Remote Estimation of Rice Yield With Unmanned Aerial Vehicle (UAV) Data and Spectral Mixture Analysis

Abstract

The accurate assessment of rice yield is crucially important for China’s food security and sustainable development. Remote sensing (RS), as an emerging technology, is expected to be useful for rice yield estimation especially at regional scales. With the development of unmanned aerial vehicles (UAVs), a novel approach for RS has been provided, and it is possible to acquire high spatio-temporal resolution imagery on a regional scale. Previous reports have shown that the predictive ability of vegetation index (VI) decreased under the influence of panicle emergence during the later stages of rice growth. In this study, a new approach which integrated UAV-based VI and abundance information obtained from spectral mixture analysis (SMA) was established to improve the estimation accuracy of rice yield at heading stage. The six-band image of all studied rice plots was collected by a camera system mounted on an UAV at booting stage and heading stage respectively. And the corresponding ground measured data was also acquired at the same time. The relationship of several widely-used VIs and Rice Yield was tested at these two stages and a relatively weaker correlation between VI and yield was found at heading stage. In order to improve the estimation accuracy of rice yield at heading stage, the plot-level abundance of panicle, leaf and soil, indicating the fraction of different components within the plot, was derived from SMA on the six-band image and in situ endmember spectra collected for different components. The results showed that VI incorporated with abundance information exhibited a better predictive ability for yield than VI alone. And the product of VI and the difference of leaf abundance and panicle abundance was the most accurate index to reliably estimate yield for rice under different nitrogen treatments at heading stage with the coefficient of determination reaching 0.6 and estimation error below 10%.

Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles

“…The result could be ascribed to the fact that the data were segmented into too many groups (number of groups = 36) when using CSP as random effects. Therefore, the number of points per group was very low (i.e., [12][13][14][15][16][17][18][19][20][21][22][23][24] and the modelling performance within the groups suffered from randomness and strong uncertainty. Even though the LME model (using CSP) achieved good performance over all data, this approach would lose statistical significance and simply yield the mean for the group.…”

“…For example, Devia et al used seven vegetation indices, combined with multivariable regressions to monitor agronomy parameters at different rice growth stages [15]. Duan et al integrated UAV-based vegetation indices (VIs) and abundance information obtained from spectral mixture analysis to improve the prediction accuracy of rice yield at the heading stage [16]. In addition, most UAV-based studies used multi-rotor UAVs due to their high stability, superior image quality and good controllability.…”

Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

“…High-throughput eld phenomics for aerial imaging of cassava UAV offers very attractive alternatives such as, convenient operation, high spatial and temporal resolutions with reasonable spatial coverage [57][58][59], makes it possible to document the within-microplot variability in phenotyping eld experiments [60,61]. UAV, a current and an invaluable tool for crop monitoring at large scale (e.g., [27,59,[62][63][64][65], has been proved to be useful for estimating canopy height and biomass in crops including rice [65], wheat [66] maize [30], sorghum [67] and peas [17]. However, in cassava, the UAV based high-throughput phenotyping methods need to be standardized for feasibility and accuracy in estimating various phenotyping parameters such as, biotic and abiotic stresses.…”

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

“…Unlike the single-stage results, in the OCW-based models, the accuracy of the rLAI model is higher than that of the rVI model. Because the appearance of saturation phenomenon in yield estimation using spectral index will limit the accuracy of models to some extent [25]. The LAI data is the three-dimensional (3D) information of the crop, and the limitations will be reduced.…”

“…Gong et al [24] found that NDVI is of great help to the prediction of rapeseed yield using unmanned aerial vehicle (UAV) imagery. Moreover, VI also contributes signi cantly to yield estimation for crops such as rice [25][26], maize [27][28] and wheat [29][30]. The simulation results of crop characteristic parameters can be obtained by constructing the linear or nonlinear empirical relationship [31] or by machine learning methods [32] like support vector machine (SVM), random forest (RF), partial least squares (PLS) and arti cial neural network (ANN) between VIs and these parameters.…”