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Estimation of Rice Biomass at Different Growth Stages by Using Fractal Dimension in Image Processing

College of Sciences, Huazhong Agricultural University, Wuhan 430070, China
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Author to whom correspondence should be addressed.
Academic Editor: Joachim Müller
Appl. Sci. 2021, 11(15), 7151; https://doi.org/10.3390/app11157151
Received: 19 June 2021 / Revised: 20 July 2021 / Accepted: 21 July 2021 / Published: 3 August 2021
(This article belongs to the Section Agricultural Science and Technology)
Rice has long served as the staple food in Asia, and the cultivation of high-yield rice crops draws increasing attention from academic researchers. The prediction of rice growth condition by image features realizes nondestructive prediction and it has great implications for smart agriculture. We found a special image parameter called the fractal dimension that can improve the effect of the prediction model. As an important geometric feature, the fractal dimension could be calculated from the image, but it is rarely used in the field of rice growth prediction. In this paper, we attempt to combine the fractal dimension with traditional rice image features to improve the effect of the model. The thresholding method is used to transform the cropped rice image into binary image, and the box-counting method is used to calculate the fractal dimension of the image. The correlation coefficients are calculated to select the characteristics with a strong correlation with biomass. The prediction models of dry weight, fresh weight and plant height of rice are established by using random forest, support vector regression and linear regression. By evaluating the prediction effect of the model, it can be concluded that the fractal dimension can improve the prediction effect of the model. Among the models obtained by the three methods, the multiple linear regression model has the best comprehensive effect, with the dry weight prediction model R2 reaching 0.8697, the fresh weight prediction model R2 reaching 0.8631 and the plant height prediction model R2 reaching 0.9196. The model established in this paper has a fine effect and has a certain guiding significance in rice research. View Full-Text
Keywords: rice biomass; fractal; machine learning; predictive model rice biomass; fractal; machine learning; predictive model
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MDPI and ACS Style

Hu, Y.; Shen, J.; Qi, Y. Estimation of Rice Biomass at Different Growth Stages by Using Fractal Dimension in Image Processing. Appl. Sci. 2021, 11, 7151. https://doi.org/10.3390/app11157151

AMA Style

Hu Y, Shen J, Qi Y. Estimation of Rice Biomass at Different Growth Stages by Using Fractal Dimension in Image Processing. Applied Sciences. 2021; 11(15):7151. https://doi.org/10.3390/app11157151

Chicago/Turabian Style

Hu, Yijun, Jingfang Shen, and Yonghao Qi. 2021. "Estimation of Rice Biomass at Different Growth Stages by Using Fractal Dimension in Image Processing" Applied Sciences 11, no. 15: 7151. https://doi.org/10.3390/app11157151

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