Improving Nitrogen Status Diagnosis and Recommendation of Maize Using UAV Remote Sensing Data
Jiaxing Liang, Wei Ren, Xiaoyang Liu, Hainie Zha, Xian Wu, Chunkang He, Junli Sun, Mimi Zhu, Guohua Mi, Fanjun Chen, Yuxin Miao, Qingchun Pan
Abstract
Effective in-season crop nitrogen (N) status diagnosis is important for precision crop N management, and remote sensing using an unmanned aerial vehicle (UAV) is one efficient means of conducting crop N nutrient diagnosis. Here, field experiments were conducted with six N levels and six maize hybrids to determine the nitrogen nutrition index (NNI) and yield, and to diagnose the N status of the hybrids combined with multi-spectral data. The NNI threshold values varied with hybrids and years, ranging from 0.99 to 1.17 in 2018 and 0.60 to 0.71 in 2019. A proper agronomic optimal N rate (AONR) was constructed and confirmed based on the measured NNI and yield. The NNI (R2 = 0.64–0.79) and grain yield (R2 = 0.70–0.73) were predicted well across hybrids using a random forest model with spectral, structural, and textural data (UAV). The AONRs calculated using the predicted NNI and yield were significantly correlated with the measured NNI (R2 = 0.70 and 0.71 in 2018 and 2019, respectively) and yield (R2 = 0.68 and 0.54 in 2018 and 2019, respectively). It is concluded that data fusion can improve in-season N status diagnosis for different maize hybrids compared to using only spectral data.