Creating a UAV Image Dataset for the Estimation of Invasive Plant Biomass

To begin, employ a random sampling approach with enough biomass samples to determine soil sampling locations in the study area. Position the sample frame horizontally over the vegetation, fully encompassing the plants with a minimum separation distance of two meters between each plant. Use a drone and a camera to build an unmanned aerial vehicle or UAV remote sensing filming system.

To plot the route within the specified study area using the UAV, first, establish a heading and side overlap rate of 70%Maintain the camera perpendicular to the ground, and at an altitude of 30 meters to capture photos at uniform time intervals of two seconds. Run the script file to store the aerial imagery for subsequent processing with Python software for biomass estimation. For creating the dataset using Python programming, proceed to segment the raw image data into smaller images of size the same as the sample images.

Use the sliding window method for segmentation, setting the horizontal and vertical steps to 280 pixels. From the segmented small images, randomly select 880 invasive plant images and 1, 500 background images to create a dataset. Then split this dataset into training, validation, and test sets in a 6:2:2 ratio.