基于仿生优化算法的水稻叶绿素含量反演模型  

Inversion Model of Clorophyll Content in Rice Based on a Bonic Optimization Algorithm

作　　者：李晓凯 于海业[1] 于跃 王洪健 张蕾[1] 张昕[1] 隋媛媛[1] LI Xiao-kai;YU Hai-ye;YU Yue;WANG Hong-jian;ZHANG Lei;ZHANG Xin;SUI Yuan-yuan(College of Biological and Agricultural Engineering,Jilin University,Changchun 130022,China)

机构地区：[1]吉林大学生物与农业工程学院,吉林长春130022

出　　处：《光谱学与光谱分析》2023年第1期93-99,共7页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金面上项目(32171913);国家自然科学基金青年科学基金项目(32001418);吉林省科技发展计划项目(20200402015NC)资助。

摘　　要：用光谱信息精准、高效地检测水稻叶片叶绿素含量,对诊断和优化水稻叶片氮素营养、开发和优化稻田氮素追肥系统、监测和评价水稻病虫害具有重要的实际意义。针对单纯采用机器学习模型反演水稻叶片叶绿素含量模型精确性和稳定性差的问题,以粳稻吉粳88为研究对象,通过网格试验获得分蘖期等关键生育期的叶片表型高光谱数据和相对叶绿素含量。选取核极限学习机(KELM)为基础建模模型,提出了一种先依据基础KELM建模效果选择预处理方法后,再利用仿生优化算法对所选预处理组合所对应的KELM模型的训练过程进行优化的新思路,以提高模型预测精度。首先,对光谱数据的各类预处理方法展开研究,通过对4类预处理方法进行全排列组合共得到72种预处理组合。利用连续投影算法(SPA)选择特征波段输入KELM模型以筛选较优预处理组合。依据建模效果,预处理组合CWT+MMS,CWT+MSC+SG+SS和CWT+SS所对应KELM的测试集决定系数(R^(2)_(p))较高,分别为0.850,0.835和0.828。其次,为使KELM模型在保证稳定性和泛化性的前提下性能达到最优,引入哈里斯鹰优化算法(HHO),通过模拟鹰群在捕食时的合作行为和追逐策略,自动最优调节上述三种KELM模型参数,使得HHO-KELM模型R^(2)_(p)分别为0.957,0.867和0.858,模型精度得到有效提升,最高提升10.7%。通过研究,证明了HHO算法优化机器学习模型反演水稻叶片叶绿素含量的可行性,为东北粳稻叶绿素含量的测定和评估提供了有力的参考和借鉴。The accurate,efficient and nondestructive detection of chlorophyll content in rice leaves using spectral information is of practical importance for diagnosing and optimizing nitrogen nutrition in rice leaves,developing and optimizing nitrogen fertilization systems in rice fields,and monitoring and evaluating rice pests and diseases.This paper addresses the problem of poor model accuracy and stability when machine learning models are used solely to invert the chlorophyll content of rice leaves.Moreover,takes Northeast japonica rice Jijing88 as the research object,obtains leaf phenotypic hyperspectral data and relative chlorophyll content at key fertility stages such as tillering through grid tests,select the kernel limit learning machine(Kernel function extreme learning machine,KELM)in machine learning as the base modeling model,and proposes a new idea of selecting preprocessing methods based on the base KELM modeling effect first,and then optimizing the KELM training process corresponding to the selected preprocessing combination using a bionic optimization algorithm to improve the model prediction accuracy.First,this paper investigates the preprocessing methods of spectral data,and a total of 72 preprocessing combinations are obtained by combining all four types of preprocessing methods.The sequential projection algorithm(Successive Projections Algorithm,SPA)is used to select the characteristic bands for input into the KELM model to filter the better preprocessing combinations.Based on the modeling effect,the test set’s coefficient of determination(R^(2)_(p))corresponding to KELM for the pretreatment combinations CWT+MMS,CWT+MSC+SG+SS,and CWT+SS was higher,0.850,0.835,and 0.828,respectively.Secondly,to make the KELM model perform optimally while ensuring stability and generalization.In this paper,the Harris Hawk Optimization Algorithm(Harris Hawks Optimizer,HHO)is introduced to automatically and optimally adjust the parameters of the above three KELM models by simulating the cooperative behavior and chasing st

关 键 词：哈里斯鹰优化算法 核极限学习机 高光谱 叶绿素含量 

分 类 号：O433.4[机械工程—光学工程]