Estimating Bermudagrass Aboveground Biomass Using Stereovision and Vegetation Coverage

Abstract

Accurate information about the amount of standing biomass is important in pasture management for monitoring forage growth patterns, minimizing the risk of overgrazing, and ensuring the necessary feed requirements of livestock. The morphological features of plants, like crop height and density, have been proven to be prominent predictors of crop yield. The objective of this study was to evaluate the effectiveness of stereovision-based crop height and vegetation coverage measurements in predicting the aboveground biomass yield of bermudagrass (Cynodon dactylon) in a pasture. Data were collected from 136 experimental plots within a 0.81 ha bermudagrass pasture using an RGB-depth camera mounted on a ground rover. The crop height was determined based on the disparity between images captured by two stereo cameras of the depth camera. The vegetation coverage was extracted from the RGB images using a machine learning algorithm by segmenting vegetative and non-vegetative pixels. After camera measurements, the plots were harvested and sub-sampled to measure the wet and dry biomass yields for each plot. The wet biomass yield prediction function based on crop height and vegetation coverage was generated using a linear regression analysis. The results indicated that the combination of crop height and vegetation coverage showed a promising correlation with aboveground wet biomass yield. However, the prediction function based only on the crop height showed less residuals at the extremes compared to the combined prediction function (crop height and vegetation coverage) and was thus declared the recommended approach (R2 = 0.91; SeY= 1824 kg-wet/ha). The crop height-based prediction function was used to estimate the dry biomass yield using the mean dry matter fraction.