摘要
【目的】火龙果植株空间信息及株数的快速、无损获取是长势精准监测及区域种植结构调整的重要前提。构建精度较高的火龙果株数提取模型,进一步为贵州省山地特色智慧农业发展提供科研理论支持。【方法】使用低空无人机搭载Pika XC2传感器采集贵州省关岭县上官镇火龙果种植区高光谱遥感影像,通过该影像对地表主要地物光谱曲线进行光谱变换以挖掘高光谱影像数据潜力并使用特征选择剔除冗余变量,基于多种机器学习分类模型精确划分研究区不同地物,同时结合实测的火龙果植株投影面积计算其株数。【结果】(1)火龙果植株原始高光谱曲线在可见光波长区间反射率较低,近红外波长区间反射率较高;不同类型光谱下火龙果植株与其余地物光谱反射率差异较大的波段不同;(2)以特征距离定义筛选策略的特征选择算法降维效果较好,各区域不同类型光谱下火龙果植株特征波段数量介于2~9个,降维比均在97%以上;(3)地物分类精度以及株数提取精度与地表复杂度成反比。所有分类模型中连续统去除光谱下的随机森林模型精度最高,其总体分类精度与Kappa系数分别在84%及0.87以上。该模型下火龙果株数提取效果最好,不同区域提取精度在83%以上。【结论】连续统去除变换与随机森林算法的结合可较为准确地识别火龙果植株信息,该思路可为大尺度下喀斯特地区火龙果植株空间信息获取提供技术参考。
【Objective】Rapid and non-destructive acquisition of plant spatial information and plant number of pitaya is an important prerequisite for accurate monitoring its growth and adjusting regional planting structure.Traditional field measurement is costly and inefficient,however,hyperspectral re-mote sensing is simple to operate and the data is more sensitive to vegetation,so it has become an effec-tive means for non-contact acquisition of vegetation spatial information at a large scale.【Methods】The DJI M600 low-altitude UAV equipped with Pika XC2 sensor was used to collect hyperspectral remote sensing images of pitaya growing areas in Shangguan town,Guanling county,Guizhou province.Differ-ent regions were divided according to surface complexity and the spectral curves of major surface ob-jects were calculated using Envi 5.3.After Savitzky-Golay second-order smoothing,first derivative spectrum(FDS)and continuum removal spectrum(CRS)were derived to explore the potential of hyper-spectral image data,and a feature selection method was proposed to eliminate redundant variables by defining dimension reduction strategy from feature distance.Based on artificial neural network(ANN),support vector machine(SVM)and random forest(RF)machine learning models,different ground ob-jects in the study area were divided,and the plant number was calculated by combining the projected ar-ea of pitaya measured on the surface.【Results】The results were as follows:(1)The reflectance of the primary hyperspectral curve of pitaya was lower in the visible wavelength region and higher in the near infrared wavelength region,and the reflectance between them was connected by red edge;The spectral reflectance of pitaya and other ground objects were different in different spectral types.The primary spectrum was located in the“red valley”and“high reflective platform”,the first derivative spectrum was located in the“red edge”and“green peak”,and the continuum removal spectrum was located in the“red valley”and“green peak”.(2)The feature selection algorithm defined by the feature distance had a better dimensionality reduction effect,and the number of feature bands was proportional to the surface complexity.The number of feature bands of pitaya ranged from 2 to 9 under different spectral types in each region,and the dimensionality reduction ratio was all above 97%.The spectral transforma-tion could effectively reduce the number of feature bands and the distance between features.The charac-teristic bands of different spectral types in each region were mainly concentrated in the“red valley”,“red edge”and“near infrared”regions.(3)The classification accuracy of ground objects and the extrac-tion accuracy of plant number were inversely proportional to the surface complexity.Among all classifi-cation models,the accuracy of CRS-RF models was the best,and the overall classification accuracy and Kappa coefficient were above 84%and 0.87,respectively,indicating that the training set and the result set were completely consistent.CRS-RF models had the best effect on the number of pitaya,and the ac-curacy in different regions was above 83.33%.【Conclusion】The combination of continuum removal transformation and random forest algorithm can accurately identify pitaya plant information,which can provide technical reference for obtaining the spatial information of pitaya plants in karst area at a large scale.In practical application,it is only necessary to input hyperspectral remote sensing image of the study area into the trained CRS-RF model,and then the space position and plant number of pitaya in the corresponding region can be output.
作者
郭松
舒田
赵泽英
许元红
陈智虎
蒋丹垚
GUO Song;SHU Tian;ZHAO Zeying;XU Yuanhong;CHEN Zhihu;JIANG Danyao(Guizhou Agricultural Science and Technology Information Institute,Guiyang 550006,Guizhou,China;College of Natural Resources and Environment,Northwest A&F University,Yangling 712100,Shaanxi,China)
出处
《果树学报》
北大核心
2025年第12期2898-2909,共12页
Journal of Fruit Science
基金
科研机构创新能力建设专项(黔科合服企[2021]15号)。
关键词
火龙果
无人机高光谱
光谱变换
特征选择
机器学习
株数提取
Pitaya
UAV hyperspectral remote sensing
Spectral transformation
Feature selection
Ma-chine learning
Extraction number of pitaya plant
