Nitrogen (N) is one of the most essential nutrients that can significantly affect crop grain yield and quality. The implementation of proximal and remote sensing technologies in precision agriculture has provided new opportunities for non-destructive and real-time diagnosis of crop N status and precision N management. Notably, leaf fluorescence sensors have shown high potential in the accurate estimation of plant N status. However, most studies using leaf fluorescence sensors have mainly focused on the estimation of leaf N concentration (LNC) rather than plant N concentration (PNC). The objectives of this study were to (1) determine the relationship of maize (Zea mays L.) LNC and PNC, (2) evaluate the main factors influencing the variations of leaf fluorescence sensor parameters, and (3) establish a general model to estimate PNC directly across growth stages. A leaf fluorescence sensor, Dualex 4, was used to test maize leaves with three different positions across four growth stages in two fields with different soil types, planting densities, and N application rates in Northeast China in 2016 and 2017. The results indicated that the total leaf N concentration (TLNC) and PNC had a strong correlation (R² = 0.91 to 0.98) with the single leaf N concentration (SLNC). The TLNC and PNC were affected by maize growth stage and N application rate but not the soil type. When used in combination with the days after sowing (DAS) parameter, modified Dualex 4 indices showed strong relationships with TLNC and PNC across growth stages. Both modified chlorophyll concentration (mChl) and modified N balance index (mNBI) were reliable predictors of PNC. Good results could be achieved by using information obtained only from the newly fully expanded leaves before the tasseling stage (VT) and the leaves above panicle at the VT stage to estimate PNC. It is concluded that when used together with DAS, the leaf fluorescence sensor (Dualex 4) can be used to reliably estimate maize PNC across growth stages.

中文翻译：

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使用叶片荧光传感器在整个生长阶段估算玉米的植物氮含量

氮（N）是最重要的营养素之一，可以显着影响农作物的产量和品质。精确农业中近程和遥感技术的实施为作物氮素状态的无损实时诊断和氮素精确管理提供了新的机会。值得注意的是，叶片荧光传感器在准确估计植物氮素状况方面显示出很高的潜力。但是，大多数使用叶片荧光传感器的研究主要集中在叶片氮浓度（LNC）而不是植物氮浓度（PNC）的估计上。这项研究的目的是（1）确定玉米（Zea mays L.）LNC和PNC的关系，（2）评估影响叶片荧光传感器参数变化的主要因素，（3）建立一个一般模型来直接估计整个成长阶段的PNC。叶片荧光传感器Dualex 4被用于测试2016年和2017年东北地区土壤类型，播种密度和氮肥施用量不同的两个田地中四个生长阶段三个位置不同的玉米叶片。结果表明，叶片总氮浓度（TLNC）与PNC有很强的相关性（R² = 0.91至0.98），单叶N浓度（SLNC）。TLNC和PNC受玉米生育期和氮肥施用量的影响，但不受土壤类型的影响。与播种后天数（DAS）参数结合使用时，经过修改的Dualex 4指数在整个生长阶段均显示与TLNC和PNC密切相关。改良的叶绿素浓度（mChl）和改良的N平衡指数（mNBI）都是PNC的可靠预测指标。通过仅使用从抽雄阶段（VT）之前新近完全展开的叶片和在VT阶段圆锥花序上方的叶片获得的信息来估计PNC，可以获得良好的结果。结论是，与DAS一起使用时，叶片荧光传感器（Dualex 4）可用于可靠地估计整个生育阶段的玉米PNC。