The rice canopy density prediction model research based on SVM

Keyin Chen; Jinzhen Xie

doi:10.1117/12.2686049

28 July 2023 The rice canopy density prediction model research based on SVM

Keyin Chen, Jinzhen Xie

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127563B (2023) https://doi.org/10.1117/12.2686049
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

This paper mainly researched the basic principle of support vector machine based on Gaussian kernel function. Aiming at the defects of existing Gaussian kernel function of support vector machine, particle swarm optimization algorithm was used to optimize the penalty factor and Gaussian radial parameters to improve the generalization prediction ability of the support vector machine. Moreover, based on the support vector machine and particle swarm optimization, the rice canopy density prediction model and correlation analysis experiment were established using the rice canopy image data. The experiment shows that the absolute error and relative error of the rice canopy density prediction model based on support vector machine and particle swarm optimization can meet the requirements of real-time control of the feed amount of the combined harvest under normal and back light conditions.

Citation Download Citation

Keyin Chen and Jinzhen Xie "The rice canopy density prediction model research based on SVM", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127563B (28 July 2023); https://doi.org/10.1117/12.2686049

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