Predicting Nitrogen Content in Rice Using Unmanned Aerial Vehicle Based Multispectral Imaging

Precise estimation of rice nitrogen (N) content is essential for optimizing fertilizer use. Traditional methods for estimating N content are time-consuming, laborious, and costly. Unmanned aerial vehicles (UAVs) are time and money efficient substitutes allowing more accurate and flexible monitoring for larger rice areas. The objectives of this study were to: (i) develop random forest (RF) and artificial neural network (ANN) models for predicting and mapping the nitrogen content (%) in rice using seven vegetation indices derived from UAV multispectral sensors and; (ii) assess the key vegetation indices (VI) and their interrelationships with the predicted nitrogen content. Experiments were conducted at two locations in Cuttack district of Odisha, India, with different nitrogen levels. The UAV images were collected synchronizing with the maximum tillering stage of rice and seven indices were generated. The rice sampling was done on the date of flying UAV images and nitrogen content was estimated in the laboratory. RF and ANN models were developed using the N content as dependent and the VIs as independent variables. Both the models exhibited robust predictive capabilities, however, the RF model exhibited better performance, compared to the ANN model. Nitrogen content prediction using the developed RF and ANN models in testing site at farmer's field ranged from 0.78% to 1.95% (R² of 0.67%) and from 0.5% to 1.78% (R² of 0.55%), respectively. Normalized difference red edge (NDRE) and normalized difference vegetation index (NDVI) turned out as significant contributors in the development of both the models.