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The tools available to farmers to manage grazed pastures and adjust forage demand to grass growth are generally rather static. Unmanned aerial systems (UASs) are interesting versatile tools that can provide relevant 3D information, such as sward height (3D structure), or even describe the physical condition of pastures through the use of spectral information. This study aimed to evaluate the potential of UAS to characterize a pasture’s sward height and above-ground biomass at a very fine spatial scale. The pasture height provided by UAS products showed good agreement (R2 = 0.62) with a reference terrestrial light detection and ranging (LiDAR) dataset. We tested the ability of UAS imagery to model pasture biomass based on three different combinations: UAS sward height, UAS sward multispectral reflectance/vegetation indices, and a combination of both UAS data types. The mixed approach combining the UAS sward height and spectral data performed the best (adj. R2 = 0.49). This approach reached a quality comparable to that of more conventional non-destructive on-field pasture biomass monitoring tools. As all of the UAS variables used in the model fitting process were extracted from spatial information (raster data), a high spatial resolution map of pasture biomass was derived based on the best fitted model. A sward height differences map was also derived from UAS-based sward height maps before and after grazing. Our results demonstrate the potential of UAS imagery as a tool for precision grazing study applications. The UAS approach to height and biomass monitoring was revealed to be a potential alternative to the widely used but time-consuming field approaches. While reaching a similar level of accuracy to the conventional field sampling approach, the UAS approach provides wall-to-wall pasture characterization through very high spatial resolution maps, opening up a new area of research for precision grazing.

Remote sensing can serve as a promising solution for monitoring spatio-temporal variability in grasslands, providing timely information about different biophysical parameters. We aimed to develop models for canopy height classification and aboveground biomass estimation in pastures of Megathyrsus maximus cv. Mombaça using machine learning techniques and images obtained from the Sentinel-2 satellite. We used different spectral bands from the Sentinel-2, which were obtained and processed entirely in the cloud computing space. Three canopy height classes were defined according to grazing management recommendations: Class 0 (< 0.45 m), Class 1 (0.45–0.80 m) and Class 2 (> 0.80 m). For modeling, the original database was divided into training data (85%) and test data (15%). To avoid dependency between our training and test datasets and ensure greater generalization capacity, we used a spatial grouping evaluation structure. The random forest algorithm was used to predict canopy height and aboveground biomass by using height and biomass field reference data obtained from 54 paddocks in Brazil between 2016 and 2018. Our results demonstrated precision, recall, and accuracy values of up to 73%, 73%, and 72%, respectively, for canopy height classification. In addition, the models showed reasonable predictive performance for aboveground fresh biomass (AFB) and dry matter concentration (DMC; R2 = 0.61 and 0.69, respectively). We conclude that the combined use of satellite imagery and machine learning techniques has potential to predict canopy height and aboveground biomass of Megathyrsus maximus cv. Mombaça. However, further studies should be conducted to improve the proposed models and develop software to implement the tool under field conditions.

An accurate estimation of biomass is needed to understand the spatio-temporal changes of forage resources in pasture ecosystems and to support grazing management decisions. A timely evaluation of biomass is challenging, as it requires efficient means such as technical sensing methods to assess numerous data and create continuous maps. In order to calibrate ultrasonic and spectral sensors, a field experiment with heterogeneous pastures continuously stocked by cows at three grazing intensities was conducted. Sensor data fusion by combining ultrasonic sward height (USH) with narrow band normalized difference spectral index (NDSI) (R2CV = 0.52) or simulated WorldView2 (WV2) (R2CV = 0.48) satellite broad bands increased the prediction accuracy significantly, compared to the exclusive use of USH or spectral measurements. Some combinations were even better than the use of the full hyperspectral information (R2CV = 0.48). Spectral regions related to plant water content were found to be of particular importance (996–1225 nm). Fusion of ultrasonic and spectral sensors is a promising approach to assess biomass even in heterogeneous pastures. However, the suggested technique may have limited usefulness in the second half of the growing season, due to an increasing abundance of senesced material.

