The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field samplin...The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.展开更多
To solve the common problem of flumes flowmeasurement accuracy without sacrificing water head, a new type of trapezoidal cutthroat flume to measure the discharge in terminal trapezoidal channels is presented.Using the...To solve the common problem of flumes flowmeasurement accuracy without sacrificing water head, a new type of trapezoidal cutthroat flume to measure the discharge in terminal trapezoidal channels is presented.Using the computational fluid dynamic method, threedimensional flow fields in trapezoidal cutthroat flumes were simulated using the RNG k-ε three-dimensional turbulence model along with the Tru VOF technique.Simulations were performed for 12 working conditions,with discharges up to 0.075 m3$s–1 to determine hydraulic performance. Experimental data for the trapezoidal cutthroat flume in terminal trapezoidal channel were also obtained to validate the simulation results. Velocity distribution of the flume obtained from simulation analyses were compared with observed results based on timeaveraged flow field and comparison yielded a solid agreement between results from the two methods, with relative error below 10%. The results indicated that the Froude number and the longitudinal average velocity increased along the convergence section and decreased in the divergent section. In the upper throat, the Froude number was less than 0.5, which meets the water measurement requirement, and the critical flow appeared near the throat section. The maximum water head loss of the trapezoidal cutthroat flume was less than 9% of the total head, compared to the rectangular cutthroat flume,and head loss of trapezoidal cutthroat flume was significantly less. Regression models developed for upstream depth versus discharge under different working conditions were satisfactory, with a relative error of less than 2.06%, which meets the common requirements of flow measurement in irrigation areas. It was concluded that trapezoidal cutthroat flumes can improve flow-measurement accuracy without sacrificing water head.展开更多
基金funded by the Key Research and Development Program of Shaanxi Province of China(2022NY-063)the Chinese Universities Scientific Fund(2452020018).
文摘The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.
基金the financial support given by the Special Fund for Agro-scientific Research in the Public Interest of China (201503125)the National Key Research and Development Program of China
文摘To solve the common problem of flumes flowmeasurement accuracy without sacrificing water head, a new type of trapezoidal cutthroat flume to measure the discharge in terminal trapezoidal channels is presented.Using the computational fluid dynamic method, threedimensional flow fields in trapezoidal cutthroat flumes were simulated using the RNG k-ε three-dimensional turbulence model along with the Tru VOF technique.Simulations were performed for 12 working conditions,with discharges up to 0.075 m3$s–1 to determine hydraulic performance. Experimental data for the trapezoidal cutthroat flume in terminal trapezoidal channel were also obtained to validate the simulation results. Velocity distribution of the flume obtained from simulation analyses were compared with observed results based on timeaveraged flow field and comparison yielded a solid agreement between results from the two methods, with relative error below 10%. The results indicated that the Froude number and the longitudinal average velocity increased along the convergence section and decreased in the divergent section. In the upper throat, the Froude number was less than 0.5, which meets the water measurement requirement, and the critical flow appeared near the throat section. The maximum water head loss of the trapezoidal cutthroat flume was less than 9% of the total head, compared to the rectangular cutthroat flume,and head loss of trapezoidal cutthroat flume was significantly less. Regression models developed for upstream depth versus discharge under different working conditions were satisfactory, with a relative error of less than 2.06%, which meets the common requirements of flow measurement in irrigation areas. It was concluded that trapezoidal cutthroat flumes can improve flow-measurement accuracy without sacrificing water head.
