Document Type : Research Paper


1 Corresponding Author, Agricultural Engineering Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. E-mail:

2 Faculty of Agricultural Technology (Aburaihan), University of Tehran, Pakdasht, Iran. E-mail:

3 Department of Agronomy and Agroecology, Islamic Azad University, Varamin-Pishva Branch, Pishva, Iran. E-mail:



Objective: In order to present a new, non-destructive, accurate, and fast method for estimating the nitrogen content of corn, Unmanned Aerial Vehicle (UAV) multispectral sensing technology was used.
Methods: The experiments were performed based on a randomized complete block design in four levels of nitrogen fertilizer (zero, 50, 100, and 150%) in Varamin in 2018. Sampling was carried out in two stages of fertilization (8-leaf Stage and Tasseling Stage). Multispectral aerial imaging and ground sampling was performed one week after each fertilizer application. After processing aerial imagery, vegetation indices were calculated and their correlation with the results of ground sampling was determined.
Results: Based on the results obtained from the correlation coefficients (r) and best subsets regression, among the spectral vegetation indices, Normalized Difference Vegetation Index (NDVI), Nitrogen Reflectance Index (NIR), and Modified Triangular Vegetation Index2 (MTVI2) indices in both eight leaf collar (V8) and tasseling (VT) of maize growth stage was identified as the best indicator to estimate the nitrogen content of forage maize. At VT, a positive and significant relationship was obtained between NDVI (R2= 0.86, P≤0.001), NRI (R2= 0.70, P≤0.001) and MTVI2 (R2= 0.46, P≤0.01) indices with maize nitrogen content.
Conclusion: It can be concluded that UAV multispectral imaging provides acceptable accuracy in determining the nitrogen content of maize. This technology can help farmers to determine the appropriate time of fertilization.


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