Using the time series nitrogen diagnosis curve for precise nitrogen management in wheat and rice

Wheat and rice are the main food crops in China. Appropriate nitrogen (N) fertilizer can effectively promote the crop growth, whereas excessive use has repercussions on yield formation and environmental preservation. Therefore, timely assessment of crop N status and precise N application management are of paramount importance. The study aims to assess the impacts of N fertilizer on N accumulation and spectral dynamics in wheat and rice, and investigate the feasibility of real-time crop N status diagnosis using unmanned aerial vehicle (UAV) spectra and devise subsequent managements. Ten experiments were conducted in Xinghua City and Lianyungang City from 2017 to 2020, involving different N fertilizer rates (0–405 kg N ha−1) and various cultivars. Field sampling was carried out simultaneously with UAV image acquisition, and the crop dry matter and N concentration were obtained by indoor analysis. Normalized difference red-edge index (NDRE) and N nutrition index (NNI) demonstrated a robust power function relationship (R2> 0.70). The time series N diagnosis curves established by critical NDRE values achieved recognition accuracy of over 89%. The validation accuracy of critical NDRE values achieved 93.84%. The probability of the calculated topdressing rate (N) falling between the agronomic optimal N rate (AONR) and economic optimal N rate (EONR) is 86%. The time series N diagnosis curve offered possibility to real-time judgement of plant N status. In addition, the subsequent N topdressing design was proved through the improvement of existing sufficiency index (SI) algorithm. The time series N diagnosis curve will provide valuable decision support for the optimal N fertilizer management of wheat and rice. Moreover, the UAV platform holds promising potential for regional-scale application in the future. •N fertilizer significantly affected dynamics of N accumulation and canopy spectra.•NDRE had a good correlation with PNA and NNI of wheat and rice.•RAGDD-based time series N diagnosis curve was established and verified.•N diagnosis curve combined well with SINR to guide field topdressing in real time.