Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis

Gong, Yi; Duan, Bo; Fang, Shenghui; Zhu, Rong; Wu, Xianting; Ma, Yi; Yi, Peng

doi:10.1186/s13007-018-0338-z

Cited by 67 publications

(61 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These bands were selected since they are commonly used for estimating vegetation parameters [41][42][43]. The cameras were co-registered in the laboratory before flight using a camera distortion correction model so that the corresponding pixels of each lens were spatially overlapped in the same focal plane [44,45]. A gimbal stable platform was equipped on the UAV to ensure a nadir view, which minimized the influence of UAV fluctuations during image acquisition.…”

Section: Uav Remote Sensing Datamentioning

confidence: 99%

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

Jiang

Fang

et al. 2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Accurate estimation of above ground biomass (AGB) is very important for crop growth monitoring. The objective of this study was to estimate rice biomass by utilizing structural and meteorological features with widely used spectral features. Structural features were derived from the triangulated irregular network (TIN), which was directly built from structure from motion (SfM) point clouds. Growing degree days (GDD) was used as the meteorological feature. Three models were used to estimate rice AGB, including the simple linear regression (SLR) model, simple exponential regression (SER) model, and machine learning model (random forest). Compared to models that do not use structural and meteorological features (NDRE, R2 = 0.64, RMSE = 286.79 g/m2, MAE = 236.49 g/m2), models that include such features obtained better estimation accuracy (NDRE*Hcv/GDD, R2 = 0.86, RMSE = 178.37 g/m2, MAE = 127.34 g/m2). This study suggests that the estimation accuracy of rice biomass can benefit from the utilization of structural and meteorological features.

show abstract

Section: Uav Remote Sensing Datamentioning

confidence: 99%

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

Jiang

Fang

et al. 2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Zhu et al [19] used a UAV remote sensing platform equipped with a multi-spectral camera to obtain the image data of wheat at the jointing stage, heading stage, and filling stage, and constructed nine linear models of different vegetation indexes and measured yields using the least square method. Gong et al [20] used a multi-spectral camera mounted on a multi-rotor UAV to obtain images of the early flowering period of rapeseed and used a normalized vegetation index to predict yield. Yu et al [21] developed a dual-camera high-throughput phenotype (HTP) platform on a UAV that collected multispectral data from multiple growth periods to improve the accuracy of soybean yield estimates.…”

Section: Introductionmentioning

confidence: 99%

Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle

et al. 2020

View full text Add to dashboard Cite

Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the results of the UAV images, the LAI, LDM, and yield data were obtained by destructive sampling. We extracted the wheat canopy reflectance and selected the best vegetation index for monitoring growth and predicting yield. Simple linear regression (LR), multiple linear regression (MLR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural network (ANN), and random forest (RF) modeling methods were used to construct a model for wheat yield estimation. The results show that the multi-spectral camera mounted on the multi-rotor UAV has a broad application prospect in crop growth index monitoring and yield estimation. The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM. Machine learning methods (i.e., PLSR, ANN, and RF) performed better for predicting wheat yield. The RF model constructed by normalized difference vegetation index (NDVI) at the jointing stage, heading stage, flowering stage, and filling stage was the optimal wheat yield estimation model in this study, with an R2 of 0.78 and relative root mean square error (RRMSE) of 0.1030. The results provide a theoretical basis for monitoring crop growth with a multi-rotor UAV platform and explore a technical method for improving the precision of yield estimation.

show abstract

“…Physical models are highly mechanical and can simulate the growth of crops, however the simulation requires a large number of input parameters, such as canopy physiological and biochemical parameters, soil data, weather data, and crop variety information. For example, in a previous study, soil data, the content of different components in crop leaves, and a vegetation index were used evaluate rapeseed yield using a physical model [25]. However, the data which are required as input to physical models are often difficult to obtain, which limits the application of physical models.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images

Tao

Feng

et al. 2020

Sensors

View full text Add to dashboard Cite

Crop yield is related to national food security and economic performance, and it is therefore important to estimate this parameter quickly and accurately. In this work, we estimate the yield of winter wheat using the spectral indices (SIs), ground-measured plant height (H), and the plant height extracted from UAV-based hyperspectral images (HCSM) using three regression techniques, namely partial least squares regression (PLSR), an artificial neural network (ANN), and Random Forest (RF). The SIs, H, and HCSM were used as input values, and then the PLSR, ANN, and RF were trained using regression techniques. The three different regression techniques were used for modeling and verification to test the stability of the yield estimation. The results showed that: (1) HCSM is strongly correlated with H (R2 = 0.97); (2) of the regression techniques, the best yield prediction was obtained using PLSR, followed closely by ANN, while RF had the worst prediction performance; and (3) the best prediction results were obtained using PLSR and training using a combination of the SIs and HCSM as inputs (R2 = 0.77, RMSE = 648.90 kg/ha, NRMSE = 10.63%). Therefore, it can be concluded that PLSR allows the accurate estimation of crop yield from hyperspectral remote sensing data, and the combination of the SIs and HCSM allows the most accurate yield estimation. The results of this study indicate that the crop plant height extracted from UAV-based hyperspectral measurements can improve yield estimation, and that the comparative analysis of PLSR, ANN, and RF regression techniques can provide a reference for agricultural management.

show abstract

Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis

Cited by 67 publications

References 72 publications

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle

Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images

Contact Info

Product

Resources

About