Abstract
The awareness of spatial and temporal variations in site-specific crop parameters, such as aboveground biomass (total dry weight: (TDW), plant length (PL) and leaf area index (LAI), help in formulating appropriate management decisions. However, conventional monitoring methods rely on time-consuming manual field operations. In this study, the feasibility of using an unmanned aerial vehicle (UAV)-based remote sensing approach for monitoring growth in rice was evaluated using a digital surface model (DSM). Approximately 160 images of paddy fields were captured during each UAV survey campaign over two vegetation seasons. The canopy surface model (CSM) was developed based on the differences observed between each DSM and the first DSM after transplanting. Mean canopy height (CH) was used as a variable for the estimation models of LAI and TDW. The mean CSM of the mesh covering several hills was sufficient to explain the PL (R2 = 0.947). TDW and LAI prediction accuracy of the model were high (relative RMSE of 20.8% and 28.7%, and RMSE of 0.76 m2 m−2 and 141.4 g m−2, respectively) in the rice varieties studied (R2 = 0.937 (Basmati370), 0.837 (Nipponbare and IR64) for TDW, and 0.894 (Basmati370), 0.866 (Nipponbare and IR64) for LAI). The results of this study support the assertion of the benefits of DSM-derived CH for predicting biomass development. In addition, LAI and TDW could be estimated temporally and spatially using the UAV-based CSM, which is not easily affected by weather conditions.
Highlights
Obtaining knowledge about the dynamic of plant biomass is an essential part of precision agriculture, as such information aids in the management decision making, risk assessment and the design of labor-saving and efficient technologies and that can compensate for the physical deficiency in the agricultural labor force [1,2,3]
This study analyzed the potential of unmanned aerial vehicles (UAVs)-based Digital Surface Model (DSM) for monitoring crop growth, with an emphasis on developing estimation models to predict the growth of three rice varieties grown under varying environmental conditions
The results demonstrated the feasibility of using canopy surface model (CSM)-derived canopy height to predict biomass growth in rice cultivars
Summary
Obtaining knowledge about the dynamic of plant biomass is an essential part of precision agriculture, as such information aids in the management decision making, risk assessment and the design of labor-saving and efficient technologies and that can compensate for the physical deficiency in the agricultural labor force [1,2,3]. Aboveground biomass (total dry weight: TDW) estimation has been widely explored due to its direct relation to crop yield commonly by farmer’s expert knowledge and destructive sampling which are not timely and labor efficient. The potential of satellite-based remote sensing for crop management has been widely studied [6]. Information on sufficient resolution and apt revisit frequency for precisely mapping smallholder farm units has been a challenge until the influx of unmanned aerial vehicles (UAVs) [9]. Recent studies based on vegetation indices (VIs) extracted from the images captured by relatively inexpensive
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