Improved detection of planting failures by computer vision

Abstract

ABSTRACT The management of natural and planted forests can be conducted sustainably by implementing techniques that consider the spatial and temporal variability of the plant and soil. In this context, precision silviculture through remote sensing can play a vital role, mainly when using Unmanned Aerial Vehicles (UAVs) equipped with specific sensors. In the present study, an automated computational routine (based on computer vision techniques) was developed and validated to perform forest inventory in commercial Eucalyptus grandis forests, using an orthophoto mosaic obtained with an RGB sensor built-in to a UAV. The developed routine employs computer vision techniques, including template matching to locate plants, Delaunay triangulation to create a mesh and indicate the predominant orientations of the planting rows, and an adaptation of the Hough transform to estimate the analytical parameters of each row. These parameters are refined using linear regression to generate the lines best fitting the input data. Finally, the failure segments on each row are identified by detecting the plants in each row. A simulation of regular point distribution on the segment is then used to identify the planting failure. This process allows the geolocation of each failure point for replanting to be quantified. The routine has significant potential in the forest inventory, allowing the geolocation of failures with an overall accuracy of 0.97 and 0.99, respectively, and a maximum positional error of 0.15 m and 0.20 m, respectively.

Keywords

• hough transform
• point Clustering
• precision forestry
• remote sensing
• unmanned aerial vehicles