基于特征筛选和粒子群优化的花生生物量估算  

Estimation of peanut biomass based on feature selection and particle swarm optimization

作　　者：刘涛[1,2] 杨奉源 刘望 张寰 殷冬梅 张全国[2] 焦有宙[2,4] LIU Tao;YANG Fengyuan;LIU Wang;ZHANG Huan;YIN Dongmei;ZHANG Quanguo;JIAO Youzhou(School of Urban and Rural Planning,Henan University of Economics and Law,Zhengzhou 450046,China;School of Mechanical and Electrical Engineering,Henan Agricultural University,Zhengzhou 450002,China;College of Agronomy,Henan Agricultural University,Zhengzhou 450002,China;Mechanical Engineering College,Henan University of Engineering,Zhengzhou 451191,China)

机构地区：[1]河南财经政法大学城乡规划学院,郑州450046 [2]河南农业大学机电工程学院,郑州450002 [3]河南农业大学农学院,郑州450002 [4]河南工程学院机械工程学院,郑州451191

出　　处：《农业工程学报》2025年第1期238-247,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：河南省科技研发计划联合基金青年科学家项目(225200810089);河南省高校科技创新人才支持计划(23HASTIT028);河南省青年人才托举工程项目(2024HYTP018);河南省博士后科研项目(202101041);河南省中原科技创新领军人才项目(234200510029)。

摘　　要：为解决花生植株生物量估算精度低、破坏性大等问题,该研究提出一种无人机低空遥感技术结合高光谱特征筛选的花生生物量估算方法。通过无人机搭载高光谱成像仪,获取田块尺度多个花生品种的高光谱影像数据,首先对获取的影像进行拼接、辐射定标、大气校正等预处理,提取出地面采样点位置的光谱反射率,计算光谱反射率的一阶微分和植被指数,使用变量投影重要性(variable importance in projection,VIP)方法对光谱反射率、一阶微分和植被指数等三种数据进行特征筛选,利用筛选后的特征和地面实测数据构建支持向量机回归(support vector regression,SVR)、反向传播神经网络回归(back propagation neural network,BPNN)和随机森林回归(random forest regression,RFR)模型,并使用粒子群优化算法(particle swarm optimization,PSO)进行模型优化。结果表明:相比原始光谱反射率和植被指数,一阶微分光谱反射率与花生生物量具有较好的相关性;使用一阶微分光谱反射率与植被指数组合的RF回归模型精度最高(决定系数R^(2)为0.754,均方根误差RMSE为0.085 kg/m^(2)),使用粒子群优化后的PSO-RF模型可进一步提高模型精度(R^(2)为0.80,RMSE为0.076 kg/m^(2))。该研究为花生生物量精准估算提供了有效的方法,为智慧乡村建设中的精细化农田管理提供技术支持。Peanut is one of the most widely cultivated oil crops globally,with China leading in both production and consumption.As the demand for oil crops increases,ensuring stable peanut production and oil supply security has become a key agricultural goal.Peanut biomass,as a crucial parameter reflecting crop growth status,is essential for precision agriculture management and efficient resource utilization.The aboveground parts of peanut plants can be used not only as animal feed but also as a resource for bioenergy production.Therefore,comprehensive and accurate biomass estimation provides valuable references for yield prediction and resource management.Traditional biomass measurement methods are often labor-intensive and time-consuming,with spatial and temporal limitations.Recently,with the development of UAV remote sensing,especially the widespread application of hyperspectral imaging technology,crop biomass estimation has become more efficient.Hyperspectral imaging,known for its high resolution and rich spectral information,has been used for growth monitoring and yield estimation of crops such as soybean,rice,and wheat,demonstrating superior performance in predicting parameters like yield,chlorophyll,and nitrogen content,as well as in disease diagnosis.However,research on peanut remains limited,particularly regarding the spectral characteristics of different peanut varieties and their impact on biomass estimation accuracy.This study,using UAV hyperspectral imaging,investigated sensitive spectral bands and feature combinations for efficient and accurate field-scale peanut biomass estimation.An experimental field with 11 peanut varieties in Xingyang,Henan,was used as the study area.First,UAV hyperspectral images of the test field were collected and preprocessed with radiometric calibration and atmospheric correction to ensure data accuracy.Spectral reflectance data from ground sampling points were then extracted,and the first derivative of spectral reflectance and multiple vegetation indices were calculated to enhance t

关 键 词：花生 生物量 智慧乡村 特征筛选 机器学习 粒子群优化 

分 类 号：S252[农业科学—农业机械化工程]