Estimating Biomass of Black Oat Using UAV-Based RGB Imaging

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.

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

“…We found 15 research papers [1,8,19,22,23,28,34,48,50,55,[62][63][64]74,75] that used structural measurements alone and 12 papers [4,5,9,12,15,16,20,21,25,30,49,57] that used structural metrics along with spectral data to estimate biomass of vegetation (Table A1). All structural variables used by studies in this review are listed in Table 1.…”

“…Mean height [3,9,12,13,15,16,[19][20][21]23,25,28,30,34,[48][49][50]57,58,63,65,[67][68][69][74][75][76] Maximum height [1,3,4,13,28,30,34,48,57,63,65,69] Minimum height [3,28,34,48,57,63,65,69] Median height [12,21,27,48,63,65,…”

“…Height variables other than mean or maximum may better capture the variation of canopy height within a plot. Rather than mean canopy height, Acorsi et al [75] calculated the average maximum height at the plot level in an attempt to accurately capture plot-level variations in height during the lodging stage of oats, when plants start to bend over due to biomass accumulation in top-heavy reproductive organs. This study found that good to excellent accuracy was achieved when estimating fresh and dry oat biomass, although coefficients of determination varied across the growing season (R 2 = 0.69-0.94) [75].…”

“…The height and volume of herbaceous vegetation tends to change depending on the phenological stage of the plant and can vary greatly throughout a single growing season and may not always have a predictable relationship with AGB at all growth stages [34]. For example, in early to middle growth stages of many herbaceous plants, stem and leaf biomass dominate and have a close relationship with height [75,78] but the plants at early stages are small and sparse, making them difficult to detect [5]. In later growth stages when reproductive organs appear, plants accumulate biomass in those organs but do not necessarily increase in height [26]; in fact, lodging in top-heavy crops-when plants bend late in the season over due to the weight of reproductive organs-actually decreases the mean canopy height [23,66].…”

Converting a Fixed-Wing Internal Combustion Engine RPAS into an Electric Lithium-Ion Battery-Driven RPAS

“…The technology of RPASs for areas such as precision agriculture is drawing increasing attention from different sectors interested in seeking real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”