Expedited generation of terrain digital classes in flat areas from UAV images for precision agriculture purposes

Abstract

Precision agriculture (PA) requires reasonably homogeneous areas for site-specific management. This work explores the applicability of digital terrain classes obtained from a digital elevation model derived from UAV-acquired images, to define management units in in a relative flat area of about 6 ha. Elevation, together with other terrain variables such as: slope degree, profile curvature, plan curvature, topographic wetness index, sediment transport index, were clustered using the Fuzzy Kohonen Clustering Network (FKCN). Four terrain classes were obtained. The result was compared with a map produced by a classification of soil properties previously interpolated by ordinary kriging. The results suggest that areas for site-specific management can be defined from terrain classes based on environmental covariates, saving time and cost in comparison with interpolation of soil variables.

Surveillance and Inspection Micro Quadcopter Drone in Agriculture

“…Even the smallest signs of pest infestations may be recognized by drones, which can also offer exact information on the scope and location of the assault. [30] This may help farmers determine the amount of pesticides they should use to protect their crops, not damage them.…”