Crop leaf area index(LAI)and biomass are two major biophysical parameters to measure crop growth and health condition.Measuring LAI and biomass in field experiments is a destructive method.Therefore,we focused on the ...Crop leaf area index(LAI)and biomass are two major biophysical parameters to measure crop growth and health condition.Measuring LAI and biomass in field experiments is a destructive method.Therefore,we focused on the application of unmanned aerial vehicles(UAVs)in agriculture,which is a cost and labor-efficientmethod.Hence,UAV-captured multispectral images were applied to monitor crop growth,identify plant bio-physical conditions,and so on.In this study,we monitored soybean crops using UAV and field experiments.This experiment was conducted at theMAFES(Mississippi Agricultural and Forestry Experiment Station)Pontotoc Ridge-Flatwoods Branch Experiment Station.It followed a randomized block design with five cover crops:Cereal Rye,Vetch,Wheat,MC:mixed Mustard and Cereal Rye,and native vegetation.Planting was made in the fall,and three fertilizer treatments were applied:Synthetic Fertilizer,Poultry Litter,and none,applied before planting the soybean,in a full factorial combination.We monitored soybean reproductive phases at R3(initial pod development),R5(initial seed development),R6(full seed development),and R7(initial maturity)and used UAV multispectral remote sensing for soybean LAI and biomass estimations.The major goal of this study was to assess LAI and biomass estimations from UAV multispectral images in the reproductive stages when the development of leaves and biomass was stabilized.Wemade about fourteen vegetation indices(VIs)fromUAVmultispectral images at these stages to estimate LAI and biomass.Wemodeled LAI and biomass based on these remotely sensed VIs and ground-truth measurements usingmachine learning methods,including linear regression,Random Forest(RF),and support vector regression(SVR).Thereafter,the models were applied to estimate LAI and biomass.According to the model results,LAI was better estimated at the R6 stage and biomass at the R3 stage.Compared to the other models,the RF models showed better estimation,i.e.,an R^(2) of about 0.58–0.68 with an RMSE(rootmean square error)of 0.52–0.60(m^(2)/m^(2))for the LAI and about 0.44–0.64 for R^(2) and 21–26(g dry weight/5 plants)for RMSE of biomass estimation.We performed a leave-one-out cross-validation.Based on cross-validatedmodels with field experiments,we also found that the R6 stage was the best for estimating LAI,and the R3 stage for estimating crop biomass.The cross-validated RF model showed the estimation ability with an R^(2) about 0.25–0.44 and RMSE of 0.65–0.85(m^(2)/m^(2))for LAI estimation;and R^(2) about 0.1–0.31 and an RMSE of about 28–35(g dry weight/5 plants)for crop biomass estimation.This result will be helpful to promote the use of non-destructive remote sensing methods to determine the crop LAI and biomass status,which may bring more efficient crop production and management.展开更多
Accurate recognition of maize seedlings on the plot scale under the disturbance of weeds is crucial for early seedling replenishment and weed removal.Currently,UAV-based maize seedling recognition depends primarily on...Accurate recognition of maize seedlings on the plot scale under the disturbance of weeds is crucial for early seedling replenishment and weed removal.Currently,UAV-based maize seedling recognition depends primarily on RGB images.The main purpose of this study is to compare the performances of multispectral images and RGB images of unmanned aerial vehicle(UAV)on maize seeding recognition using deep learning algorithms.Additionally,we aim to assess the disturbance of different weed coverage on the recognition of maize seeding.Firstly,principal component analysis was used in multispectral image transformation.Secondly,by introducing the CARAFE sampling operator and a small target detection layer(SLAY),we extracted the contextual information of each pixel to retain weak features in the maize seedling image.Thirdly,the global attention mechanism(GAM)was employed to capture the features of maize seedlings using the dual attention mechanism of spatial and channel information.The CGS-YOLO algorithm was constructed and formed.Finally,we compared the performance of the improved algorithm with a series of deep learning algorithms,including YOLO v3,v5,v6 and v8.The results show that after PCA transformation,the recognition mAP of maize seedlings reaches 82.6%,representing 3.1 percentage points improvement compared to RGB images.Compared with YOLOv8,YOLOv6,YOLOv5,and YOLOv3,the CGS-YOLO algorithm has improved mAP by 3.8,4.2,4.5 and 6.6 percentage points,respectively.With the increase of weed coverage,the recognition effect of maize seedlings gradually decreased.When weed coverage was more than 70%,the mAP difference becomes significant,but CGS-YOLO still maintains a recognition mAP of 72%.Therefore,in maize seedings recognition,UAV-based multispectral images perform better than RGB images.The application of CGS-YOLO deep learning algorithm with UAV multi-spectral images proves beneficial in the recognition of maize seedlings under weed disturbance.展开更多
