中国农业大学学报2025，Vol.30Issue(3)：206-217,12.DOI:10.11841/j.issn.1007-4333.2025.03.19
基于无人机高光谱影像和机器学习算法的花生生物量估算方法研究
Research on estimating peanut biomass using UAV hyperspectral imaging and machine learning algorithm
摘要
Abstract
Accurate and efficient crop biomass estimation is of remarkable importance to identify superior varieties,regional production management and food security assessment.In order to evaluate the potential of UAV hyperspectral remote sensing technology in crop biomass estimation,a peanut planting test field in Xingyang City was taken as the research object.The hyperspectral image data of multiple varieties of peanuts at maturity were collected by UAV equipped with hyperspectral cameras.A peanut biomass estimation model was constructed by combining various machine learning algorithms.The accuracy of the model was evaluated and compared.Firstly,the reflectance of the hyperspectral images was smoothed and preprocessed using the S-G filter,and continuous wavelet transform was applied using the Gaussian4 wavelet basis function to screen 53 vegetation indices as feature inputs.Secondly,the sensitive vegetation indexes were selected through the Pearson correlation coefficient method.Lastly,the identified vegetation index was used to construct Support Vector Regression(SVR),Random Forest(RF),Convolutional Neural Network(CNN),Particle Swarm Optimization Support Vector Machine(PSO-SVR),Particle Swarm Optimization Random Forest(PSO-RF),and Particle Swarm Optimization Convolutional Neural Network(PSO-CNN)models for estimating peanut biomass were constructed and their accuracy was evaluated.The results indicated that:The deep learning model CNN demonstrated a superior performance in the prediction accuracy of peanut biomass compared to traditional machine learning models such as RF and SVM.The CNN model had a determination coefficient(R2)of 0.710,RMSE of 0.371 kg/m2,MSE of 0.138 kg/m2,and MAE of 0.329 kg/m2 on the test set.After parameter optimization using the Particle Swarm Optimization(PSO)algorithm,the prediction accuracies of RF,SVR,and CNN models were improved,with the CNN model showing the most significant improvement,with an increase of approximately 8.2％in the determination coefficient.Therefore,using the PSO-optimized CNN model during the peanut harvest period provides the most accurate estimation of overall peanut biomass.This study provides a scientific method for accurate peanut biomass prediction and a strong support for the development of smart rural areas.关键词
高光谱影像/连续小波变换/花生生物量/机器学习/智慧乡村Key words
hyperspectral imaging/continuous wavelet transformation/peanut biomass/machine learning/smart rural areas分类
农业科技引用本文复制引用
刘涛,刘望,杨奉源,张寰,殷冬梅,焦有宙,张梅凤,张全国..基于无人机高光谱影像和机器学习算法的花生生物量估算方法研究[J].中国农业大学学报,2025,30(3):206-217,12.基金项目
河南省科技研发计划联合基金青年科学家项目(225200810089) （225200810089）
河南省高校科技创新人才支持计划(23HASTIT028) （23HASTIT028）
河南省青年人才托举工程项目(2024HYTP018) （2024HYTP018）
河南省博士后科研项目(202101041) （202101041）
