Estimation of biophysical parameters to monitor and manage pasture using a mobile application

This study aimed to develop a mobile solution for the estimation cover of green vegetation, pasture height, and aboveground biomass of pasture using orthogonal images. Experimental plots of Panicum sp grass were photographed in two experiments. The first experiment yielded data on plot biomass and the second experiment on pasture height. All samples were automatically georeferenced using the GPS location function of the smartphone. For green cover area, the application uses a region-growing segmentation algorithm and conversion to HSV color space (Hue, Saturation and Value) to obtain the relation of the regions where pixels average in the green matrix and compare the number of pixels classified as pasture with the total number of pixels in the image. The method for estimating height and biomass divides into two parts. The first part is the extraction of characteristics from the images using texture parameters, vegetation indexes, and information about image shadow projections. In the second part, a regression model was developed using the SVR (Support Vector Regression) technique. The model provided accuracy of 0.518 and 0.647 for the estimates biomass and pasture height, respectively.