Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands

Abstract

The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV)/drones to map both pasture quantity as biomass yield and pasture quality as the proportions of key pasture nutrients, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed machine learning based predictive models of both pasture measures. UAV-based structure from motion photogrammetry provided a measure of yield from overlapping high resolution visible colour imagery. Pasture nutrient composition was estimated from the spectral signatures of visible near infrared hyperspectral UAV sensing. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients (R2) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of yield and on average indicated an error of 0.8 t ha−1 in grasslands and 1.3 t ha−1 in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R2 = 0.9 and CP R2 = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale; however, further research is needed, in particular further field sampling of both yield and nutrient elements across such a diverse landscape, with the potential to scale up to a satellite platform for broader scale monitoring.

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

“…After weighing, the sample is dried in an oven to determine the amount of dry biomass [67]. Cut and dry is the most common reference data collection method in biomass estimation studies, and it has been used in various recent studies [69][70][71][72][73]. The method provides objective reference data, but the cutting phase can include lot of variation.…”

“…Typically, these studies quantified either the symptoms caused by the pests and/or the number of pests, with two exceptions [80]. In the case of fall armyworm (Spodoptera frugiperda) in wheat fields [71], the reference data were reported to consist of the reported outbreaks by farmer. UAV-based RGB imaging was seen as having the potential to predict the movement and damage cause by this pest [81].…”

Determining Antecedent Pasture State for Climate Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite Based Deep-Learning Method for Estimating Time-Integrated Measures of Pasture Yield

“…Native pastures account for the majority of the feed-base and the climate is highly variable. The antecedent climatic conditions, including knowledge of the existing pasture growth state, is important in the matching of livestock numbers to the initial and ongoing available pasture resource, requiring continual observation and fine-tuning of herbivore density [1,3].This estimation of stocking rate depends primarily on the assessment of the available and on-going pasture yield and as described by [4] and others [5,6] was traditionally carried out through manual field based sampling and simulation modelling. New techniques utilising both airborne remote sensing, including Remotely Piloted Aircraft Systems (RPAS) and space-borne satellite remote sensing are increasingly used to estimate pasture yield.…”

“…New techniques utilising both airborne remote sensing, including Remotely Piloted Aircraft Systems (RPAS) and space-borne satellite remote sensing are increasingly used to estimate pasture yield. Recent studies by these and other authors have applied both techniques either in other countries or in isolation to one another [4,6,7].…”

“…Coupling both RPAS and satellite remote sensing techniques to estimate plant pasture biomass provides a means of a rapid and accurate collection of calibration and validation data, to be then applied across the landscape at the satellite image scale in a more temporal and spatially explicit manner. [4] have developed an RPAS based method to estimate pasture biomass at the field site scale for use in further scaling to satellite remotely sensed imagery. Techniques developed by [7] have demonstrated scaling RPAS derived imagery up to the satellite image, in a method that aims to estimate plant pasture production as a product of Absorbed Photo-synthetically Active Radiation (APAR).…”