Abstract
When performing yield prediction and weed identification by analyzing a three-dimensional (3D) model of a farm constructed from aerial images, it is useful to exclude in advance forests, houses, warehouses, and work roads among others included in the model. In this study, we introduce the process of selecting only the grassland part from the 3D model of a farm. Assuming the use of the point cloud classification function and digital surface model provided in the model construction software, the selection process is implemented in the computer using the programming language Python. The process consists of the following three steps: 1) construction of a 3D model after performing point cloud classification to obtain a digital surface model and an orthomosaic image excluding most of the forests and buildings, 2) complete removal of the noise remaining in the forest part using the slope attribute of the digital surface model, and 3) removal of the fields not subject to analysis using the color information of work roads. We use the Agisoft Metashape Professional software to perform 1) and program with a Python script to perform 2) and 3), including a user interface to visually execute the process. We apply these steps of image processing to the 3D models constructed from aerial images taken with three types of small multicopter at three different times in two different grasslands. By carrying this method, we can easily select the image of distorted grassland fields.
