摘要
为了快速、无损监测植物叶绿素含量,通过设定两种不同的微生物复垦处理(接菌和对照),并选取6种草本植物(沙打旺、紫花苜蓿、羊草、冰草、老芒麦、无芒雀麦)按4种豆禾混播比例(1∶1、1∶2、1∶3、2∶1)进行试验,分别测定试验小区内沙打旺的叶绿素含量及光谱反射率。采用原始光谱、原始光谱倒数的对数及一阶微分3种指标,结合BP神经网络回归、支持向量机(SVM)回归、随机森林(RF)回归3种建模方法,针对不同处理下的植物光谱特征曲线建立模型。结果表明:接菌处理提高了叶绿素含量,在不同混播比例下,叶绿素的含量也各有差异;与原始光谱曲线相比,其倒数的对数、一阶微分建模在精度上有不同程度的提升,其中一阶微分的建模精度最佳;在微生物复垦条件下,RF回归模型精度最高;在不同种植比例条件下,豆禾1∶2和1∶3区域使用BP神经网络回归建立的模型精度高,而1∶1和2∶1区域的光谱样本则更适合采用RF回归方法。
In order to monitor the chlorophyll content of plants quickly and non-destructively,two different microbial reclamation treatments(inoculation group and control group)were set up,and six herbaceous plants(Astragalus adsurgens,Medicago sativa,Leymus chinensis,Agropyron cristatum,Elymus sibiricus,Bromus inermis)were selected according to four kinds of mixed sowing ratios(1:1,1:2,1:3,2:1).The chlorophyll content and spectral reflectance of Astragalus adsurgens in the test area were measured respectively.Using the original spectrum,the logarithm of the reciprocal of the original spectrum,and the first-order differential,combined with three modeling methods of BP neural network regression,support vector machine(SVM)regression,and random forest(RF)regression,models were established for plant spectral characteristic curves under different treatments.The results show that the inoculation treatment increases the chlorophyll content,and the chlorophyll content is also different under different mixed sowing ratios.Compared with the original spectral curve,the modeling accuracy of the reciprocal logarithm and first order differential of original spectral is improved to varying degrees,and the modeling accuracy of FDR is the best.Under the condition of microbial reclamation,the RF regression model has the highest accuracy.Under the conditions of different planting ratios,the model established by BP neural network regression in the 1:2 and 1:3 regions of legumes has high accuracy,while the spectral samples in the 1:1 and 2:1 regions are more suitable for using RF regression method.
作者
佘长超
解琳琳
王建国
毕银丽
孙郡庆
李梦琪
黄月军
SHE Changchao;XIE Linlin;WANG Jianguo;BI Yinli;SUN Junqing;LI Mengqi;HUANG Yuejun(CHN Energy Beidian Shengli Energy Co.,Ltd.,Xilin hot,Inner Mongolia 026000,China;College of Geology and Environment,Xi'an University of Science and Technology,Xi'an,Shaanxi 710054,China;State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources,China University of Mining and Technology-Beijing,Beijing 100083,China;CHN Energy Xinjiang Toksun Energy Co.,Ltd.,Turpan,Xinjiang 838100,China)
出处
《矿业研究与开发》
北大核心
2025年第10期182-189,共8页
Mining Research and Development
基金
国家重点研发计划项目(2016YFC050106)
国家自然科学基金项目(52404186)。
关键词
排土场复垦
沙打旺
丛枝菌根真菌
叶绿素含量
高光谱技术
Reclamation of mining waste dump
Astragalus adsurgens
Arbuscular mycorrhizal fungi
Chlorophyll content
Hyperspectral technology
