Rice nitrogen nutrition monitoring based on unmanned aerial vehicle multispectral image

An accurate and universal monitoring model of nitrogen nutrition can greatly contribute to the precise management and application of regional rice. In this study, a systematic investigation was carried out to explore the impact of the images on the monitoring models. The consumer-grade multispectral images were captured by unmanned aerial vehicles (UAV) from the different ecological sites and rice varieties. A series of field experiments were conducted at two test sites: Mengzhe Town, Xishuangbanna, Yunnan Province (with trial variety Yunjing 37) and Beibei District, Chongqing City (with trial variety Jiyu 6135) in South China, with the varying nitrogen levels. Four multispectral drones (DJI Phantom) were used to capture the multispectral images of the rice canopy during tillering, jointing, and heading stages. The nitrogen content in rice plant canopies (CNC) was measured to calculate the above-ground nitrogen accumulation (PNA) using the Kjeldahl method. The nitrogen nutrition monitoring models were established using vegetation indices, partial least-squares regression (PLSR), random forest (RF), and backpropagation neural network (BPNN) for the single trial site, single variety, different trial sites, and multiple varieties of rice. The transferability was also explored in the models. The monitoring models were established for the CNC and PNA during each growth stage of rice for the single trial site and single variety with the high accuracy (normalized difference vegetation index NDVI or near-infrared normalized vegetation index NNVI, where the coefficient of determination was 0.68-0.88). However, the inaccurate model was obtained with the vegetation index during the tillering stage (NDVI, the coefficient of determination was 0.53-0.79), indicating the low transferability of all models. It was difficult for the vegetation indices to construct the monitoring models for the canopy nitrogen content throughout the entire growth period of rice. But a monitoring model was established for the above-ground nitrogen accumulation of single-variety rice in the period of the growth using the ratio vegetation index, the high accuracy and the high transferbility of the model. The monitoring models constructed by PLSR, RF, and BPNN were more accurate than those by vegetation indices. Among them, the highest accuracy was achieved in the monitoring model for the canopy nitrogen content and above-ground nitrogen accumulation in the entire growth period of multiple varieties using RF, with the coefficient of determination values were 0.84 and 0.94, respectively, and root-mean-square errors of 0.28% and 10.09 kg/hm², respectively. The finding can provide a theoretical basis and technical support for the application of consumer-grade multispectral images captured by drones in monitoring rice nitrogen nutrition during the entire growth period of different ecological sites and rice varieties.