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Acta Agronomica Sinica ›› 2020, Vol. 46 ›› Issue (8): 1248-1257.doi: 10.3724/SP.J.1006.2020.01004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Canopy SIF synergize with total spectral reflectance optimized by the MDBPSO algorithm to monitor wheat stripe rust

BAI Zong-Fan1,JING Xia1,*(),ZHANG Teng1,DONG Ying-Ying2   

  1. 1College of Geometrics, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi, China
    2Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100094, China
  • Received:2020-01-09 Accepted:2020-03-24 Online:2020-08-12 Published:2020-04-17
  • Contact: Xia JING E-mail:jingxiaxust@163.com
  • Supported by:
    National Natural Science Foundation of China (41601467)(41601467)

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Abstract:

In order to extract the characteristic parameters sensitive to wheat stripe rust from total spectral reflectance and improve the operation efficiency and estimation accuracy of the wheat stripe rust remote sensing detection model, this paper improved the traditional discrete binary particle swarm optimization (DBPSO) algorithm from two aspects: inertia weight and particle update method. The modified discrete binary particle swarm optimization (MDBPSO) algorithm was used to select the characteristic parameters for the severity of wheat stripe rust from the total spectral reflectance. The selected characteristic variables were combined with canopy solar-induced chlorophyll fluorescence (SIF) data as independent variables to construct wheat stripe rust estimation model. Random forest (RF) algorithm and back propagation (BP) neural network algorithm were used as model construction method to compare and analyze the accuracy of the feature parameter construction model extracted by the correlation coefficient (CC) analysis method and the DBPSO algorithm. The MDBPSO algorithm had faster convergence speed and higher optimization accuracy than the DBPSO algorithm. The number of iterations before and after the improvement was reduced from 395 to 156. The optimal fitness function (OFV) value decreased from 0.145 to 0.127. When the MDBPSO algorithm was used to select feature variables, the accuracy of the models constructed by the two methods of RF and BP neural networks was higher than that by CC analysis and DBPSO. The validation set determination coefficient (R2V) between the predicted disease index (DI) value and the measured DI value of the RF algorithm was 9% and 3% higher than that of the CC analysis method and the DBPSO algorithm, the validation set root mean square error (RMSEV) was reduced by 28% and 11%, respectively. The R2V between the predicted disease index (DI) value and the measured DI value of the BP neural network algorithm was 13% and 6% higher than that of the CC analysis method and DBPSO algorithm, respectively, and the RMSEV was reduced by 21% and 10% respectively. The MDBPSO algorithm can improve the remote sensing detection accuracy of wheat stripe rust. Among the three feature selection algorithms of MDBPSO, DBPSO, and CC analysis, the accuracy of the model constructed by the RF algorithm was higher than that by the BP neural network algorithm. The R2V between the predicted DI value and the measured DI value of the RF model was at least 7% higher than that of BP neural network algorithm, with an average increase of 9%; RMSEV had reduced by at least 15%, with an average reduction of 20%. The MDBPSO-RF model for wheat stripe rust remote sensing detection constructed by RF method using the characteristic parameters selected by the MDBPSO algorithm as independent variables is an appropriate model for wheat stripe rust remote sensing detection. The research results provide new ideas for further realizing large-area high-precision remote sensing monitoring of crop health.

Key words: total spectral reflectance, modified particle swarm optimization, solar-induced chlorophyll fluorescence, stripe rust of wheat, feature band

Fig. 1

Curve of correlation coefficient between reflectance and disease index after SNV transformation (n = 52) The dotted line in the figure indicates the significant at the 0.1% propability level, and R0.001[52] = 0.443."

Fig. 2

Convergence curves of fitness functions for DBPSO and MDBPSO algorithms"

Fig. 3

Feature bands selected by the DBPSO and MDBPSO algorithms"

Fig. 4

Distribution of measured and predicted values of wheat stripe rust disease index — 1:1 relationship line; ■ training set; △ test set."

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