Abstract
The spatial and temporal variability of crop parameters are fundamental in precision agriculture. Remote sensing of crop canopy can provide important indications on the growth variability and help understand the complex factors influencing crop yield. Plant biomass is considered an important parameter for crop management and yield estimation, especially for grassland and cover crops. A recent approach introduced to model crop biomass consists in the use of RGB (red, green, blue) stereo images acquired from unmanned aerial vehicles (UAV) coupled with photogrammetric softwares to predict biomass through plant height (PHT) information. In this study, we generated prediction models for fresh (FBM) and dry biomass (DBM) of black oat crop based on multi-temporal UAV RGB imaging. Flight missions were carried during the growing season to obtain crop surface models (CSMs), with an additional flight before sowing to generate a digital terrain model (DTM). During each mission, 30 plots with a size of 0.25 m² were distributed across the field to carry ground measurements of PHT and biomass. Furthermore, estimation models were established based on PHT derived from CSMs and field measurements, which were later used to build prediction maps of FBM and DBM. The study demonstrates that UAV RGB imaging can precisely estimate canopy height (R2 = 0.68–0.92, RMSE = 0.019–0.037 m) during the growing period. FBM and DBM models using PHT derived from UAV imaging yielded R2 values between 0.69 and 0.94 when analyzing each mission individually, with best results during the flowering stage (R2 = 0.92–0.94). Robust models using datasets from different growth stages were built and tested using cross-validation, resulting in R2 values of 0.52 for FBM and 0.84 for DBM. Prediction maps of FBM and DBM yield were obtained using calibrated models applied to CSMs, resulting in a feasible way to illustrate the spatial and temporal variability of biomass. Altogether the results of the study demonstrate that UAV RGB imaging can be a useful tool to predict and explore the spatial and temporal variability of black oat biomass, with potential use in precision farming.
Highlights
Monitoring biophysical parameters from crop canopy throughout the growing season is essential to understand variations in crop development and its relation with environmental factors and management practices [1,2]
A similar condition was observed by Bendig et al [6] in summer barley, where the inclusion of lodging plots reduced the performance of biomass prediction models, recommending the use of average maximum PHTCSM instead of average mean PHTCSM to mitigate this effect
plant height (PHT) obtained from crop surface models (CSMs) tend to be lower in comparison to ground measurements because the top portion of the plants are represented in the CSMs, and the lower parts, like leaves and even the soil level depending on the crop structure, covering more details than ground-based PHT
Summary
Monitoring biophysical parameters from crop canopy throughout the growing season is essential to understand variations in crop development and its relation with environmental factors and management practices [1,2]. The spatial and temporal variability of biophysical parameters can be further used to increase crop productivity and farm profitability by improving the management of farm inputs following precision agriculture concepts. Black oat (Avena strigosa Schreb.) is a double-purpose crop used both as a temperate annual forage and as a cover crop under no-tillage system in South America [11]. In both cases quantifying biomass plays a key role, considered an essential parameter for effective pasture management [10]
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