Research on SPAD Estimation Model for Spring Wheat Booting Stage Based on Hyperspectral Analysis.

Abstract

With the rapid progression of agricultural informatization technology, the methodologies of crop monitoring based on spectral technology are constantly upgraded. In order to carry out the efficient, precise and nondestructive detection of relative chlorophyll (SPAD) during the booting stage, we acquired hyperspectral reflectance data about spring wheat vertical distribution and adopted the fractional-order differential to transform the raw spectral data. After that, based on correlation analysis, fractional differential spectra and fractional differential spectral indices with strong correlation with SPAD were screened and fused. Then, the least-squares support vector machine (LSSSVM) and the least-squares support vector machine (SMA-LSSSVM) optimized on the slime mold algorithm were applied to construct the estimation models of SPAD, and the model accuracy was assessed to screen the optimal estimation models. The results showed that the 0.4 order fractional-order differential spectra had the highest correlation with SPAD, which was 9.3% higher than the maximum correlation coefficient of the original spectra; the constructed two-band differential spectral indices were more sensitive to SPAD than the single differential spectra, in which the correlation reached the highest level of 0.724. The SMA-LSSSVM model constructed based on the two-band fractional-order differential spectral indices was better than the single differential spectra and the integration of both, which realized the assessment of wheat SPAD.

Plant phenotyping relevance

ハイパースペクトルデータからコムギのSPAD（相対クロロフィル）を推定するセンサー・計算手法の構築と精度評価が研究の中心であり、植物生理形質のフェノタイピング手法に該当する。

Code and data availability

The supplied blocks describe a hyperspectral SPAD estimation study for spring wheat, but contain no data availability statement, public dataset deposit, author code release, or supplement with measurements/models. No paper-specific public asset is identified.

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