Monitoring of Corn Growth Stages by UAV Platform Sensors

Abstract

Increasing agricultural productivity with economic and environmental sustainability is one of the main challenges in agriculture. The aerial survey platforms known as unmanned aerial vehicles (UAV), so-called drones, allow monitoring, evaluation, and decision support activities to improve the management of crops and herds in farms of any production scale. Vegetation indices are used to map the vegetation cover, mainly on a large scale, using satellite images. However, sensors coupled to UAV platforms provide other indices that can be used to detect the stress load of vegetation at more precise spatial scales. The Visible Atmospherically Resistant Index - VARI and the Green Leaf Index - GLI showed similar performances in the initial vegetative stages of corn crop. Both indices were sensitive to class discrimination at intervals that indicate from bare soil and low vigor (shades of red, orange, and yellow) to the condition of high vegetation vigor (shades of green). The results of vegetation indices in the visible spectrum range prove the applicability of the method for data collection and information extraction related to development and growth of crops. Overall, the indices VARI and GLI appear as a potential alternative for crop monitoring using low cost RGB sensors onboard UAV platforms.