(7): 2560–2580.

Song X, Xu D Y, Huang C C, Zhang K K, Huang S M, Guo D D, et al. Monitoring of nitrogen accumulation in wheat plants based on hyperspectral data. Remote Sensing Applications: Society and Environment, 2021; 23: 100598.

Lu J S, Li W Y, Yu M L, Zhang X B, Ma Y, Su X, et al. Estimation of rice plant potassium accumulation based on non-negative matrix factorization using hyperspectral reflectance. Precision Agriculture, 2021; 22(1): 51–74.

Cao Y L, Jiang K L, Wu J X, Yu F H, Du W, Xu T Y. Inversion modeling of japonica rice canopy chlorophyll content with UAV hyperspectral remote sensing. PloS One, 2020; 15(9): e0238530.

Liu W, Sun C F, Zhao Y N, Xu F, Song Y L, Fan J R, et al. Monitoring of wheat powdery mildew under different nitrogen input levels using hyperspectral remote sensing. Remote Sensing, 2021; 13(18): 3753.

Li T S, Zhu Z, Cui J, Chen J H, Shi X Y, Zhao X, et al. Monitoring of leaf nitrogen content of winter wheat using multi-angle hyperspectral data. International Journal of Remote Sensing, 2021; 42(12): 4672–4692.

Tian Y C, Yao X, Yang J, Cao W X, Zhu Y. Extracting red edge position parameters from ground-and space-based hyperspectral data for estimation of canopy leaf nitrogen concentration in rice. Plant Production Science, 2011; 14(3): 270–281.

Wang L, Chen S S, Li D, Wang C Y, Jiang H, Zhang Q, et al. Estimation of paddy rice nitrogen content and accumulation both at leaf and plant levels from UAV hyperspectral Imagery. Remote Sensing, 2021; 13(15): 2956.

Tian Y C, Gu K J, Chu X, Yao X, Cao W X, Zhu Y. Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice. Plant and Soil, 2014; 376(1): 193–209.

Tan K Z, Wang S W, Song Y Z, Liu Y, Gong Z P. Estimating nitrogen status of rice canopy using hyperspectral reflectance combined with BPSO-SVR in cold region. Chemometrics and Intelligent Laboratory Systems, 2018; 172: 68–79.

Liu M L, Liu X N, Ding W C, Wu L. Monitoring stress levels on rice with heavy metal pollution from hyperspectral reflectance data using wavelet-fractal analysis. International Journal of Applied Earth Observation and Geoinformation, 2011; 13(2): 246–255.

Guo B B, Qi S L, Heng Y R, Duan J Z, Zhang H Y, Wu Y P, et al. Remotely assessing leaf N uptake in winter wheat based on canopy hyperspectral red-edge absorption. European Journal of Agronomy, 2017; 82 (Part A): 113–124.

Strachan I B, Pattey E, Boisvert J B. Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sensing of Environment, 2002; 80(2): 213–224.

Mahajan G R, Sahoo R N, Pandey R N, Gupta V K, Kumar D. Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.). Precision Agriculture, 2014; 15(5): 499–522.

Mutanga O, Kumar L. Estimating and mapping grass phosphorus concentration in an African savanna using hyperspectral image data. International Journal of Remote Sensing, 2007; 28(21): 4897–4911.

Ball K R, Liu H, Brien C, Berger B, Power S A, Pendall E. Hyperspectral imaging predicts yield and nitrogen content in grass-legume polycultures. Precision Agriculture, 2022; 23: 2270–2288.

Chen S M, Hu T T, Luo L H, He Q, Zhang S W, Lu J S. Prediction of nitrogen, phosphorus, and potassium contents in apple tree leaves based on in-situ canopy hyperspectral reflectance using stacked ensemble extreme learning machine model. Journal of Soil Science and Plant Nutrition, 2022; 22: 10–24.

Guo P T, Shi Z, Li M F, Luo W, Cha Z Z. A robust method to estimate foliar phosphorus of rubber trees with hyperspectral reflectance. Industrial Crops and Products, 2018; 126: 1–12.

Farrell M D, Mersereau R M. On the impact of PCA dimension reduction for hyperspectral detection of difficult targets. IEEE Geoscience and Remote Sensing Letters, 2005; 2(2): 192–195.

Bandos T V, Bruzzone L, Camps-Valls G. Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing, 2009; 47(3): 862–873.

Yi Q X, Huang J F, Wang F M, Wang X Z, Liu Z Y. Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network. Environmental Science & Technology, 2007; 41(19): 6770–6775.

Ye A S, Zhou X B, Miao F. Innovative hyperspectral image classification approach using optimized CNN and ELM. Electronics, 2022; 11(5): 775.

Singh M, Chauhan S. A hybrid-extreme learning machine based ensemble method for online dynamic security assessment of power systems. Electric Power Systems Research, 2023; 214: 108923.

Yang X. A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO). 2010; pp.65–74. doi: 10.1007/978-3-642-12538-6_6.

Xia C, Yang S, Huang M, Zhu Q B, Guo Y, Qin J W. Maize seed classification using hyperspectral image coupled with multi-linear discriminant analysis. Infrared Physics & Technology, 2019; 103: 103077.

Prendergast L A, Smith J A. Influence functions for linear discriminant analysis: Sensitivity analysis and efficient influence diagnostics. Journal of Multivariate Analysis, 2022; 190: 104993.

Huang G B, Zhu Q Y, Siew C-K. Extreme learning machine: A new learning scheme of feedforward neural networks. IEEE International Joint Conference on Neural Networks, Budapest, 2004; pp.985–990. doi: 10.1109/ijcnn.2004.1380068.

Singh M, Chauhan S. A hybrid-extreme learning machine based ensemble method for online dynamic security assessment of power systems. Electric Power Systems Research, 2023; 214(Part B): 108923.




Copyright (c) 2024 International Journal of Agricultural and Biological Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

2023-2026 Copyright IJABE Editing and Publishing Office