Real time estimation of chlorophyll content based on vegetation indices derived from multispectral UAV in the kinnow orchard

Abstract: Nondestructive estimation of the biophysical properties of crops provide quick and real time information of crop health under wide range of environment.  The chlorophyll content is an important indicator of crop health and widely used for determination of nutritional status of the crops real time in precision agriculture.  Advancement in the low altitude remote sensing (LARS) technologies such as Unmanned Aerial vehicles (UAVs) provides high temporal and spatial resolution solution for nondestructive, rapid and accurate estimation of biophysical properties of various crops.  The main objective of this study was to evaluate the high resolution multispectral UAV images for nondestructive and real time estimation of the kinnow tree leaves chlorophyll content in district Sargodha, Pakistan.  Kinnow tree leaves chlorophyll contents were measured manually using chlorophyll meter (SPAD-502 Minolta) in the kinnow orchard along with GPS positions in district Sargodha.  The UAVs images were also acquired during the same time when ground-truthing campaign for kinnow leaves chlorophyll content was performed. Vegetation indices including Normalized Difference Vegetation Index (NDVI), Transformed Normalized Difference Vegetation Index (TNDVI), Modified Chlorophyll Absorbed Ratio Index (MCARI2), Soil adjusted vegetation Index (SAVI) and Modified soil adjusted vegetation index (MSAVI2) were derived by multispectral UAV images for chlorophyll estimation.  The regression analysis was performed between ground-truthing data of chlorophyll content and UAV derived vegetation indices for predicting kinnow leave chlorophyll content model. MSAVI2 and TNDVI were proved to be more robust indices to estimate the chlorophyll content in the kinnow orchard with the highest coefficients of determination ( R 2 ) 0.89 and 0.85 respectively.  The results showed that the multispectral UAV can be used for accurately estimation of chlorophyll content and assess crop health status in a wider range which will help in managing crop nutrition requirement in real time in the kinnow orchard. Keywords: Chlorophyll content, kinnow orchard, Multispectral UAV, Vegetation indices DOI: 10.33440/j.ijpaa.20180101.0001 Citation: Tahir M N, Naqvi S Z A, Lan Y B, Zhang Y L, Wang Y K, Afzal M, et al.  Real time monitoring chlorophyll content based on vegetation indices derived from multispectral UAVs in the kinnow orchard.  Int J Precis Agric Aviat, 2018; 1(1): 24–31.

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