基于RFE_RF算法的幼龄沉香叶片含水率预估模型

王甜; 王雪峰; 刘嘉政

doi:10.12302/j.issn.1000-2006.202010043

王甜, 王雪峰, 刘嘉政

南京林业大学学报（自然科学版） ›› 2022, Vol. 46 ›› Issue (4) : 177-184.

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南京林业大学学报（自然科学版） ›› 2022, Vol. 46 ›› Issue (4) : 177-184. DOI: 10.12302/j.issn.1000-2006.202010043

研究论文

基于RFE_RF算法的幼龄沉香叶片含水率预估模型

王甜 ¹ ,
王雪峰 ¹^,^* ,
刘嘉政 ²

作者信息 +

The prediction model of moisture content of young Aquilaria sinensis leaves based on RFE_RF algorithm

Author information +

文章历史 +

摘要

【目的】针对随机森林算法在树木水分预测模型中高维度变量筛选困难及精度较低的问题,研究基于递归特征消除(RFE)与随机森林(RF)的融合算法,构建幼龄沉香(Aquilaria sinensis)可见光图像与叶片含水率的估测模型,探索适合幼龄沉香生长的水分条件,为实现沉香幼苗水分亏缺程度的无损监测提供可行方法。【方法】以2年生的名贵树种沉香为研究对象,用相机获取4种不同水分梯度下的幼龄沉香可见光图像,提取15种图像特征,利用递归特征消除法筛选沉香叶片最优的图像特征子集,然后结合随机森林算法构建沉香叶片含水率的预测模型,最后利用十折交叉验证法,将RFE_RF模型与常规随机森林(RF)以及最小二乘法支持向量机(LSSVM)相比较,检验模型的可行性。利用递归特征消除和随机森林融合(RFE_RF)算法筛选出幼龄沉香叶片图像的标准红光值(I_NR)、饱和度(S)、矩形度(E_R)3个特征,并以此作为模型自变量。【结果】与重度水淹胁迫相比,幼龄沉香对于长期重度干旱胁迫更加敏感,且干旱时间超出2周时幼苗叶片严重受损,威胁沉香生长;沉香最适叶片水分生长范围为50%~65%,适度增加水分,有利于沉香生长。基于RFE_RF融合算法构建的预测模型敏感度、特异性、误报率和精度分别达到88.64%、85. 31%、14. 39%和91.62%,优于LSSVM模型效果;与RF预测模型相比其敏感度提高3.34%、特异性提高10.87%、误报率降低36.83%、精度提高13.39%。【结论】基于RFE_RF融合算法建立的沉香叶片颜色、形状特征与含水率的模型,解决了随机森林过程中高维度变量选择问题,提高了RF在林木水分预测模型中的精度,实现了沉香幼苗叶片含水率的无损估测和诊断,为珍贵树种在经营管理中对水分进行准确管控提供了新思路。

Abstract

【Objective】 To solve the problems of difficult selections of high-dimensional variables and low accuracy of random forest algorithm in tree water prediction model, this paper studies the fusion algorithm based on recursive feature elimination (RFE) and random forest (RF), constructs the estimation model of visible light images and leaf water content of young A. sinensis, explores the water conditions suitable for the growth of young Aquilaria sinensis, and provides a feasible reference method for realizing the nondestructive monitoring of water deficit degree of A. sinensis seedlings. 【Method】 Taking two-years-old precious tree species of A. sinensis as the research object, the visible light images of young A. sinensis under four different water gradients were obtained by camera, 15 image features were extracted, the recursive feature elimination method was used to screen the optimal image feature subset of agallochum leaves, and then the prediction model of water content of A. sinensis leaves was constructed combined with RF algorithm. Finally, the RFE_RF model is compared with the conventional RF and least square support vector machine (LSSVM) to test the feasibility of the model. Three features of standard red light value (I_NR), saturation (S) and rectangularity (E_R) of young A. sinensis leaf images were screened out by recursive feature elimination and random forest fusion (RFE_RF) algorithm, and used as the model independent variables. 【Result】 The results showed that compared with severe flooding stress, young A. sinensis was more sensitive to long-term severe drought stress, and the drought time exceeded two weeks, the leaves of the seedlings were severely damaged, threatening the growth of A. sinensis; the optimum leaf water growth range of A. sinensis was 50%-65%, moderately increase moisture, which is conducive to the growth of A. sinensis. The false alarm rate and accuracy of the prediction model constructed based on the RFE_RF fusion algorithm reached 88.64%, 85.31%, 14.39% and 91.62%, respectively, which were better than the LSSVM model; the sensitivity is increased by 3.34%, the specificity is increased by 10.87%, the false alarm rate is decreased by 36.83%, and the precision is increased by 13.39%. 【Conclusion】 The model of Aquilaria sinensis leaf color, shape characteristics and water content established by RFE_RF solves the problem of high-dimensional variable selections in the random forest process, improves the accuracy of RF in the forest moisture prediction model, and realizes the analysis of leaf water content of Aquilaria sinensis seedlings. The non-destructive estimation and diagnosis provide new ideas for accurate water management and management of precious tree species.

导出引用

王甜, 王雪峰, 刘嘉政. 基于RFE_RF算法的幼龄沉香叶片含水率预估模型[J]. 南京林业大学学报（自然科学版）. 2022, 46(4): 177-184 https://doi.org/10.12302/j.issn.1000-2006.202010043

WANG Tian, WANG Xuefeng, LIU Jiazheng. The prediction model of moisture content of young Aquilaria sinensis leaves based on RFE_RF algorithm[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2022, 46(4): 177-184 https://doi.org/10.12302/j.issn.1000-2006.202010043

中图分类号： TP181;S718

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