Swarm intelligence algorithms own superior performance in solving high-dimensional and multi-objective optimization problems.The application of the swarm intelligence algorithms to visible and near-infrared(VIS-NIR)sp...Swarm intelligence algorithms own superior performance in solving high-dimensional and multi-objective optimization problems.The application of the swarm intelligence algorithms to visible and near-infrared(VIS-NIR)spectral analysis of soil moisture can contribute to the optimization of the soil moisture prediction model and the development of the real-time soil moisture sensor.In this study,a high-resolution spectrometer was used to obtain spectral data of different levels of soil moisture which were manually configured.Isolation Forest algorithm(iForest)was used to eliminate outliers from the data.Based on the root mean square error of prediction RMSEP of Back Propagation Neural Network(BPNN)model results,a series of new swarm intelligence algorithms,including Manta Ray Foraging Optimization(MRFO),Slime Mould Algorithm(SMA),etc.,were used to select the characteristic wavelengths of soil moisture.The analysis results showed that MRFO owned the best performance if only from the predictive capability perspective and SMA had a better performance when considering the proportion of the selecting wavelengths and the results of the model prediction.By comparing and analyzing the modeling results of traditional intelligence algorithms Genetic Algorithm(GA)and Particle Swarm Optimization(PSO),it was found that the new swarm intelligence had a better performance in selecting the characteristic wavelengths of soil moisture.Integrating the results of all intelligence algorithms used,soil moisture sensitive wavelengths were selected as 490 nm,513 nm,543 nm,900 nm and 926 nm,which provide the basis for the design of real-time soil moisture sensor based on VIS-NIR.展开更多
以以色列南部Seder Boker地区采集的粘壤土样品为研究对象。在室内利用ASD Field Spec 3型高光谱仪获取土壤的原始光谱,在进行数据预处理和不同数学变换后,共获取了4种光谱指标:光谱反射率(REF)、倒数之对数(LR)、一阶微分(FDR)和去包络...以以色列南部Seder Boker地区采集的粘壤土样品为研究对象。在室内利用ASD Field Spec 3型高光谱仪获取土壤的原始光谱,在进行数据预处理和不同数学变换后,共获取了4种光谱指标:光谱反射率(REF)、倒数之对数(LR)、一阶微分(FDR)和去包络线(CR)。采用偏最小二乘回归法(PLSR)、逐步回归法(SR)和岭回归法(RR)构建了基于不同指标的土壤含水率高光谱反演模型,并对反演结果进行精度验证与比较。结果表明:REF-PLSR模型在所有回归模型中的反演与预测效果均为最优(R2c=0.990,R2p=0.987),在逐步回归模型和岭回归模型中,LR-SR(R2c=0.981,R2p=0.971)、LR-RR(R2c=0.975,R2p=0.979)均为最佳模型。对于其他3种指标,虽然逐步回归法和岭回归法的建模效果较偏最小二乘回归法略有下降,但R2c均大于0.9,R2p均大于0.8,RPD均大于2.5,RMSE均小于0.03,模型仍具有较好的反演效果;逐步回归法和岭回归法均实现了模型的简化,但岭回归法采用有偏估计从而提高了模型的稳健性,且实现了波段的优选(用于建模的波段数仅为全光谱的0.3%)。粘壤土土壤含水率LR-RR高光谱反演模型的建立为高光谱模型的优化、土壤含水率的快速测定提供了途径。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.32071915)China Agriculture Research System of MOF and MARA-Food Legumes(CARS-08).
文摘Swarm intelligence algorithms own superior performance in solving high-dimensional and multi-objective optimization problems.The application of the swarm intelligence algorithms to visible and near-infrared(VIS-NIR)spectral analysis of soil moisture can contribute to the optimization of the soil moisture prediction model and the development of the real-time soil moisture sensor.In this study,a high-resolution spectrometer was used to obtain spectral data of different levels of soil moisture which were manually configured.Isolation Forest algorithm(iForest)was used to eliminate outliers from the data.Based on the root mean square error of prediction RMSEP of Back Propagation Neural Network(BPNN)model results,a series of new swarm intelligence algorithms,including Manta Ray Foraging Optimization(MRFO),Slime Mould Algorithm(SMA),etc.,were used to select the characteristic wavelengths of soil moisture.The analysis results showed that MRFO owned the best performance if only from the predictive capability perspective and SMA had a better performance when considering the proportion of the selecting wavelengths and the results of the model prediction.By comparing and analyzing the modeling results of traditional intelligence algorithms Genetic Algorithm(GA)and Particle Swarm Optimization(PSO),it was found that the new swarm intelligence had a better performance in selecting the characteristic wavelengths of soil moisture.Integrating the results of all intelligence algorithms used,soil moisture sensitive wavelengths were selected as 490 nm,513 nm,543 nm,900 nm and 926 nm,which provide the basis for the design of real-time soil moisture sensor based on VIS-NIR.
文摘以以色列南部Seder Boker地区采集的粘壤土样品为研究对象。在室内利用ASD Field Spec 3型高光谱仪获取土壤的原始光谱,在进行数据预处理和不同数学变换后,共获取了4种光谱指标:光谱反射率(REF)、倒数之对数(LR)、一阶微分(FDR)和去包络线(CR)。采用偏最小二乘回归法(PLSR)、逐步回归法(SR)和岭回归法(RR)构建了基于不同指标的土壤含水率高光谱反演模型,并对反演结果进行精度验证与比较。结果表明:REF-PLSR模型在所有回归模型中的反演与预测效果均为最优(R2c=0.990,R2p=0.987),在逐步回归模型和岭回归模型中,LR-SR(R2c=0.981,R2p=0.971)、LR-RR(R2c=0.975,R2p=0.979)均为最佳模型。对于其他3种指标,虽然逐步回归法和岭回归法的建模效果较偏最小二乘回归法略有下降,但R2c均大于0.9,R2p均大于0.8,RPD均大于2.5,RMSE均小于0.03,模型仍具有较好的反演效果;逐步回归法和岭回归法均实现了模型的简化,但岭回归法采用有偏估计从而提高了模型的稳健性,且实现了波段的优选(用于建模的波段数仅为全光谱的0.3%)。粘壤土土壤含水率LR-RR高光谱反演模型的建立为高光谱模型的优化、土壤含水率的快速测定提供了途径。
