Description
The assessment of the amount of biomass in the field is one of the critical factors that helps to manage and optimize numerous operations associated with forage management in the livestock industry. Pasture management decisions about stocking rate, grazing duration, and fertilizer application rate depend on accurate forage availability measurements. The objective of this study was to develop different nondestructive methods of forage biomass estimation using unmanned vehicles based on the relationship between crop height (CH) and the measured above-ground biomass. The unmanned vehicle-based methods were developed and tested on Alfalfa (Medicago Sativa) and Tall Fescue (Schedonorus phoenix (Scop.) Holub) fields. The real-time compressed crop height was measured using the ultrasound proximal sensor and a compression ski installed on the unmanned ground vehicle (UGV) and orthomosaic from aerial images was used for plot identification for site-specific analysis. The experiment was carried out before and after harvest to calculate the harvested CH to generate its regression relation with wet and dry biomass yield of forage. The results show that these systems produce promising results with R-square values of 0.8 and 0.5 for biomass estimation in Alfalfa and Tall Fescue respectively. These methods will significantly reduce the on-field destructive forage sampling for biomass estimation and aid in predicting the available biomass along with reducing the human efforts and resources for performing biomass sampling tasks, resulting in reduction of time and cost.
Included in
Agricultural Science Commons, Agronomy and Crop Sciences Commons, Plant Biology Commons, Plant Pathology Commons, Soil Science Commons, Weed Science Commons
Estimating Forage Biomass using Unmanned Ground and Aerial Vehicles
The assessment of the amount of biomass in the field is one of the critical factors that helps to manage and optimize numerous operations associated with forage management in the livestock industry. Pasture management decisions about stocking rate, grazing duration, and fertilizer application rate depend on accurate forage availability measurements. The objective of this study was to develop different nondestructive methods of forage biomass estimation using unmanned vehicles based on the relationship between crop height (CH) and the measured above-ground biomass. The unmanned vehicle-based methods were developed and tested on Alfalfa (Medicago Sativa) and Tall Fescue (Schedonorus phoenix (Scop.) Holub) fields. The real-time compressed crop height was measured using the ultrasound proximal sensor and a compression ski installed on the unmanned ground vehicle (UGV) and orthomosaic from aerial images was used for plot identification for site-specific analysis. The experiment was carried out before and after harvest to calculate the harvested CH to generate its regression relation with wet and dry biomass yield of forage. The results show that these systems produce promising results with R-square values of 0.8 and 0.5 for biomass estimation in Alfalfa and Tall Fescue respectively. These methods will significantly reduce the on-field destructive forage sampling for biomass estimation and aid in predicting the available biomass along with reducing the human efforts and resources for performing biomass sampling tasks, resulting in reduction of time and cost.
