Leveraging Multispectral Imagery for Fertigation Timing Recommendations Through the N-Time Automated Decision Support System

Jackson S. Stansell, Joe D. Luck, Tyler G. Smith, Hongfeng Yu, Daran R. Rudnick, Brian T. Krienke

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Multispectral imagery captured from aerial robots, aircraft, and satellites provides data that may be leveraged to quantify crop nitrogen (N) status. While multispectral imagery is commercially accessible, there are few systems designed to transform multispectral imagery into actionable recommendations and prescriptions for farmers. N-Time Fertigation Management System (N-Time) was developed as an autonomous software decision support system (DSS) that analyzed multispectral imagery and applied a N recommendation algorithm to produce a binary action recommendation and accompanying prescription for an imminent fertilizer application via a center pivot irrigation system (fertigation). N-Time FMS automated the sensor-based fertigation management framework (SBFM) which utilized multispectral images of the crop canopy to inform the number and timing of fertigation applications made throughout the growing season. The framework involved a frequent (i.e. weekly) monitoring and recommendation cycle for which manual execution of computational processes for a single field required more than two hours. N-Time reduced the time requirement per field including user interaction to 7.4 minutes for 12 cm/pixel resolution multispectral imagery (i.e. UAV captured) and 3.1 min for 3 m/pixel multispectral imagery (i.e. satellite captured). N-Time also demonstrated greater than 99% agreement with computational outputs produced through manual execution with non-specialized software. During 2020 and 2021, N-Time was used to implement SBFM in research trials on approximately 320 ha of production-scale irrigated grain maize. These trials demonstrated that SBFM produced higher nitrogen use efficiency than the grower's best management in 94% of implementations and higher profitability than the grower's best management in 59% of implementations.

Original languageEnglish (US)
Title of host publicationAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII
EditorsJ. Alex Thomasson, Alfonso F. Torres-Rua
PublisherSPIE
ISBN (Electronic)9781510651043
DOIs
StatePublished - 2022
EventAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII 2022 - Virtual, Online
Duration: Jun 6 2022Jun 12 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12114
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII 2022
CityVirtual, Online
Period6/6/226/12/22

Keywords

  • Fertigation
  • N-Time
  • automation
  • decision support
  • multispectral imagery
  • nitrogen use efficiency
  • sensor-based fertigation management

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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