Improving Nitrogen Status Diagnosis and Recommendation of Maize Using UAV Remote Sensing Data
Improving Nitrogen Status Diagnosis and Recommendation of Maize Using UAV Remote Sensing Data
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DOI：
10.3390/agronomy13081994
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发表时间：
2023-07
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影响因子：
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通讯作者：
Jiaxing Liang;Wei Ren;Xiaoyang Liu;H. Zha;X. Wu;Chunkang He;Junli Sun;Mimi Zhu;G. Mi;Fanjun Chen;Y. Miao;Qingchun Pan
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作者：
Jiaxing Liang;Wei Ren;Xiaoyang Liu;H. Zha;X. Wu;Chunkang He;Junli Sun;Mimi Zhu;G. Mi;Fanjun Chen;Y. Miao;Qingchun Pan
有效的作物氮素营养状况诊断对作物氮素精准管理具有重要意义，无人机遥感是进行作物氮素营养诊断的有效手段之一。在此基础上，采用6个氮素水平和6个玉米杂交种进行田间试验，测定氮素营养指数（NNI）和产量，并结合多光谱数据诊断杂交种的氮素状况。NNI阈值因混合动力车和年份而异，2018年为0.99至1.17，2019年为0.60至0.71。根据测得的氮素营养指数（NNI）和产量，构建并确定了适宜的农艺最适施氮量（AONR）。NNI（R2 = 0.64-0.79）和谷物产量（R2 = 0.70-0.73），以及使用随机森林模型与光谱，结构和纹理数据（UAV）的杂交种预测。使用预测的NNI和产量计算的AONR与测量的NNI（2018年和2019年分别为R2 = 0.70和0.71）和产量（2018年和2019年分别为R2 = 0.68和0.54）显著相关。结果表明，与仅使用光谱数据相比，数据融合可以改善不同玉米杂交种的季节氮素状况诊断。
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.