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The use of Unmanned Aerial Vehicles (UAVs) is becoming very common for last few years for monitoring agricultural crops efficiently.  Low altitude remote sensing (UAV) provides people with high temporal and spatial resolution for non-destructive, accurate and timely estimation of biophysical parameters like Leaf Area Index (LAI), crop growth, plant biomass and final crop yield.  Collection of the data by UAV helps to reduce errors and it fills the biasness on an observational scale in precision agriculture.  The main objective of this study was to estimate real time LAI and yield of groundnut crop based on Normalized Difference Vegetation Index (NDVI) using low cost multispectral UAV.  A field experiment was set up  with three different groundnut cultivars (V1= BARD-479, V2= Chakwal-2011 and V3= Chakwal-2016) with three replications.  Field data collection regarding LAI was performed in 2019 at two different growth stages (2-3 leaf stage and Peg formation stage) of groundnut on PMAS-Arid Agriculture Research Farm (Knoot), Pakistan.  Final yield was calculated at the time of crop maturity.  In this study, low cost UAV platform was established with DJI Phantom 4 pro and Parrot Sequoia Sensor to develop a multispectral UAV system used as the survey platform.  A Parrot Sequoia camera was mounted on the UAV used as the remote sensor.  The sensor provided the information in five narrow bands including Red, Blue, Green, Near infrared (NIR) and Red Edge.  The processing of UAV images was performed in the Python environment and NDVI images were created.  Then regression model was performed to compare the …