Yield Prediction by Integrating NDVI and N-Tester Data with Yield Monitor Data

Monitoring of crop growth and forecasting its yield well before harvest is very important for better crop and food management. Unmanned aerial vehicle (UAV) installed with near infrared camera (NIR camera) is a potentially important for acquisition of data to provide spatial and temporal data for site specific crop management. Hence, the study has been carried out to develop the empirical relationship for Infrared camera and N-Tester data at different crop growth stages with yield data for maize crop. Infrared camera and N-Tester were used to collect data at different growth stages of the crop to develop relationship with the yield monitor data. The near infrared (NIR) camera was mounted on parrot AR. Drone 2.0 frame for image acquisition. Based on aerial images of the plots the Normalized Difference Vegetation Index (NDVI) was calculated. Maize field was harvested by the combine harvester mounted with yield monitor to generate the yield map of the field. Yield is the measure for quantifying the agricultural input and crop management. Yield map is vital for site specific crop management. Statistical linear regression models were used to develop empirical relationship between the NDVI and N-Tester data and yield at different growth stages of maize crop. The yield prediction equations have maximum coefficient of determination (R²) 0.84 for N-Tester and 0.86 for NDVI (NIR camera) at silking stage (R1).NDVI and N-tester values were positively correlated with yield data at all growth stages of maize. It was concluded that the silking stage (R1 stage) i.e. 55 DAP was the most prominent stage for yield prediction using NDVI and N-Tester values.