Detection of excess nitrogen stress of corn and hazards with aerial multi-spectral imaging by UAV

Introduction
One of the main hazards in the agricultural sector is the uncontrolled use of nitrogen fertilizers. Excessive consumption of these fertilizers, in addition to increasing production costs, pollutes the environment, increases product vulnerability and increases the health risk to humans (Bagheri et al., 2013). Improving the efficiency of nitrogen consumption depends on monitoring the nitrogen status of the crop at different stages of growth and applying sufficient fertilizer at the right time and the right place (Zhao et al., 2003). Due to the adverse consequences of improper use of nitrogen fertilizers and the limitations of current methods to determine the amount of required nitrogen fertilizer, it is necessary to use new, fast, and non-destructive technologies for optimal nitrogen fertilizer consumption and increase its efficiency (Xue and Yang, 2008). Numerous studies have evaluated the possibility of using remote sensing technology to determine plant nitrogen status (Warren and Metternicht, 2005). Research shows that this technology can determine the amount of plant nitrogen (Bajwa., 2006; Reum and Xue and Yang, 2008). In recent years, with the development of UAVs, a new opportunity to monitor agricultural products has been found. Li et al used three types of cameras mounted on the UAV to estimate the nitrogen status of the crop in two wheat cultivars. The results of this study showed that the crop coverage index is well correlated with the amount of nitrogen in wheat cultivars (Li et al., 2010). In a study by Zaman Allah et al, UAV aerial imaging was used to investigate the tolerance of corn to nitrogen stress. The results showed that multi-spectral aerial images were able to determine the amount of soil nitrogen. So, nitrogen stress could be detected using vegetation indices. In this study, the NDVI index was strongly correlated with ground data (Zaman Allah et al., 2015). Krienke et al. (2017) used aerial imaging using UAVs to estimate nitrogen changes in corn vegetation. The results indicated that the UAV equipped with active sensors was able to determine the nitrogen stress of the plant (Krienke et al., 2017). Corti et al used a low-cost camera mounted on a UAV to study the nitrogen status of corn (Corti et al., 2019). Given the need to reduce the risks of overuse of nitrogen fertilizers, this study aims to identify the nitrogen stress of corn as a solution to determine the need for plant fertilizers using new, fast and non-destructive technologies to measure multi-level aerial reconnaissance with UAVs. Due to the importance of reducing the hazards of overuse of nitrogen fertilizers, this study aims to identify the nitrogen stress of corn as a solution to determine the need for fertilizer using new, fast and non-destructive technologies by multi-spectral aerial remote sensing by UAV.
Materials and methods
The research area was a part of an agricultural farm with an area of about 850 square meters in the village of Javadabad in the city of Varamin in the south of Tehran province with geographical coordinates of 35 ° 8ʹN and 51 ° 40ʹE. Soil sampling was carried out before planting. The experiment was performed as a randomized complete block design with 4 repeated nitrogen fertilizers (urea fertilizer) in four treatment levels including 0%, 50%, 100% and 150%. The hybrid corn seeds 450 was planted with a seeder in the depth of 5-8 cm with a row spacing of 75 cm and a spacing of 15 cm between plants. The farm was irrigated by tape according to the phonological stages of corn growth. Urea fertilizer along with irrigation water was injected in two stages of 8-leaves (V8) and tasselling (VT) growth stages. Ground sampling and multi-spectral aerial imaging were performed in 8-leaves and tasselling growth stages between 11 and 13 o'clock. Multi-spectral aerial imaging by UAV was performed on a sunny, cloudless day from a height of 100 meters above the ground. A multi-spectral ADC micro camera (520-920 nm) made by Tetracam was used for imaging (Bagheri et al., 2016). The UAV designed by Bagheri et al., 2016 was used. After capturing and extracting images from the camera's memory card, aerial images were processed using ENVI 5.4 and PixelWrench2 software. The radiometric correction was performed using a white Teflon calibration plate. Pre-processing of images included changing the image format from DCM to TIFF, creating false-color images, radiometric correction of images. Vegetation indices such as NDVI, NRI, MTVI2, CI, and GM, which were associated with plant chlorophyll and nitrogen content were calculated. For ground sampling, 10 corn bushes were randomly selected from each plot and the entire plant was cut above the ground. For all the leaves of each plant, the leaf chlorophyll index was measured using a 502-chlorophyll measuring device (Minolta Corp., Osaka, Japan) after calibration of the device. The Kjeldhal method was used to determine the nitrogen content of the samples (Bagheri et al., 2012). Data analysis was performed using statistical methods. Also, the nitrogen and fertilizer stress estimation models were extracted based on the studied vegetation indices through regression models and the best model for nitrogen stress at each stage of growth was introduced.
Discus and Results
The relationship between chlorophyll and nitrogen content at different stages of growth
In the V8 and VT growth stages, with increasing the amount of nitrogen fertilizer distributed, the chlorophyll of the samples increased; because with the increase of fertilizer received to the optimum level, the chlorophyll of the leaves has increased. The correlation coefficient between chlorophyll and nitrogen data was obtained in the V8 growth stage obtained 0.92 and 0.999 in the VT growth stage.
Correlation of the studied vegetation indices with the amount of corn nitrogen
In the V8 stage, the second-order regression equation with a correlation coefficient of 0.77, 0.67, 0.86 and 0.88 for the NDVI, NRI, MTVI2 and CI indices, respectively, have the highest correlation for estimating the percentage of nitrogen among other models. In the VT growth stage for the NDVI, CI and GM indices, the second-order regression equation with a correlation coefficient of 0.84, 0.75 and 0.77, respectively, had the highest correlation to estimate the percentage of nitrogen. For NRI and MTVI2 indices, both the power and logarithmic equations with a correlation coefficient of 0.90 and 0.75 had the highest correlation for estimating the percentage of nitrogen.
Conclusion
Due to the need for reducing the risks of overuse of nitrogen fertilizers and the ability of remote sensing technology as a new, fast, and non-destructive method to detect plant variabilities, this study was conducted to identify the nitrogen stress of corn as a way to descript Nitrogen fertilization. The overall results of this study are:
 - The amount of chlorophyll and nitrogen content of plants increased with an increasing amount of nitrogen fertilizer applied in both growth stages.
 - In the V8 growth stage, the second-order regression equation with a correlation coefficient of 0.77, 0.67, 0.86 and 0.88 for the NDVI, NRI, MTVI2 and CI indices, respectively, has the highest correlation for estimating nitrogen stress among other models.
 - In the VT growth stage, for NDVI, CI and GM indices, the second-order regression equation with a correlation coefficient of 0.84, 0.75 and 0.77 had the highest correlation to estimate nitrogen stress, respectively.
 - The multi-spectral aerial remote sensing method is capable enough to detect variability and stress in nitrogen fertilizer.