ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Citation
Articles | Volume X-3-2024
https://doi.org/10.5194/isprs-annals-X-3-2024-69-2024
https://doi.org/10.5194/isprs-annals-X-3-2024-69-2024
04 Nov 2024
 | 04 Nov 2024

Dry mass grassland estimation using UAV ultra-wide RGB images

Rebeca Campos Emiliano da Silva, Antonio Maria Garcia Tommaselli, Nilton Nobuhiro Imai, Rorai Pereira Martins-Neto, Daniel da Silva da Silveira, and Edemar Moro

Keywords: grassland, dry matter, machine learning, UAV, precision agriculture, remote sensing

Abstract. Dry mass is an important parameter to optimise grassland management. Traditionally, dry mass values are estimated manually by cutting, drying, and weighing vegetation samples. In large areas of cultivation, this becomes a time-consuming and costly activity. In recent years, many researchers have studied different sensors embedded in Unmanned Aerial Vehicles (UAV) to collect spatial data and estimate biomass using machine learning algorithms for forest and agricultural applications. However, there needs to be more research dealing with estimating production indices for pasture, especially in Brazil, as stated. This study evaluates the feasibility of using the GoPro wide-angle RGB camera on UAVs (Unmanned Aerial Vehicles) to estimate the dry mass of pastures. Different data analysis methods were compared, including the combination of vegetation indices (VIs) values and three-dimensional metrics (3D) extracted from the Canopy Height Model (CHM): all metrics (ALL), three VIs plus four 3D metrics (VI3 + CHM4) and only 3D metrics. Random Forest (RF) machine learning algorithm was used to estimate dry mass. The best results were obtained when merging all the variables from the two flight campaigns, with a coefficient of determination (R2) of 0.80 for the model and a Pearson Correlation Coefficient (PCC) of 0.85 for validation, with a Root Mean Square Error (RMSE%) of 20.5%. In summary, using RGB sensors embedded in UAVs is a promising technique for estimating farm grazing parameters.