Combining leaf fluorescence and active canopy reflectance sensing technologies to diagnose maize nitrogen status across growth stages

Rui Dong, Yuxin Miao, Xinbing Wang, Fei Yuan, Krzysztof Kusnierek

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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 mays L.) 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.

Original languageEnglish (US)
Pages (from-to)939-960
Number of pages22
JournalPrecision Agriculture
Volume23
Issue number3
DOIs
StatePublished - Jun 2022

Bibliographical note

Funding Information:
This study was supported by the Norwegian Ministry of Foreign Affairs (SINOGRAIN II, CHN-17/0019), the UK Biotechnology and Biological Sciences Research Council (BB/P004555/1), Minnesota Department of Agriculture/Agricultural Fertilizer Research and Education Council (MDA/AFREC R2020-32, R2021-32), the USDA National Institute of Food and Agriculture (State project 1016571).

Funding Information:
This study was supported by the Norwegian Ministry of Foreign Affairs (SINOGRAIN II, CHN-17/0019), the UK Biotechnology and Biological Sciences Research Council (BB/P004555/1), Minnesota Department of Agriculture/Agricultural Fertilizer Research and Education Council (MDA/AFREC R2020-32, R2021-32), the USDA National Institute of Food and Agriculture (State project 1016571).

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Fluorescence sensing
  • Integrated sensing
  • Nitrogen nutrition index
  • Nitrogen status diagnosis
  • Reflectance sensing

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