Search Results (5)

Timely and accurate monitoring of crop nitrogen (N) status is essential for precision agriculture. UAV-based hyperspectral remote sensing offers high-resolution data for estimating plant nitrogen concentration (PNC), but its cost and complexity limit large-scale application. This study compares the performance of UAV hyperspectral data (S185 sensor) with simulated multispectral data from DJI Phantom 4 Multispectral (P4M), PlanetScope (PS), and Sentinel-2A (S2) in estimating winter wheat PNC. Spectral data were collected across six growth stages over two seasons and resampled to match the spectral characteristics of the three multispectral sensors. Three variable selection strategies (one-dimensional (1D) spectral reflectance, optimized two-dimensional (2D), and three-dimensional (3D) spectral indices) were combined with Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLSR) to build PNC prediction models. Results showed that, while hyperspectral data yielded slightly higher accuracy, optimized multispectral indices, particularly from PS and S2, achieved comparable performance. Among models, SVM and RFR showed consistent effectiveness across strategies. These findings highlight the potential of low-cost multispectral platforms for practical crop N monitoring. Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms. Full article

The quality of the image data and the potential to invert crop growth parameters are essential for effectively using unmanned aerial vehicle (UAV)-based sensor systems in precision agriculture (PA). However, the existing research falls short in providing a comprehensive examination of sensor data quality and the inversion potential of crop growth parameters, and there is still ambiguity regarding how the quality of data affects the inversion potential. Therefore, this study explored the application potential of RGB and multispectral (MS) images acquired from three lightweight UAV platforms in the realm of PA: the DJI Mavic 2 Pro (M2P), Phantom 4 Multispectral (P4M), and Mavic 3 Multispectral (M3M). The reliability of pixel-scale data quality was evaluated based on image quality assessment metrics, and three winter wheat growth parameters, above-ground biomass (AGB), plant nitrogen content (PNC) and soil and plant analysis development (SPAD), were inverted using machine learning models based on multi-source image features at the plot scale. The results indicated that the RGB image quality from the M3M outperformed that of the M2P, while the MS image quality was marginally superior to that of the P4M. Nevertheless, these advantages in pixel-scale data quality did not improve inversion accuracy for crop parameters at the plot scale. Spectral features (SFs) derived from the P4M-based MS sensor demonstrated significant advantages in AGB inversion (R² = 0.86, rRMSE = 27.47%), while SFs derived from the M2P-based RGB camera exhibited the best performance in SPAD inversion (R² = 0.60, rRMSE = 7.67%). Additionally, combining spectral and textural features derived from the P4M-based MS sensor yielded the highest accuracy in PNC inversion (R² = 0.82, rRMSE = 14.62%). This study clarified the data quality of three prevalent UAV mounted sensor systems in PA and their influence on parameter inversion potential, offering guidance for selecting appropriate sensors and monitoring key crop growth parameters. Full article

This study tested the potential of parametric and nonparametric regression modeling utilizing multispectral data from two different unoccupied aerial vehicles (UAVs) as a tool for the prediction of and indirect selection of grain yield (GY) in barley breeding experiments. The coefficient of determination (R²) of the nonparametric models for GY prediction ranged between 0.33 and 0.61 depending on the UAV and flight date, where the highest value was achieved with the DJI Phantom 4 Multispectral (P4M) image from 26 May (milk ripening). The parametric models performed worse than the nonparametric ones for GY prediction. Independent of the retrieval method and UAV, GY retrieval was more accurate in milk ripening than dough ripening. The leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fraction vegetation cover (fCover), and leaf chlorophyll content (LCC) were modeled at milk ripening using nonparametric models with the P4M images. A significant effect of the genotype was found for the estimated biophysical variables, which was referred to as remotely sensed phenotypic traits (RSPTs). Measured GY heritability was lower, with a few exceptions, compared to the RSPTs, indicating that GY was more environmentally influenced than the RSPTs. The moderate to strong genetic correlation of the RSPTs to GY in the present study indicated their potential utility as an indirect selection approach to identify high-yield genotypes of winter barley. Full article

In recent years, the use of unmanned aerial vehicles (UAVs) has received increasing attention in remote sensing, vegetation monitoring, vegetation index (VI) mapping, precision agriculture, etc. It has many advantages, such as high spatial resolution, instant information acquisition, convenient operation, high maneuverability, freedom from cloud interference, and low cost. Nowadays, different types of UAV-based multispectral minisensors are used to obtain either surface reflectance or digital number (DN) values. Both the reflectance and DN values can be used to calculate VIs. The consistency and accuracy of spectral data and VIs obtained from these sensors have important application value. In this research, we analyzed the earth observation capabilities of the Parrot Sequoia (Sequoia) and DJI Phantom 4 Multispectral (P4M) sensors using different combinations of correlation coefficients and accuracy assessments. The research method was mainly focused on three aspects: (1) consistency of spectral values, (2) consistency of VI products, and (3) accuracy of normalized difference vegetation index (NDVI). UAV images in different resolutions were collected using these sensors, and ground points with reflectance values were recorded using an Analytical Spectral Devices handheld spectroradiometer (ASD). The average spectral values and VIs of those sensors were compared using different regions of interest (ROIs). Similarly, the NDVI products of those sensors were compared with ground point NDVI (ASD-NDVI). The results show that Sequoia and P4M are highly correlated in the green, red, red edge, and near-infrared bands (correlation coefficient (R²) > 0.90). The results also show that Sequoia and P4M are highly correlated in different VIs; among them, NDVI has the highest correlation (R² > 0.98). In comparison with ground point NDVI (ASD-NDVI), the NDVI products obtained by both of these sensors have good accuracy (Sequoia: root-mean-square error (RMSE) < 0.07; P4M: RMSE < 0.09). This shows that the performance of different sensors can be evaluated from the consistency of spectral values, consistency of VI products, and accuracy of VIs. It is also shown that different UAV multispectral minisensors can have similar performances even though they have different spectral response functions. The findings of this study could be a good framework for analyzing the interoperability of different sensors for vegetation change analysis. Full article

Crop monitoring and appropriate agricultural management practices of elite germplasm will enhance bioenergy’s efficiency. Unmanned aerial systems (UAS) may be a useful tool for this purpose. The objective of this study was to assess the use of UAS with true color and multispectral imagery to predict the yield and total cellulosic content (TCC) of newly created energy cane germplasm. A trial was established in the growing season of 2016 at the Texas A&M AgriLife Research Center in Weslaco, Texas, where 15 energy cane elite lines and three checks were grown on experimental plots, arranged in a complete block design and replicated four times. Four flights were executed at different growth stages in 2018, at the first ratoon crop, using two multi-rotor UAS: the DJI Phantom 4 Pro equipped with RGB camera and the DJI Matrice 100, equipped with multispectral sensor (SlantRange 3p). Canopy cover, canopy height, NDVI (Normalized Difference Vegetation Index), and ExG (Excess Green Index) were extracted from the images and used to perform a stepwise regression to obtain the yield and TCC models. The results showed a good agreement between the predicted and the measured yields (R² = 0.88); however, a low coefficient of determination was found between the predicted and the observed TCC (R² = 0.30). This study demonstrated the potential application of UAS to estimate energy cane yield with high accuracy, enabling plant breeders to phenotype larger populations and make selections with higher confidence. Full article