Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning

Powdery mildew has a negative impact on wheat growth and restricts yield formation. Therefore, accurate monitoring of the disease is of great significance for the prevention and control of powdery mildew to protect world food security. The canopy spectral reflectance was obtained using a ground feature hyperspectrometer during the flowering and filling periods of wheat, and then the Savitzky–Golay method was used to smooth the measured spectral data, and as original reflectivity (OR). Firstly, the OR was spectrally transformed using the mean centralization (MC), multivariate scattering correction (MSC), and standard normal variate transform (SNV) methods. Secondly, the feature bands of above four transformed spectral data were extracted through a combination of the Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) algorithms. Finally, partial least square regression (PLSR), support vector regression (SVR), and random forest regression (RFR) were used to construct an optimal monitoring model for wheat powdery mildew disease index (mean disease index, mDI). The results showed that after Pearson correlation, two-band optimization combinations and machine learning method modeling comparisons, the comprehensive performance of the MC spectrum data was the best, and it was a better method for pretreating disease spectrum data. The transformed spectral data combined with the CARS–SPA algorithm was able to extract the characteristic bands more effectively. The number of bands screened was more than the number of bands extracted by the OR data, and the band positions were more evenly distributed. In comparison of different machine learning modeling methods, the RFR model performed the best (coefficient of determination, R2 = 0.741–0.852), while the SVR and PLSR models performed similarly (R2 = 0.733–0.836). Taken together, the estimation accuracy of spectral data transformation using the MC method combined with the RFR model (MC-RFR) was the highest, the model R2 was 0.849–0.852, and the root mean square error (RMSE) and the mean absolute error (MAE) ranged from 2.084 to 2.177 and 1.684 to 1.777, respectively. Compared with the OR combined with the RFR model (OR-RFR), the R2 increased by 14.39%, and the R2 of RMSE and MAE decreased by 23.9 and 27.87%. Also, the monitoring accuracy of flowering stage is better than that of grain filling stage, which is due to the relative stability of canopy structure in flowering stage. It can be seen that without changing the shape of the spectral curve, and that the use of MC to preprocess spectral data, the use of CARS and SPA algorithms to extract characteristic bands, and the use of RFR modeling methods to enhance the synergy between multiple variables, and the established model (MC-CARS-SPA-RFR) can better extract the covariant relationship between the canopy spectrum and the disease, thereby improving the monitoring accuracy of wheat powdery mildew. The research results of this study provide ideas and methods for realizing high-precision remote sensing monitoring of crop disease status..

Medienart:	E-Artikel

Erscheinungsjahr:	2022
Erschienen:	2022

Enthalten in:	Zur Gesamtaufnahme - volume:13
Enthalten in:	Frontiers in Plant Science - 13(2022)

Sprache:	Englisch

Beteiligte Personen:	Zi-Heng Feng [VerfasserIn] Zi-Heng Feng [VerfasserIn] Lu-Yuan Wang [VerfasserIn] Zhe-Qing Yang [VerfasserIn] Yan-Yan Zhang [VerfasserIn] Xiao Li [VerfasserIn] Li Song [VerfasserIn] Li He [VerfasserIn] Jian-Zhao Duan [VerfasserIn] Wei Feng [VerfasserIn]

Links:	doi.org [kostenfrei] doaj.org [kostenfrei] www.frontiersin.org [kostenfrei] Journal toc [kostenfrei]

Themen:	Feature band selection Machine learning Plant culture Remote sensing monitoring Spectral transformation Wheat powdery mildew

doi:	10.3389/fpls.2022.828454

funding:
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PPN (Katalog-ID):	DOAJ059568488