Rice varieties classification via two-stage weighted ELM method

Haiyang Yu; Fanhua Shang; Yuxing Wang; Datao Wang

doi:10.1117/12.3048205

3 October 2024 Rice varieties classification via two-stage weighted ELM method

Haiyang Yu, Fanhua Shang, Yuxing Wang, Datao Wang

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132722W (2024) https://doi.org/10.1117/12.3048205
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

Rice production is the key factor to ensure food security. Rice varieties classification is an important means to improve rice quality. Considering the problem that traditional machine learning methods lack analytic solutions and can not adapt parameters. This paper implements the ELM method based on two-stage weights. In the initial stage, the weights of the output layer can be calculated according to the Moore-Penrose generalized inverse. Then regular function balances the expectation error and model complexity. In the model optimized stage, we calculated the model weights of the new function according to the predictive performance. These strategies help to reduce the fitting error of sequence fragments. Numerical experiment results are proved at the University of California Irvine data set, the results show that the proposed method not only has better generalization performance, but also guarantees the prediction effect of contrast method for different scenes.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haiyang Yu, Fanhua Shang, Yuxing Wang, and Datao Wang "Rice varieties classification via two-stage weighted ELM method", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132722W (3 October 2024); https://doi.org/10.1117/12.3048205

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