Rice nitrogen status detection using commercial-scale imagery

Determining the mid-season nitrogen status of rice is important for precision application of fertilizer to optimize productivity. While there has been much research aimed at developing remote-sensing-based models to predict the nitrogen status of rice, this has been predominantly limited to scientific small plot trials, relying on experts performing radiometric calibrations, encompassing limited cultivars, seasons and locations, and uniform management practices. As such, there has been little testing of models at commercial scale, against the range of conditions encountered across entire growing regions. To fill this gap, this work brings together four years of data, from both experimental replicated plot trials (38 datasets with 1734 observations) and commercial farms (12 datasets with 106 observations). Using commercial scale imagery acquired from airplanes, a number of nitrogen uptake modeling methodologies were evaluated. Universal single vegetation index based linear regression models had prediction root mean squared error (RMSE) of more than 45 kg/ha when tested at the 12 commercial sites. Machine learning models using multiple remote sensing features were able to improve predictions somewhat (RMSE > 30 kg/ha). Practically useful accuracies were achieved after using three local field samples to calibrate models to each field image. The prediction RMSE using this methodology was 22.9 kg/ha, or 19.4%. This approach enables provision of optimal variable-rate mid-season rice fertilizer prescriptions to growers, while motivating continued research towards development of methods that reduce requirement of local sampling.

Automated summary

Research on predicting nitrogen status in rice fields has been limited to scientific small plot trials, lacking testing at commercial scales. By using machine learning models and remote sensing features, accuracy in nitrogen uptake prediction can be improved, as shown through analysis of experimental plot trials and commercial farm data.