Accurate Inference of Rice Biomass Based on Support Vector Machine

Abstract : Biomass is an important phenotypic trait in plant growth analysis. In this study, we established and compared 8 models for measuring aboveground biomass of 402 rice varieties. Partial least squares (PLS) regression and all subsets regression (ASR) were carried out to determine the effective predictors. Then, 6 models were developed based on support vector regression (SVR). The kernel function used in this study was radial basis function (RBF). Three different optimization methods, Genetic Algorithm (GA) K-fold Cross Validation (K-CV), and Particle Swarm Optimization (PSO), were applied to optimize the penalty error C and RBF $\upgamma$γ. We also compared SVR models with models based on PLS regression and ASR. The result showed the model in combination of ASR, GA optimization and SVR outperformed other models with coefficient of determination (R2) of 0.85 for the 268 varieties in the training set and 0.79 for the 134 varieties in the testing set, respectively. This paper extends the application of SVR and intelligent algorithm in measurement of cereal biomass and has the potential of promoting the accuracy of biomass measurement for different varieties.
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Communication dans un congrès
Daoliang Li; Zhenbo Li. 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2015, Beijing, China. IFIP Advances in Information and Communication Technology, AICT-478 (Part I), pp.356-365, 2016, Computer and Computing Technologies in Agriculture IX. 〈1010.1007/978-3-319-48357-3_35〉
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Lingfeng Duan, Wanneng Yang, Guoxing Chen, Lizhong Xiong, Chenglong Huang. Accurate Inference of Rice Biomass Based on Support Vector Machine. Daoliang Li; Zhenbo Li. 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2015, Beijing, China. IFIP Advances in Information and Communication Technology, AICT-478 (Part I), pp.356-365, 2016, Computer and Computing Technologies in Agriculture IX. 〈1010.1007/978-3-319-48357-3_35〉. 〈hal-01557851〉

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