Corn nitrogen status diagnosis with an innovative multi-parameter crop circle phenom sensing system

Cadan Cummings, Yuxin Miao, Gabriel Dias Paiao, Shujiang Kang, Fabián G. Fernández

Research output: Contribution to journalArticlepeer-review

Abstract

Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha−1 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (∆Temp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.

Original languageEnglish (US)
Article number401
Pages (from-to)1-18
Number of pages18
JournalRemote Sensing
Volume13
Issue number3
DOIs
StatePublished - Jan 2021

Bibliographical note

Funding Information:
Funding: This research was funded by the Minnesota Department of Agriculture/Agricultural Fertilizer Research and Education Council (MDA/AFREC R2018-25, R2019-20 and R2020-32), the Minnesota Soybean Research and Promotion Council (project 00071830), the USDA National Institute of Food and Agriculture (State project 1016571), and the Startup Fund.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Active canopy sensing
  • Integrated sensing system
  • Machine learning
  • Nitrogen nutrition index
  • Precision nitrogen management

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