scholarly journals UAV Remote Sensing Estimation of Rice Yield Based on Adaptive Spectral Endmembers and Bilinear Mixing Model

2021 ◽  
Vol 13 (11) ◽  
pp. 2190
Author(s):  
Ningge Yuan ◽  
Yan Gong ◽  
Shenghui Fang ◽  
Yating Liu ◽  
Bo Duan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rice Yield ◽  
Estimation Error ◽  
Structural Difference ◽  
Mixing Model ◽  
Accurate Estimation ◽  
Endmember Extraction ◽  
Yield Estimation ◽  
Estimation Model ◽  
Heading Stage

The accurate estimation of rice yield using remote sensing (RS) technology is crucially important for agricultural decision-making. The rice yield estimation model based on the vegetation index (VI) is commonly used when working with RS methods, however, it is affected by irrelevant organs and background especially at heading stage. The spectral mixture analysis (SMA) can quantitatively obtain the abundance information and mitigate the impacts. Furthermore, according to the spectral variability and information complexity caused by the rice cropping system and canopy characteristics of reflection and scattering, in this study, the multi-endmember extraction by the pure pixel index (PPI) and the nonlinear unmixing method based on the bandwise generalized bilinear mixing model (NU-BGBM) were applied for SMA, and the VIE (VIs recalculated from endmember spectra) was integrated with abundance data to establish the yield estimation model at heading stage. In two paddy fields of different cultivation settings, multispectral images were collected by an unmanned aerial vehicle (UAV) at booting and heading stage. The correlation of several widely-used VIs and rice yield was tested and weaker at heading stage. In order to improve the yield estimation accuracy of rice at heading stage, the VIE and foreground abundances from SMA were combined to develop a linear yield estimation model. The results showed that VIE incorporated with abundances exhibited a better estimation ability than VI alone or the product of VI and abundances. In addition, when the structural difference of plants was obvious, the addition of the product of VIF (VIs recalculated from bilinear endmember spectra) and the corresponding bilinear abundances to the original product of VIE and abundances, enhanced model reliability. VIs using the near-infrared bands improved more significantly with the estimation error below 8.1%. This study verified the validation of the targeted SMA strategy while estimating crop yield by remotely sensed VI, especially for objects with obvious different spectra and complex structures.

scholarly journals Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Shanjun Luo ◽  
Yingbin He ◽  
Qian Li ◽  
Weihua Jiao ◽  
Yaqiu Zhu ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Growth Stage ◽  
Estimation Error ◽  
Potato Yield ◽  
Hyperspectral Data ◽  
Single Stage ◽  
Accurate Estimation ◽  
Yield Estimation ◽  
Area Index ◽  
Red Edge

Abstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

Analytical Model for High–Level Area Estimation of FPGA Design

2016 ◽  
Vol 7 (2) ◽  
pp. 35-44
Author(s):  
Rachna Singh ◽  
Arvind Rajawat
Keyword(s):  
Early Stage ◽  
Estimation Error ◽  
Research Work ◽  
Performance Estimation ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Fpga Design ◽  
Estimation Model ◽  
Area Estimation ◽  
High Level

FPGAs have been used as a target platform because they have increasingly interesting in system design and due to the rapid technological progress ever larger devices are commercially affordable. These trends make FPGAs an alternative in application areas where extensive data processing plays an important role. Consequently, the desire emerges for early performance estimation in order to quantify the FPGA approach. A mathematical model has been presented that estimates the maximum number of LUTs consumed by the hardware synthesized for different FPGAs using LLVM.. The motivation behind this research work is to design an area modeling approach for FPGA based implementation at an early stage of design. The equation based area estimation model permits immediate and accurate estimation of resources. Two important criteria used to judge the quality of the results were estimation accuracy and runtime. Experimental results show that estimation error is in the range of 1.33% to 7.26% for Spartan 3E, 1.6% to 5.63% for Virtex-2pro and 2.3% to 6.02% for Virtex-5.

Boro Rice Yield Estimation Model Using Modis Ndvi Data for Bangladesh

2019 ◽  
Author(s):  
Md. Samiul Alam ◽  
KAZI KALPOMA ◽  
Md. Sanaul Karim ◽  
Abdullah Al Sefat ◽  
Jun-ichi Kudoh
Keyword(s):  
Rice Yield ◽  
Yield Estimation ◽  
Estimation Model ◽  
Modis Ndvi ◽  
Boro Rice

scholarly journals Quantifying the uncertainty in estimates of surface–atmosphere fluxes through joint evaluation of the SEBS and SCOPE models

2013 ◽  
Vol 17 (4) ◽  
pp. 1561-1573 ◽  
Author(s):  
J. Timmermans ◽  
Z. Su ◽  
C. van der Tol ◽  
A. Verhoef ◽  
W. Verhoef
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Energy Balance ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Estimation Error ◽  
Accurate Estimation ◽  
Area Index ◽  
Ground Heat Flux ◽  
Scope Model

