Proximal plant sensing with active canopy sensors offers a leap in the non-destructive assessment of crop agronomic information. For managing fertilizer nitrogen (N), sensor readings must be translated using functional models or algorithms to fertilizer amounts. Six field experiments were conducted in three wheat seasons in the West Nile Delta in Egypt to develop and validate an algorithm based on GreenSeeker canopy reflectance sensor for field-specific fertilizer N management in wheat, which takes into account both spatial and temporal variability of N during the crop growth season. The proposed algorithm is based on the prediction of total N uptake and response index of N uptake determined from normalized difference vegetation index measured by the sensor from plots differing in yield potential as established by applying a range of fertilizer N levels in the four experiments conducted in the first two wheat seasons. The treatments in the two experiments conducted in the third wheat season were designed to define appropriate fertilizer N management prior to applying a sensor-based dose at Feekes 6 stage (jointing stage). The application of 40 and 60 kg N ha−1 at 10 and 30 days after sowing of wheat and a sensor-guided dose of N estimated by using the algorithm developed in this study resulted in yields similar to those obtained by following the general recommendation, but with an average of 66 kg N ha−1 less fertilizer N. These results were also reflected in a substantial increase in N recovery (21.9%) and agronomic (7.7 kg grain kg−1 N) efficiencies compared with the general recommendation, thereby proving the usefulness of the sensor-based algorithm in optimizing fertilizer N management in wheat.

中文翻译：

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使用 GreenSeeker 近端光学传感器开发小麦氮肥优化算法

使用有源冠层传感器的近端植物传感为农作物农艺信息的无损评估提供了飞跃。为了管理肥料氮 (N)，必须使用功能模型或算法将传感器读数转换为肥料量。在埃及西尼罗河三角洲的三个小麦季节进行了 6 次田间试验，以开发和验证基于 GreenSeeker 冠层反射传感器的小麦田间特定肥料氮管理算法，该算法同时考虑了氮在小麦种植过程中的时空变化。作物生长季节。所提出的算法基于对总氮吸收和氮吸收响应指数的预测，该指数由传感器测量的归一化差异植被指数确定，该指数来自不同产量潜力的地块，这些地块是通过在四个实验中进行的施肥 N 水平建立的。前两个小麦季节。在第三个小麦季节进行的两个实验中的处理旨在确定适当的肥料 N 管理，然后在 Feekes 6 阶段（联合阶段）应用基于传感器的剂量。施用 40 和 60 kg N ha 在第三个小麦季节进行的两个实验中的处理旨在确定适当的肥料 N 管理，然后在 Feekes 6 阶段（联合阶段）应用基于传感器的剂量。施用 40 和 60 kg N ha 在第三个小麦季节进行的两个实验中的处理旨在确定适当的肥料 N 管理，然后在 Feekes 6 阶段（联合阶段）应用基于传感器的剂量。施用 40 和 60 kg N ha-1在小麦播种后 10 天和 30 天，使用本研究中开发的算法估算的传感器引导的 N 剂量产生的产量与遵循一般建议获得的产量相似，但平均为 66 kg N ha-1氮肥减少。这些结果也反映在氮回收率（21.9%）和农艺学（7.7 kg 谷物 kg-1N) 与一般建议相比的效率，从而证明了基于传感器的算法在优化小麦肥料 N 管理方面的有用性。