The Effect of Restricted Irrigation Applications on Vegetation Index Based on UAV Multispectral Sensing

Abstract

Increasing water demands in agricultural cultivation have made it necessary to develop better irrigation management strategies within today's development and technologies. Information production based on imaging technologies is also included in these uses. In this study, the usability of UAV-based multispectral images in the evaluation of subsurface and surface drip irrigation applications was investigated. For this purpose, a silage maize trial programmed to be multiples of ET0 (0.00, 0.25, 0.50, 0.75, 1.00, 1.25, 1.5) was imaged using an unmanned aerial vehicle during the growing season. 9 different vegetation indexes were created in the images taken and their usability in monitoring the applications was compared with each other. It was determined that LCI and TGI indexes in subsurface drip irrigation method and VARI index in surface drip irrigation method can be used at the level of irrigation programs (p<0.05). When the temporal data were examined during the development period, it was determined that there were differences between the 9 plant index results (p<0.05). When the surface and subsurface drip irrigation methods derived from the analysis of multispectral images were compared with the Vegetation Indexes (VI), it was observed that there was a statistically significant difference between the treatments. When irrigation rates were compared, similar differences were determined in vegetation index values. The obtained results demonstrated the feasibility of UAV-integrated multispectral images to characterize the responses of plants to different irrigation applications. It is thought that Smart Agriculture, Precision Agriculture, Organic Agriculture, and Good Agricultural Practices made with UAVs will have high utilization potential at the farm level.

Keywords

• silage corn
• water limited
• irrigation method
• multispectral sensor
• vegetation index
• unmanned aerial vehicles (uav)