Prediction of SPAD in rice leaf based on RGB and HSI color space

doi:10.3969/j.issn.1004-1524.2018.10.23

›› 2018, Vol. 30 ›› Issue (10): 1782-1788.DOI: 10.3969/j.issn.1004-1524.2018.10.23

• Biosystems Engineering • Previous Articles Next Articles

Prediction of SPAD in rice leaf based on RGB and HSI color space

SUN Yuting¹, WANG Yinglong^{1, 2}, YANG Hongyun^{2, 3, *}, ZHOU Qiong¹, SUN Aizhen², YANG Wenji^{2, 3}

1.School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, China;
2.Key Laboratory of Agricultural Information Technology of Colleges and Universities in Jiangxi Province, Nanchang 330045,China;
3.School of Software, Jiangxi Agricultural University, Nanchang 330045, China

Received:2018-01-23 Online:2018-10-25 Published:2018-11-02

Abstract

Abstract: The relationship between the leaf image parameters and the SPAD values of rice leaf under the RGB and HSI color spaces was studied. The method of support vector machine (SVM) was used to predict the SPAD value of rice leaf, which provided a theoretical basis for rapid and accurate acquisition of plant SPAD value by using machine vision technology, and provided theoretical guidance for scientific fertilization. The experiment was conducted at Agricultural Experiment Station of Jiangxi Agricultural University and Chengxin farm in Jiangxi Province during 2015 to 2017. And the tested rice varieties were JY458, ZZ35 and LYP9. Four different nitrogen levels were designed for each rice variety. The relationship between the color parameters of rice leaf image and the SPAD value was analyzed by extracting the leaf color parameters and measuring the SPAD value. The model to predict the SPAD value was established by using SVM. The results showed that compared with RGB, the root mean square error of the predicted values of the three rice varieties based on the HSI color space was reduced by 0.067 5 (JY458), 0.020 0 (ZZ35) and 0.154 2 (LYP9), respectively. The average relative error was lower than the RGB color space. They were reduced by 0.084 2% (JY458), 0.133 5% (ZZ35) and 0.238 2% (LYP9), respectively. There was a significant correlation between the leaf color image parameters and the SPAD values of rice under the two color spaces. By optimizing support vector machine with improved grid search algorithm, the prediction model of SPAD value of rice leaves was established. The prediction error was small, which could meet the demand of agronomic scientific research, and also provided a new method for the prediction of plant SPAD value.

Key words: rice, SPAD value, support vector machine, HSI, RGB

CLC Number:

S126

SUN Yuting, WANG Yinglong, YANG Hongyun, ZHOU Qiong, SUN Aizhen, YANG Wenji. Prediction of SPAD in rice leaf based on RGB and HSI color space[J]. , 2018, 30(10): 1782-1788.

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Prediction of SPAD in rice leaf based on RGB and HSI color space

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