Version 1
: Received: 15 September 2022 / Approved: 16 September 2022 / Online: 16 September 2022 (07:40:27 CEST)
How to cite:
Fang, J.; Tan, K.; Zhang, Z.; Liu, Z.; Xu, H.; Zhao, Q. Classification And Identification of Organic Matter in Black Soil Based on Simulated Annealing Optimization of LSVM-Stacking Model. Preprints2022, 2022090239. https://doi.org/10.20944/preprints202209.0239.v1
Fang, J.; Tan, K.; Zhang, Z.; Liu, Z.; Xu, H.; Zhao, Q. Classification And Identification of Organic Matter in Black Soil Based on Simulated Annealing Optimization of LSVM-Stacking Model. Preprints 2022, 2022090239. https://doi.org/10.20944/preprints202209.0239.v1
Fang, J.; Tan, K.; Zhang, Z.; Liu, Z.; Xu, H.; Zhao, Q. Classification And Identification of Organic Matter in Black Soil Based on Simulated Annealing Optimization of LSVM-Stacking Model. Preprints2022, 2022090239. https://doi.org/10.20944/preprints202209.0239.v1
APA Style
Fang, J., Tan, K., Zhang, Z., Liu, Z., Xu, H., & Zhao, Q. (2022). Classification And Identification of Organic Matter in Black Soil Based on Simulated Annealing Optimization of LSVM-Stacking Model. Preprints. https://doi.org/10.20944/preprints202209.0239.v1
Chicago/Turabian Style
Fang, J., Hongzhao Xu and Qinghe Zhao. 2022 "Classification And Identification of Organic Matter in Black Soil Based on Simulated Annealing Optimization of LSVM-Stacking Model" Preprints. https://doi.org/10.20944/preprints202209.0239.v1
Abstract
For the soil in different regions, the nutrient fertility contained in it is different, and the detection and zoning management of soil nutrients before tillage every year can improve grain yield. In this paper, an integrated learning strategy model based on black soil hyperspectral data is designed for rapid classification of organic matter content classification of black soil. Soil hyperspectral image dataset of Xiangyang Experimental Base was collected; by changing the internal structure of the stacking model, an LSVM-stacking model with (MLP, SVC, DTree, XGBl, kNN) five classifiers as the L1 layer was built, and the simulated annealing algorithm was used for hyperparameter optimization. Compared to other stacking models, the LSVM-stacking metrics are significantly improved. The accuracy rate of hyperparameter optimization is improved by 38.6515%, the accuracy rate of the independent test data set is 0.9488, and the comparison of individual learners can improve the recognition classification accuracy of label"1" to 1.0.
Keywords
Hyperspectral Technology; Non-destructive Testing; Black Soil; Ensemble learning; Support Vector Machine
Subject
Environmental and Earth Sciences, Soil Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