Abstract. Accurate estimation of global evapotranspiration is considered to be of great importance due to its key role in the terrestrial and atmospheric water budget. Global estimation of evapotranspiration on the basis of observational data can only be achieved by using remote sensing. Several algorithms have been developed that are capable of estimating the daily evapotranspiration from remote sensing data. Evaluation of remote sensing algorithms in general is problematic because of differences in spatial and temporal resolutions between remote sensing observations and field measurements. This problem can be solved in part by using soil-vegetation-atmosphere transfer (SVAT) models, because on the one hand these models provide evapotranspiration estimations also under cloudy conditions and on the other hand can scale between different temporal resolutions. In this paper, the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model is used for the evaluation of the Surface Energy Balance System (SEBS) model. The calibrated SCOPE model was employed to simulate remote sensing observations and to act as a validation tool. The advantages of the SCOPE model in this validation are (a) the temporal continuity of the data, and (b) the possibility of comparing different components of the energy balance. The SCOPE model was run using data from a whole growth season of a maize crop. It is shown that the original SEBS algorithm produces large uncertainties in the turbulent flux estimations caused by parameterizations of the ground heat flux and sensible heat flux. In the original SEBS formulation the fractional vegetation cover is used to calculate the ground heat flux. As this variable saturates very fast for increasing leaf area index (LAI), the ground heat flux is underestimated. It is shown that a parameterization based on LAI reduces the estimation error over the season from RMSE = 25 W m−2 to RMSE = 18 W m−2. In the original SEBS formulation the roughness height for heat is only valid for short vegetation. An improved parameterization was implemented in the SEBS algorithm for tall vegetation. This improved the correlation between the latent heat flux predicted by the SEBS and the SCOPE algorithm from −0.05 to 0.69, and led to a decrease in difference from 123 to 94 W m−2 for the latent heat flux, with SEBS latent heat being consistently lower than the SCOPE reference. Lastly the diurnal stability of the evaporative fraction was investigated.

Cotton Yield Estimation from UAV-Based Plant Height

2019 ◽  
Vol 62 (2) ◽  
pp. 393-404 ◽  
Author(s):  
Aijing Feng ◽  
Meina Zhang ◽  
Kenneth A. Sudduth ◽  
Earl D. Vories ◽  
Jianfeng Zhou
Keyword(s):  
Remote Sensing ◽  
Plant Height ◽  
Crop Yield ◽  
Low Cost ◽  
Reference Points ◽  
Cotton Field ◽  
Accurate Estimation ◽  
Cotton Yield ◽  
Yield Estimation ◽  
Sensing System

Abstract. Accurate estimation of crop yield before harvest, especially in early growth stages, is important for farmers and researchers to optimize field management and evaluate crop performance. However, existing in-field methods for estimating crop yield are not efficient. The goal of this research was to evaluate the performance of a UAV-based remote sensing system with a low-cost RGB camera to estimate cotton yield based on plant height. The UAV system acquired images at 50 m above ground level over a cotton field at the first flower growth stage. Waypoints and flight speed were selected to allow >70% image overlap in both forward and side directions. Images were processed to develop a geo-referenced orthomosaic image and a digital elevation model (DEM) of the field that was used to extract plant height by calculating the difference in elevation between the crop canopy and bare soil surface. Twelve ground reference points with known height were deployed in the field to validate the UAV-based height measurement. Geo-referenced yield data were aligned to the plant height map based on GPS and image features. Correlation analysis between yield and plant height was conducted row-by-row with and without row registration. Pearson correlation coefficients between yield and plant height with row registration for all individual rows were in the range of 0.66 to 0.96 and were higher than those without row registration (0.54 to 0.95). A linear regression model using plant height was able to estimate yield with root mean square error of 550 kg ha-1 and mean absolute error of 420 kg ha-1. Locations with low yield were analyzed to identify the potential reasons, and it was found that water stress and coarse soil texture, as indicated by low soil apparent electricity conductivity (ECa), might contribute to the low yield. The findings indicate that the UAV-based remote sensing system equipped with a low-cost digital camera was potentially able to monitor plant growth status and estimate cotton yield with acceptable errors. Keywords: Cotton, Geo-registration, Plant height, UAV-based remote sensing, Yield estimation.

scholarly journals Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

2020 ◽  
Author(s):  
Shanjun Luo ◽  
Yingbin He ◽  
Qian Li ◽  
Weihua Jiao ◽  
Yaqiu Zhu ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Growth Stage ◽  
Estimation Error ◽  
Regional Scale ◽  
Potato Yield ◽  
Single Stage ◽  
Accurate Estimation ◽  
Yield Estimation ◽  
Area Index ◽  
Red Edge

Abstract Background: The accurate estimation of potato yield at regional scale is crucial for food security, precision agriculture and agricultural sustainable development. Methods: In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results: The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCI red edge ) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R 2 value of 0.8333, and the estimation error about 8%. Conclusion: This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

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