Inversion model of chlorophyll content in <i>japonica</i> rice canopy based on PSO-ELM and hyper-spectral remote sensing

YU Fenghua; FENG Shuai; ZHAO Yiran; WANG Dingkang; XING Simin; XU Tongyu

doi:10.7671/j.issn.1001-411X.202007044

Journal of South China Agricultural University > 2020 > 41(6): 59-66. > DOI: 10.7671/j.issn.1001-411X.202007044

YU Fenghua, FENG Shuai, ZHAO Yiran, et al. Inversion model of chlorophyll content in japonica rice canopy based on PSO-ELM and hyper-spectral remote sensing[J]. Journal of South China Agricultural University, 2020, 41(6): 59-66. DOI: 10.7671/j.issn.1001-411X.202007044

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Inversion model of chlorophyll content in japonica rice canopy based on PSO-ELM and hyper-spectral remote sensing

1.
College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
2.
Liaoning Agricultural Information Engineering Technology Research Center, Shenyang 110866, China

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Received Date: July 30, 2020
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
Chlorophyll content is an important indicator of the growth status of japonica rice. This study was aimed at obtaining chlorophyll content of japonica rice in a regional scale in time with UAV hyper-spectral remote sensing technology.

Method
This study was based on the UAV remote sensing test data of japonica rice in Liaozhong Experiment Station of Shenyang Agricultural University from 2016 to 2017. The successive projection algorithm (SPA) was used to extract the effective bands including 410, 481, 533, 702 and 798 nm. The extracted characteristic bands were used as the input, and the inversion models of chlorophyll contents in japonica rice canopy were established respectively using the extreme learning machine (ELM) and particle swarm optimization-extreme learning machine (PSO-ELM). In the PSO-ELM model, five parameters of PSO algorithm including proportion of population (p), inertial weight (w), learning factors (C₁, C₂), and velocity position correlation coefficient (m) were optimized.

Result
The optimal parameters were determined: p was 80, w was from 0.9 to 0.3 with a linear decline, C₁ and C₂ were 2.80 and 1.10 respectively, and m was 0.60. For the established models of chlorophyll content in japonica rice using the optimized ELM and PSO-ELM, the determination coefficients were 0.734 and 0.887 respectively, and the mean square error were 1.824 and 0.783 respectively.

Conclusion
The inversion model for chlorophyll content in japonica rice based on the optimized PSO-ELM has higher precision compared with the model based on ELM, and has better inversion ability of chlorophyll content in japonica rice. This study provides data support and application basis for the diagnosis of chlorophyll content in japonica rice by UAV hyper-spectral remote sensing technology in Northeast China.
- UAV,
- chlorophyll content,
- japonica rice,
- hyperspectral remote sensing,
- PSO-ELM

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References (32)

References

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Inversion model of chlorophyll content in japonica rice canopy based on PSO-ELM and hyper-spectral remote sensing