Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

Abstract

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.

An Improved LAI Estimation Method Incorporating with Growth Characteristics of Field-Grown Wheat

“…Taking crop canopy height as one of the characteristics of LAI estimation will effectively improve the accuracy of LAI estimation, and previous studies have also reached similar conclusions [42][43][44]. In our method, the canopy height and the vegetation index were combined in a multiplicative manner, and the effectiveness of this manner has been proved in previous studies on monitoring the growth of rapeseed [26], which can effectively avoid the problem of weight selection. Ensuring the accuracy of crop height information extraction is a key step before applying height information to the features of crop LAI estimation.…”

“…In the Equation ( 3), VI Rmax is a vegetation index with the highest correlation with LAI. The VI Rmax and CHM were combined in the form of multiplication (VI Rmax * CHM) to establish LAI regression models [26].…”

“…In recent years, research on monitoring crop growth status by combining spectral information and crop height has gradually increased. Studies have found that the combination of vegetation index and canopy height can effectively improve the estimation accuracy of LAI [26], biomass [16,27,28], forest volume [29] and crop yield [30]. Other studies showed that vegetation index combined with canopy coverage can also be used to estimate LAI [31,32].…”

Rapid Detection of Wheat Ears in Orthophotos From Unmanned Aerial Vehicles in Fields Based on YOLOX

“…The best performance on the P2 dataset with an R 2 of 0.9249 and an RMSE of 0.6583. And the model performed poorly on the H2 and H3 datasets due to the effect of the UAV flight height on the image GSD, which resulted in the wheat ears occupying too few pixels in the image; the features were difficult to be captured by the network, which also verifies that low GSD and small targets are difficult to identify with deep networks ( Zhang et al, 2020 ).…”

Catalyzing Holistic Agriculture Innovation Through Industrial Biotechnology

“…161,163,164 Unmanned aerial drones are capable of offering centimeter resolution. 166,[174][175][176][177] Temporal resolution is also important for monitoring and tracking crop development, and for different satellite systems temporal resolution can range from 18 days to 12 hours. 161,164 The cost and ease of deploying an unmanned aerial drone, with advanced sensors, allows for high spatial resolution and unprecedented temporal frequency.…”