Use of RGB Images in Field Conditions to Evaluate the Quality of Pastures in Farms in Antioquia: A Methodology

Abstract

The use of RGB (Red, Green, and Blue) images is a useful technique considered in the prediction of diseases, moisture content, height, and nutritional composition of different crops of productive interest. It is important to adopt a methodology in the field that allows the acquisition of images without losing the quality of the information in the RGB bands since the prediction and adjustment of the grass quality parameters depend on it. Currently, there are few studies and methodologies that support the validity of the use of RGB images in the field, since there are many environmental factors that can distort the information collected. For this study, a field methodology was established where RGB images were captured using the unmanned aerial vehicle drone, DJI Phantom 4 Pro. A total of 270 images of grass crops for animal feed were taken on 15 farms in Antioquia. The images were pre-processed using the programming language Python, where a region of interest for each image was chosen and the average RGB values were extracted. Different indices were created with the RGB bands and based on them; several models were used for the nutritional variables of the pasture, managing to find suitable equations for acid detergent fiber, crude protein, and moisture.