Abstract
Unmanned aerial vehicle (UAV) photogrammetry was used to monitor crop height in a flooded paddy field. Three multi-rotor UAVs were utilized to conduct flight missions in order to capture RGB (RedGreenBlue) and multispectral images, and these images were analyzed using several different models to provide the best results. Two image sets taken by two UAVs, mounted with RGB cameras of the same resolution and Global Navigation Satellite System (GNSS) receivers of different accuracies, were applied to perform photogrammetry. Two methods were then proposed for creating crop height models (CHMs), one of which was denoted as the M1 method and was based on the Digital Surface Point Cloud (DSPC) and the Digital Terrain Point Cloud (DSPT). The other was denoted as the M2 method and was based on the DSPC and a bathymetric sensor. An image set taken by another UAV mounted with a multispectral camera was used for multispectral-based photogrammetry. A Normal Differential Vegetation Index (NDVI) and a Vegetation Fraction (VF) were then extracted. A new method based on multiple linear regression (MLR) combining the NDVI, the VF, and a Soil Plant Analysis Development (SPAD) value for estimating the measured height (MH) of rice was then proposed and denoted as the M3 method. The results show that the M1 method, the UAV with a GNSS receiver with a higher accuracy, obtained more reliable estimations, while the M2 method, the UAV with a GNSS receiver of moderate accuracy, was actually slightly better. The effect on the performance of CHMs created by the M1 and M2 methods is more negligible in different plots with different treatments; however, remarkably, the more uniform the distribution of vegetation over the water surface, the better the performance. The M3 method, which was created using only a SPAD value and a canopy NDVI value, showed the highest coefficient of determination (R2) for overall MH estimation, 0.838, compared with other combinations.
Highlights
Introduction published maps and institutional affilRice is one of the staple foods of many countries and regions, so an adequate rice supply is essential [1,2]
The image error was based on the theoretical location information of images estimated by the photogrammetry, coordinate, and altitude information recorded by the Global Navigation Satellite System (GNSS) receiver during the Unmanned aerial vehicle (UAV) flight
In the M1 method, the estimated height and Measured Height (MH) based on the P4P had high linear correlations where R2 reached 0.9223 in the treatment of low density with cover crop, and the lowest R2 value was obtained in the treatment of low density without cover crop, but it still was 0.8844
Summary
Introduction published maps and institutional affilRice is one of the staple foods of many countries and regions, so an adequate rice supply is essential [1,2]. The field-based phenotype (FBP) is one of the essential components of a crop system, as it is the eventual expression of the crop’s genetic factors [4], which are crucial to improve crop production [5]. FBPs are various, including some morphological traits, for example, crop height (CH), lodging, crop canopy cover (CCC), and physiological traits such as leaf chlorophyll content (LCC). Leaf chlorophyll content is one of the essential indicators used to evaluate the growth status of crops and can be used to understand a crop’s environmental stress and the level of N content [6,7]. Crop canopy cover is another important FBP that can indicate a crop’s emergence or the senescence status of certain crops [8]. Crop height is an important indicator, and a fundamental phenotypic parameter of crops; [9] has shown the iations
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