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
Spartina alterniflora's robust reproductive capacity has enabled it to spread rapidly, posing a serious threat to native ecosystems in China. Therefore, accurate quantification of Spartina alterniflora aboveground...Spartina alterniflora's robust reproductive capacity has enabled it to spread rapidly, posing a serious threat to native ecosystems in China. Therefore, accurate quantification of Spartina alterniflora aboveground biomass at a fine scale is crucial for understanding its growth dynamics and managing its invasion. This study focuses on the coastal wetlands of central Jiangsu Province, China, utilizing multispectral unmanned aerial vehicle(UAV) data to map the distribution of Spartina alterniflora. Object-based image analysis(OBIA) combined with support vector machines(SVM) was employed for classification. Additionally, multiple regression models, including univariate, band-based, vegetation index(VI)-based, and multivariate linear regression models integrating both band and VI data, were developed to estimate biomass:(1) the Bands + VIs multiple linear regression model based on fresh weight exhibited the highest estimation accuracy;(2) the optimal model achieved R^(2) values of 0.81 and 0.82 at Dafeng and Tiaozini Nature Reserve,with RMSE values of 591.78 g/m^(2) and 337.62 g/m^(2), and MAE values of 576.82 g/m^(2) and 287.71 g/m^(2), respectively;and(3) the aboveground biomass of Spartina alterniflora primarily ranged from 994.60 g/m^(2) to 5 351.48 g/m^(2) at Dafeng and from 796.05 g/m^(2) to 1 994.02 g/m^(2) in Tiaozini Nature Reserve. These findings highlight the effectiveness of multispectral UAV technology for accurately estimating Spartina alterniflora biomass, providing a robust methodology for wetland vegetation monitoring and invasive species management.展开更多
Leaf Area of Index(LAI)refers to half of the total leaf area of all crops per unit area.It is an important index to represent the photosynthetic capacity and biomass of crops.To obtain LAI conditions of summer maize i...Leaf Area of Index(LAI)refers to half of the total leaf area of all crops per unit area.It is an important index to represent the photosynthetic capacity and biomass of crops.To obtain LAI conditions of summer maize in different growth stages quickly and accurately,further guiding field fertilization and irrigation.The Unmanned aerial vehicles(UAV)multispectral data,growing degree days,and canopy height model of 2020-2021 summer maize were used to carry out LAI inversion.The vegetation index was constructed by the ground hyperspectral data and multispectral data of the same range of bands.The correlation analysis was conducted to verify the accuracy of the multispectral data.To include many bands as possible,four vegetation indices which included R,G,B,and NIR bands were selected in this study to test the spectral accuracy.There were nine vegetation indices calculated with UAV multispectral data which were based on the red band and the near-infrared band.Through correlation analysis of LAI and the vegetation index,vegetation indices with a higher correlation to LAI were selected to construct the LAI inversion model.In addition,the Canopy Height Model(CHM)and Growing degree days(GDD)of summer maize were also calculated to build the LAI inversion model.The LAI inversion of summer maize was carried out based on multi-growth stages by using the general linear regression model(GLR),Multivariate nonlinear regression model(MNR),and the partial least squares regression(PLSR)models.R²and RMSE were used to assess the accuracy of the model.The results show that the correlation between UAV multispectral data and hyperspectral data was greater than 0.64,which was significant.The Wide Dynamic Range Vegetation Index(WDRVI),Normalized Difference Vegetation Index(NDVI),Ratio Vegetation Index(RVI),Plant Biochemical Index(PBI),Optimized Soil-Adjusted Vegetation Index(OSAVI),CHM and GDD have a higher correlation with LAI.By comparing the models constructed by the three methods,it was found that the PLSR has the best inversion effect.It was based on OSAVI,GDD,RVI,PBI,CHM,NDVI,and WDRVI,with the training model’s R²being 0.8663,the testing model’s R²being 0.7102,RMSE was 1.1755.This study showed that the LAI inversion model based on UAV multispectral vegetation index,GDD,and CHM improves the accuracy of LAI inversion effectively.That means the growing degree days and crop population structure change have influenced the change of maize LAI certainly,and this method can provide decision support for maize growth monitoring and field fertilization.展开更多
基金This research was supported in part by a postdoctoral research fellow appointment to the Agricultural Research Service(ARS)Research Participation Program administered by the Oak Ridge Institute for Science and Education(ORISE)through an interagency agreement between the U.S.Department of Energy(DOE)and the U.S.Department of Agriculture(USDA).