The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the quantification of remote methods, there has been growing interest in using satellite imagery and machine learning to quantify pastures at the field scale. Here, we systematically reviewed 214 journal articles published between 1991 to 2021 to determine how vegetation indices derived from satellite imagery impacted the type and quantification of pasture indicators. We reveal that previous studies have been limited by highly spatiotemporal satellite imagery and prognostic analytics. While the number of studies on pasture classification, degradation, productivity, and management has increased exponentially over the last five years, the majority of vegetation parameters have been derived from satellite imagery using simple linear regression approaches, which, as a corollary, often result in site-specific parameterization that become spurious when extrapolated to new sites or production systems. Few studies have successfully invoked machine learning as retrievals to understand the relationship between image patterns and accurately quantify the biophysical variables, although many studies have purported to do so. Satellite imagery has contributed to the ability to quantify pasture indicators but has faced the barrier of monitoring at the paddock/field scale (20 hectares or less) due to (1) low sensor (coarse pixel) resolution, (2) infrequent satellite passes, with visibility in many locations often constrained by cloud cover, and (3) the prohibitive cost of accessing fine-resolution imagery. These issues are perhaps a reflection of historical efforts, which have been directed at the continental or global scales, rather than at the field level. Indeed, we found less than 20 studies that quantified pasture biomass at pixel resolutions of less than 50 hectares. As such, the use of remote sensing technologies by agricultural practitioners has been relatively low compared with the adoption of physical agronomic interventions (such as ‘no-till’ practices). We contend that (1) considerable opportunity for advancement may lie in fusing optical and radar imagery or hybrid imagery through the combination of optical sensors, (2) there is a greater accessibility of satellite imagery for research, teaching, and education, and (3) developers who understand the value proposition of satellite imagery to end users will collectively fast track the advancement and uptake of remote sensing applications in agriculture.

This study tackles the challenge of accurately estimating pasture biomass by integrating proximal sensing, remote sensing, and machine learning techniques. Field measurements of vegetation height collected using the PaddockTrac ultrasonic sensor were combined with vegetation indices (e.g., NDVI, MSAVI2) derived from Landsat 7 and Sentinel-2 satellite data. We applied the Boruta algorithm for feature selection to identify influential biophysical predictors and evaluated four machine learning models—Linear Regression, Decision Tree, Random Forest, and XGBoost—for biomass prediction. XGBoost consistently performed the best, achieving an R2 of 0.86, an MAE of 414 kg ha⁻1, and an RMSE of 538 kg ha⁻1 using Landsat 7 data across multiple years. Sentinel-2’s red-edge indices did not substantially improve predictions, suggesting a limited benefit from finer spectral resolutions in this homogenous pasture context. Nonetheless, these indices may offer value in more complex vegetation scenarios. The findings emphasize the effectiveness of combining detailed ground-based measurements with advanced machine learning and remote sensing data, providing a scalable and accurate approach to biomass estimation. This integrated framework provides practical insights for precision agriculture and optimized pasture management, significantly advancing efficient and sustainable rangeland monitoring.

The total biomass of a tall fescue (Festuca arundinacea var. Fletcher) pasture was assessed by using a vehicle mounted light detection and ranging (LiDAR) unit to derive canopy height and an active optical reflectance sensor to determine the spectro-optical reflectance index, normalized difference vegetation index (NDVI). In a random plot design, measurements of NDVI and pasture height were combined to estimate biomass with a root mean square error of prediction (RMSEP) equal to ±455.28 kg green dry matter (GDM)/ha, over a range of 286 kg to 3933 kg GDM/ha. The combination of NDVI and height measurements were observed to be more accurate in assessing total biomass than just the NDVI (RMSEP ± 846.51 kg/ha) and height (RMSEP ± 708.13 kg/ha). Based on the results of the study it was concluded the use of combined LiDAR and active optical reflectance sensors can help unlock the complex interrelationship between green fraction and biomass in swards containing both green and senescent material.

Accurate pasture biomass (PB) estimation is critical for tactical grazing management, yet traditional satellite-derived vegetation indices such as Normalised Difference Vegetation Index (NDVI) saturate when canopy density exceeds about 3 t DM ha−1. This limits predictive accuracy because the spectral signal plateaus under dense vegetation, masking further biomass increases. To address this limitation, this study integrated multiple data sources to improve PB estimation in dairy systems. The dataset combined Sentinel-2 spectral bands, rising plate-meter (RPM) PB measurements, daily weather data, and paddock management features. A total of 3161 paired RPM–satellite observations were collected from 80 paddocks across 16 New South Wales dairy farms between November 2021 and July 2024. Eight regression algorithms and four predictor configurations were evaluated using robust cross-validation, including an 80:20 farm/paddock-stratified train–test-set split. The XGBoost model using full-band reflectance and concurrent weather data achieved strong baseline performance (R2 = 0.63; MAE = 243 kg DM ha−1) on non-interpolated data, outperforming NDVI-based models. To address temporal gaps between field readings and satellite imagery, Multiquadric interpolation was applied to RPM data, adding roughly 30% new observations. This enhanced dataset improved test performance to R2 = 0.70 and MAE = 216 kg DM ha−1, with gains maintained on external validations (R2 = 0.41/0.48; MAE = 267/235 kg DM ha−1). A progressive training strategy, which refreshed model parameters with seasonally aligned data, further reduced errors by 30% compared to static models and sustained performance even when farms or seasons were excluded. This fortified Sentinel-2 modelling workflow, combining RPM interpolation and progressive calibration, achieved accuracy comparable to the commercial Pasture.io platform (R2 = 0.66; MAE = 240 kg DM ha−1) which uses satellite imagery with higher temporal and spatial resolution, demonstrating potential for automated recalibration and near real-time, paddock-level decision support in pasture-based dairy systems.

