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.
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