农业机械学报2024，Vol.55Issue(9)：249-261,13.DOI:10.6041/j.issn.1000-1298.2024.09.021
融合无人机光谱信息与纹理特征的冬小麦综合长势监测
Comprehensive Growth Monitoring of Winter Wheat by Integrating UAV Spectral Information and Texture Features
摘要
Abstract
Efficient and timely acquisition of crop growth information plays an important role in crop production management.At present,crop growth monitoring in small areas is mostly achieved through the inversion of spectral information from UAV.However,further research is needed to comprehensively consider the surface feature information of crops at different growth stages for monitoring crop growth in small areas.Taking winter wheat as the research object,comprehensive growth monitoring indicators(CGMI)was constructed based on the plant height and leaf area index(LAI)of winter wheat according to the coefficient of variation method,and a comprehensive growth monitoring method was proposed for winter wheat that combining UAV spectral information and texture features.A drone equipped with a multispectral lens was used to acquire images of winter wheat in four growth stages,and 12 vegetation indices and 8 types of texture features in each band were obtained.The Person correlation analysis method was used to screen the vegetation index and texture features that had good correlation with CGMI,and then random forest regression(RF),partial least squares regression(PLSR)and support vector regression(SVR)methods were used to construct growth monitoring models based on vegetation index and growth monitoring models based on vegetation index and texture features,respectively.Through comparison,the superior growth monitoring model was obtained,and finally the spatial distribution information of winter wheat growth in the study area was obtained.The results showed that among the three machine learning methods,the SVR growth monitoring model based on vegetation index and texture featureshad the highest accuracy(training set R2was 0.789,MAE was 0.03,NRMSE was 4.8％,RMSE was 0.04).Compared with SVR growth monitoring model based on vegetation index,the coefficient of determination of this model was increased by 5.1％,the average absolute error was decreased by 3.3％,the standard root mean square error was decreased by 8.3％,and the root mean square error was decreased by 10％.The research results showed that the method was accurate and reliable,which can provide an important reference for winter wheat growth monitoring.关键词
冬小麦/综合长势监测指标/无人机/多光谱/纹理特征Key words
winter wheat/comprehensive growth monitoring indicators/UAV/multi spectral/texture features分类
农业科技引用本文复制引用
承达瑜,何伟德,付春晓,赵伟,王建东,赵安周..融合无人机光谱信息与纹理特征的冬小麦综合长势监测[J].农业机械学报,2024,55(9):249-261,13.基金项目
河北省重大科技成果转化专项(22287401Z)和国家自然科学基金项目(42171212) （22287401Z）
