UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing

Carrying out monitoring during the crop cycle through vegetation indices (VIs) with obtained unmanned aerial vehicle allows agility in decisions about management practices, as well as concerning nutritional deficiencies in crops, as nitrogen (N). This nutrient absorbed in greater quantity, and that most influences the grain yield in corn. This research hypothesized that different N topdressing levels can affect the agronomic performance of corn varieties and that those effects can be expressed by VIs. The objective was to evaluate the use of VIs in the monitoring of corn varieties submitted to different N levels. Two experiments were carried out in a randomized block design with three replicates in a factorial scheme, replicated for two crop seasons (2017/2018 and 2018/2019). The first factor was composed of 11 cultivars of corn. The second factor was composed of two contrasting N levels (60 kg ha(-1) - low and 180 kg ha(-1) - high). Vegetation indices (NDVI and NDRE) and agronomic traits (leaf N content, plant height, ear insertion height, stem diameter, ear length, number of rows per ear, number of grains per row, and grain yield) were evaluated. Our findings allow us to understand how top dressing can influence the agronomic performance of corn genotypes and their relationship with UAV-vegetation indices in two crop seasons using Sensefly Sequoia multispectral sensor. High N topdressing levels provides better agronomic and spectral response in corn, regardless of the variety used. This behavior can be confirmed through the NDVI and NDRE. High N topdressing levels provides a positive correlation between the VIs evaluated (NDVI and NDRE) with the grain yield in corn.

Automated summary

The study used unmanned aerial vehicles to monitor vegetation indices (VIs) during the crop cycle, finding that high nitrogen topdressing levels positively impact the agronomic performance of corn, as indicated by nitrogen content, plant characteristics, and VIs.