ML-LME: A Plant Growth Situation Analysis Model Using the Hierarchical Effect of Fractal Dimension

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ID: 265597
2021
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Abstract
Rice plays an essential role in agricultural production as the most significant food crop. Automated supervision in the process of crop growth is the future development direction of agriculture, and it is also a problem that needs to be solved urgently. Productive cultivation, production and research of crops are attributed to increased automation of supervision in the growth. In this article, for the first time, we propose the concept of rice fractal dimension heterogeneity and define it as rice varieties with different fractal dimension values having various correlations between their traits. To make a comprehensive prediction of the rice growth, Machine Learning and Linear Mixed Effect (ML-LME) model is proposed to model and analyze this heterogeneity, which is based on the existing automatic measurement system RAP and introduces statistical characteristics of fractal dimensions as novel features. Machine learning algorithms are applied to distinguish the rice growth stages with a high degree of accuracy and to excavate the heterogeneity of rice fractal dimensions with statistical meaning. According to the information of growth stage and fractal dimension heterogeneity, a precise prediction of key rice phenotype traits can be received by ML-LME using a Linear Mixed Effect model. In this process, the value of the fractal dimension is divided into groups and then rices of different levels are respectively fitted to improve the accuracy of the subsequent prediction, that is, the heterogeneity of the fractal dimension. Afterwards, we apply the model to analyze the rice pot image. The research results show that the ML-LME model, which possesses the hierarchical effect of fractal dimension, performs more excellently in predicting the growth situation of plants than the traditional regression model does. Further comparison confirmed that the model we proposed is the first to consider the hierarchy structure of plant fractal dimension, and that consideration obviously strengthens the model on the ability of variation interpretation and prediction precision.
Reference Key
ma2021mathematicsml-lme: Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xiaohang Ma;Yongze Wu;Jingfang Shen;Lingfeng Duan;Ying Liu;Ma, Xiaohang;Wu, Yongze;Shen, Jingfang;Duan, Lingfeng;Liu, Ying;
Journal Mathematics
Year 2021
DOI
10.3390/math9121322
URL
https://www.mdpi.com/2227-7390/9/12/1322/htm
Keywords
Machine learning fractal theory linear mixed effect model rice phenotype heterogeneity

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