Combining leaf fluorescence and active canopy reflectance sensing technologies to diagnose maize nitrogen status across growth stages
Combining leaf fluorescence and active canopy reflectance sensing technologies to diagnose maize nitrogen status across growth stages
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DOI：
10.1007/s11119-021-09869-w
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发表时间：
2022-01
影响因子：
6.2
通讯作者：
R. Dong;Y. Miao;Xinbing Wang;Fei Yuan;K. Kusnierek
中科院分区：
文献类型：
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作者：
R. Dong;Y. Miao;Xinbing Wang;Fei Yuan;K. Kusnierek
能够进行无损作物监测的快速方法对于准确的当季氮 (N) 状态评估和精确的氮管理至关重要。本文的目的是 (1) 比较叶片荧光传感器 Dualex 4 和主动冠层反射传感器 Crop Circle ACS-430 的性能，用于估算玉米 (Zea maysL.) 各个生长阶段的氮状态指标； （2）利用在早期生长阶段从冠层传感器获取的反射率参数来评估跨生长阶段氮状态预测的潜力； (3) 研究结合主动冠层传感器和叶片荧光传感器数据使用跨生长阶段的通用模型间接估计氮营养指数 (NNI) 的前景。结果表明，两个传感器的数据在各个阶段都与 NNI 密切相关。然而，使用直接NNI估计方法，在测试的指标中，基于Kappa统计，只有氮平衡指数（NBI）能够令人满意地诊断氮状况。通过纳入播种后天数的信息，减少了生长阶段对近端传感的影响。研究发现，叶片荧光传感器在估计植物氮浓度方面表现相对较好，而冠层反射传感器在估计地上生物量方面表现较好。它们的组合显着提高了 N 诊断（包括 NNI 预测）的可靠性。此外，该研究证实，可以通过使用早期的冠层反射测量来预测后期的地上生物量来评估氮状况。此外，经验证，综合 NBI 是比叶绿素浓度指数更稳健、更灵敏的氮状态指标。结论是，将早期生长阶段（例如 V8）的主动冠层传感器数据与播种后几天修改的叶片荧光传感器数据相结合，可以提高玉米整个生长阶段氮状态诊断的准确性。
Rapid methods allowing for non-destructive crop monitoring are imperative for accurate in-season nitrogen (N) status assessment and precision N management. The objectives of this paper were to (1) compare the performance of a leaf fluorescence sensor Dualex 4 and an active canopy reflectance sensor Crop Circle ACS-430 for estimating maize (Zea maysL.) N status indicators across growth stages; (2) evaluate the potential of N status prediction across growth stages using the reflectance parameters acquired from the canopy sensor at an early growth stage; and, (3) investigate the prospect of combining the active canopy sensor and leaf fluorescence sensor data to estimate N nutrition index (NNI) indirectly using a general model across growth stages. The results indicated that data from both sensors were closely related to NNI across stages. However, using the direct NNI estimation method, among the tested indices, only the N balance index (NBI) could diagnose N status satisfactorily, based on the Kappa statistics. The effect of growth stages on proximal sensing was reduced by incorporating the information of days after sowing. It was found that the leaf fluorescence sensor performed relatively better in estimating plant N concentration whereas the canopy reflectance sensor performed better in aboveground biomass estimation. Their combination significantly improved the reliability of N diagnosis, including NNI prediction. In addition, the study confirmed that N status can be assessed by predicting aboveground biomass at the later stages using the canopy reflectance measurements at an early stage. Furthermore, the integrated NBI was verified to be a more robust and sensitive N status indicator than the chlorophyll concentration index. It is concluded that combining active canopy sensor data, of an early growth stage (e.g. V8), with leaf fluorescence sensor data, modified using days after sowing, can improve the accuracy of corn N status diagnosis across growth stages.