文摘Crop leaf area index(LAI)and biomass are two major biophysical parameters to measure crop growth and health condition.Measuring LAI and biomass in field experiments is a destructive method.Therefore,we focused on the application of unmanned aerial vehicles(UAVs)in agriculture,which is a cost and labor-efficientmethod.Hence,UAV-captured multispectral images were applied to monitor crop growth,identify plant bio-physical conditions,and so on.In this study,we monitored soybean crops using UAV and field experiments.This experiment was conducted at theMAFES(Mississippi Agricultural and Forestry Experiment Station)Pontotoc Ridge-Flatwoods Branch Experiment Station.It followed a randomized block design with five cover crops:Cereal Rye,Vetch,Wheat,MC:mixed Mustard and Cereal Rye,and native vegetation.Planting was made in the fall,and three fertilizer treatments were applied:Synthetic Fertilizer,Poultry Litter,and none,applied before planting the soybean,in a full factorial combination.We monitored soybean reproductive phases at R3(initial pod development),R5(initial seed development),R6(full seed development),and R7(initial maturity)and used UAV multispectral remote sensing for soybean LAI and biomass estimations.The major goal of this study was to assess LAI and biomass estimations from UAV multispectral images in the reproductive stages when the development of leaves and biomass was stabilized.Wemade about fourteen vegetation indices(VIs)fromUAVmultispectral images at these stages to estimate LAI and biomass.Wemodeled LAI and biomass based on these remotely sensed VIs and ground-truth measurements usingmachine learning methods,including linear regression,Random Forest(RF),and support vector regression(SVR).Thereafter,the models were applied to estimate LAI and biomass.According to the model results,LAI was better estimated at the R6 stage and biomass at the R3 stage.Compared to the other models,the RF models showed better estimation,i.e.,an R^(2) of about 0.58–0.68 with an RMSE(rootmean square error)of 0.52–0.60(m^(2)/m^(2))for the LAI and about 0.44–0.64 for R^(2) and 21–26(g dry weight/5 plants)for RMSE of biomass estimation.We performed a leave-one-out cross-validation.Based on cross-validatedmodels with field experiments,we also found that the R6 stage was the best for estimating LAI,and the R3 stage for estimating crop biomass.The cross-validated RF model showed the estimation ability with an R^(2) about 0.25–0.44 and RMSE of 0.65–0.85(m^(2)/m^(2))for LAI estimation;and R^(2) about 0.1–0.31 and an RMSE of about 28–35(g dry weight/5 plants)for crop biomass estimation.This result will be helpful to promote the use of non-destructive remote sensing methods to determine the crop LAI and biomass status,which may bring more efficient crop production and management.
基金supported by the Major Science and Technology Project of Guizhou Province([2024]004)Science and Technology Program Project of Guizhou Provincial Tobacco Company of CNTC(2024520000240087).
文摘Accurate recognition of maize seedlings on the plot scale under the disturbance of weeds is crucial for early seedling replenishment and weed removal.Currently,UAV-based maize seedling recognition depends primarily on RGB images.The main purpose of this study is to compare the performances of multispectral images and RGB images of unmanned aerial vehicle(UAV)on maize seeding recognition using deep learning algorithms.Additionally,we aim to assess the disturbance of different weed coverage on the recognition of maize seeding.Firstly,principal component analysis was used in multispectral image transformation.Secondly,by introducing the CARAFE sampling operator and a small target detection layer(SLAY),we extracted the contextual information of each pixel to retain weak features in the maize seedling image.Thirdly,the global attention mechanism(GAM)was employed to capture the features of maize seedlings using the dual attention mechanism of spatial and channel information.The CGS-YOLO algorithm was constructed and formed.Finally,we compared the performance of the improved algorithm with a series of deep learning algorithms,including YOLO v3,v5,v6 and v8.The results show that after PCA transformation,the recognition mAP of maize seedlings reaches 82.6%,representing 3.1 percentage points improvement compared to RGB images.Compared with YOLOv8,YOLOv6,YOLOv5,and YOLOv3,the CGS-YOLO algorithm has improved mAP by 3.8,4.2,4.5 and 6.6 percentage points,respectively.With the increase of weed coverage,the recognition effect of maize seedlings gradually decreased.When weed coverage was more than 70%,the mAP difference becomes significant,but CGS-YOLO still maintains a recognition mAP of 72%.Therefore,in maize seedings recognition,UAV-based multispectral images perform better than RGB images.The application of CGS-YOLO deep learning algorithm with UAV multi-spectral images proves beneficial in the recognition of maize seedlings under weed disturbance.
