Prof. Yinli Bi Profile

Prof. Yinli Bi

at China Univ of Mining and Technology

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Proceedings Article | 20 September 2020 Presentation + Paper

Detecting leaf phosphorus content in arbuscular mycorrhizal fungi-inoculated soybean using hyperspectral remote sensing data

Weiping Kong, Wenjiang Huang, Lingling Ma, Lingli Tang, Chuanrong Li, Yinli Bi

Proceedings Volume 11528, 115280M (2020) https://doi.org/10.1117/12.2570757

KEYWORDS: Reflectivity, Phosphorus, Remote sensing, Near infrared, Data modeling, Visible radiation, Atmospheric modeling, Statistical modeling, Nondestructive evaluation

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Phosphorus (P) is an important parameter participated in the process of metabolism, photosynthesis, and energy exchange of crops. A growing number of studies have focused on effects of arbuscular mycorrhizal fungi (AMF) inoculation on crop P uptake. In this context, efficient and nondestructive monitoring of the changes of leaf P content (LPC) in inoculated crops is of vital. In this study, hyperspectral remote sensing was explored in an attempt to diagnose P deficiency in the inoculated and non-inoculated soybean plants. Greenhouse pot experiment was conducted under drought stresses, and measurements of leaf spectral reflectance and LPC were carried out at the 30th, 45th and 64th days after inoculation. We transformed the raw spectral reflectance (R) into the first derivation (FD), second derivation (SD), reciprocal (1/R), reciprocal logarithm (log(1/R)) and first derivation of log(1/R) (log’(1/R)). Results indicated that the AMF-inoculated plant had significantly higher LPC than the counterparts under different drought stresses. Analysis of the correlation between LPC and the raw and five transformed reflectance in the 350-2500 nm spectral range indicated that the green bands center around 545 nm and 567 nm, as well as NIR band center around 832 nm were the most sensitive (r>0.73). The kernel ridge regression (KRR) of LPC with the sensitive bands selected from the raw/ transformed reflectance was performed, showing that FD, 1/R and log(1/R) produced excellent results in LPC assessment, with determination of coefficient (R²) all larger than 0.70. Validation with independent samples revealed that the log(1/R)-KRR model achieved the strongest and superior prediction accuracy, with R2 of 0.93, RMSE of 0.23 g/kg and RRMSE of 7.8%, respectively. Our results indicate that the log(1/R)-KRR derived from hypersectral remote sensing data can provide the most suitable estimation model for describing the dynamic changes of LPC in the AMF-inoculated soybean.

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