The profitability of agricultural industries that utilise pasture can be strongly affected by the ability to accurately measure pasture biomass. Pasture height measurement is one technique that has been used to estimate pasture biomass. However, pasture height measurement errors can occur if the sensor is mounted to a farm vehicle that experiences tilting or bouncing. This work describes the development of novel low ultrasonic frequency arrays for pasture biomass estimation. Rather than just measuring the distance to the top of the pasture, as previous ultrasonic studies have done, this hardware is designed to also allow ultrasonic measurements to be made vertically through the pasture to the ground. The hardware was mounted to a farm bike driving over pasture at speeds of up to 20 km/h. The analysed results show the ability of the hardware to measure the ground location through the grass. This allowed pasture height measurement to be independent of tilting and bouncing of the farm vehicle, leading to 20 to 25% improvement in the R 2 value obtained for biomass estimation compared with the traditional technique. This corresponded to a reduction in root mean squared error of predicted biomass from about 350 to 270 kg/ha, where the average biomass of the pasture was 1915 kg/ha.

Applied to grazing management, unmanned aerial systems (UASs) allow for the monitoring of vegetation at the level of each individual on the pasture while covering a significant area (>10 ha per flight). Few studies have investigated the use of UASs to describe the forage quality in terms of nutritive value or chemical composition, while these parameters are essential in supporting the productive functions of animals and are known to change in space (i.e., sward species and structure) and time (i.e., sward phenology). Despite interest, these parameters are scarcely assessed by practitioners as they usually require important laboratory analyses. In this context, our study investigates the potential of off-the-shelf UAS systems in modeling essential parameters of pasture productivity in a precision livestock context: sward height, biomass, and forage quality. In order to develop a solution which is easily reproducible for the research community, we chose to avoid expensive solutions such as UAS LiDAR (light detection and ranging) or hyperspectral sensors, as well as comparing several UAS acquisition strategies (sensors and view angles). Despite their low cost, all tested strategies provide accurate height, biomass, and forage quality estimates of timothy pastures. Considering globally the three groups of parameters, the UAS strategy using the DJI Phantom 4 pro (Nadir view angle) provides the most satisfactory results. The UAS survey using the DJI Phantom 4 pro (Nadir view angle) provided R2 values of 0.48, 0.72, and 0.7, respectively, for individual sward height measurements, mean sward height, and sward biomass. In terms of forage quality modeling, this UAS survey strategy provides R2 values ranging from 0.33 (Acid Detergent Lignin) to 0.85 (fodder units for dairy and beef cattle and fermentable organic matter). Even if their performances are of lower order than state-of-art techniques such as LiDAR for sward height or hyperspectral sensors (for biomass and forage quality modeling), the important trade-off in terms of costs between UAS LiDAR (>100,000 €) or hyperspectral sensors (>50,000 €) promotes the use of such low-cost UAS solutions. This is particularly true for sward height modeling and biomass monitoring, where our low-cost solutions provide more accurate results than state-of-the-art field approaches, such as rising plate meters, with a broader extent and a finer spatial grain.

The optimization of pasture food value, known as ‘biomass’, is crucial in the management of the farming of grazing animals and in improving food production for the future. Optical sensing methods, particularly from satellite platforms, provide relatively inexpensive and frequently updated wide-area coverage for monitoring biomass and other forage properties. However, there are also benefits from direct or proximal sensing methods for higher accuracy, more immediate results, and for continuous updates when cloud cover precludes satellite measurements. Direct measurement, by cutting and weighing the pasture, is destructive, and may not give results representative of a larger area of pasture. Proximal sensing methods may also suffer from sampling small areas, and can be generally inaccurate. A new proximal methodology is described here, in which low-frequency ultrasound is used as a sonar to obtain a measure of the vertical variation of the pasture density between the top of the pasture and the ground and to relate this to biomass. The instrument is designed to operate from a farm vehicle moving at up to 20 km h−1, thus allowing a farmer to obtain wide coverage in the normal course of farm operations. This is the only method providing detailed biomass profile information from throughout the entire pasture canopy. An essential feature is the identification of features from the ultrasonic reflectance, which can be related sensibly to biomass, thereby generating a physically-based regression model. The result is significantly improved estimation of pasture biomass, in comparison with other proximal methods. Comparing remotely sensed biomass to the biomass measured via cutting and weighing gives coefficients of determination, R2, in the range of 0.7 to 0.8 for a range of pastures and when operating the farm vehicle at speeds of up to 20 km h−1.