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Postgraduate Research&Practice Innovation Program of Jiangsu Province under contract Nos KYCX23_1067 and KYCX25_1231the National Natural Science Foundation of China under contract No.42476157。
文摘Spartina alterniflora's robust reproductive capacity has enabled it to spread rapidly, posing a serious threat to native ecosystems in China. Therefore, accurate quantification of Spartina alterniflora aboveground biomass at a fine scale is crucial for understanding its growth dynamics and managing its invasion. This study focuses on the coastal wetlands of central Jiangsu Province, China, utilizing multispectral unmanned aerial vehicle(UAV) data to map the distribution of Spartina alterniflora. Object-based image analysis(OBIA) combined with support vector machines(SVM) was employed for classification. Additionally, multiple regression models, including univariate, band-based, vegetation index(VI)-based, and multivariate linear regression models integrating both band and VI data, were developed to estimate biomass:(1) the Bands + VIs multiple linear regression model based on fresh weight exhibited the highest estimation accuracy;(2) the optimal model achieved R^(2) values of 0.81 and 0.82 at Dafeng and Tiaozini Nature Reserve,with RMSE values of 591.78 g/m^(2) and 337.62 g/m^(2), and MAE values of 576.82 g/m^(2) and 287.71 g/m^(2), respectively;and(3) the aboveground biomass of Spartina alterniflora primarily ranged from 994.60 g/m^(2) to 5 351.48 g/m^(2) at Dafeng and from 796.05 g/m^(2) to 1 994.02 g/m^(2) in Tiaozini Nature Reserve. These findings highlight the effectiveness of multispectral UAV technology for accurately estimating Spartina alterniflora biomass, providing a robust methodology for wetland vegetation monitoring and invasive species management.
基金financially supported by Top Talents Program for One Case One Discussion of Shandong Province,Natural Science Foundation of Shandong Province(Grant No.ZR2021 MD091)China Agriculture Research System(CARS-15-22)Academy of Ecological Unmanned Farm(Grant No.2019 ZBXC200).
文摘Leaf Area of Index(LAI)refers to half of the total leaf area of all crops per unit area.It is an important index to represent the photosynthetic capacity and biomass of crops.To obtain LAI conditions of summer maize in different growth stages quickly and accurately,further guiding field fertilization and irrigation.The Unmanned aerial vehicles(UAV)multispectral data,growing degree days,and canopy height model of 2020-2021 summer maize were used to carry out LAI inversion.The vegetation index was constructed by the ground hyperspectral data and multispectral data of the same range of bands.The correlation analysis was conducted to verify the accuracy of the multispectral data.To include many bands as possible,four vegetation indices which included R,G,B,and NIR bands were selected in this study to test the spectral accuracy.There were nine vegetation indices calculated with UAV multispectral data which were based on the red band and the near-infrared band.Through correlation analysis of LAI and the vegetation index,vegetation indices with a higher correlation to LAI were selected to construct the LAI inversion model.In addition,the Canopy Height Model(CHM)and Growing degree days(GDD)of summer maize were also calculated to build the LAI inversion model.The LAI inversion of summer maize was carried out based on multi-growth stages by using the general linear regression model(GLR),Multivariate nonlinear regression model(MNR),and the partial least squares regression(PLSR)models.R²and RMSE were used to assess the accuracy of the model.The results show that the correlation between UAV multispectral data and hyperspectral data was greater than 0.64,which was significant.The Wide Dynamic Range Vegetation Index(WDRVI),Normalized Difference Vegetation Index(NDVI),Ratio Vegetation Index(RVI),Plant Biochemical Index(PBI),Optimized Soil-Adjusted Vegetation Index(OSAVI),CHM and GDD have a higher correlation with LAI.By comparing the models constructed by the three methods,it was found that the PLSR has the best inversion effect.It was based on OSAVI,GDD,RVI,PBI,CHM,NDVI,and WDRVI,with the training model’s R²being 0.8663,the testing model’s R²being 0.7102,RMSE was 1.1755.This study showed that the LAI inversion model based on UAV multispectral vegetation index,GDD,and CHM improves the accuracy of LAI inversion effectively.That means the growing degree days and crop population structure change have influenced the change of maize LAI certainly,and this method can provide decision support for maize growth monitoring and field fertilization.
