Quality of forest plantations using aerial images and computer vision techniques

Willer Fagundes de Oliveira, Antônio Wilson Vieira, Silvânio Rodrigues dos Santos


Geotechnology has provided several tools that allow the spatial and temporal variability of soils and plants to be investigated, leading to the consolidation of Precision Agriculture. The great challenge for studies using sensors mounted aboard Remotely Piloted Aircraft (RPA) lies in interpreting the high-dimensional data, since most sensors do not measure the biometric parameters of a plant directly. Therefore, the aim of the present study was to develop a methodology for using digital images (obtained by means of an airborne RGB sensor mounted aboard an RPA) in the quality control of forest plantations, specifically Eucalyptus (Eucalyptus ssp.), planted in a commercial area. A Phantom 4 Pro multirotor RPA was used, equipped with a 20 Megapixel RGB sensor, acquiring images with 80% and 60% longitudinal and lateral overlap, respectively. From the generated orthomosaic, a Test Area was outlined to be used in developing the processing routine based on computer vision techniques. In general, the proposed methodology maps the individual location of each plant in the orthomosaic, resulting in a mesh that allows the automatic generation of report maps of various silvicultural variables, such as plant count, planting failures, and spacing between rows and plants. In addition to high computer performance, with real-time processing, the methodology was highly accurate in correctly identifying more than 93% of plants in an area of more than 3,000 plants.


Remote Sensing; Vegetation index; Drone; Python; Eucalyptus

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