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Utilizing drones, machine learning and artificial intelligence to optimize nitrogen credits from a red clover cover crop

Principal Investigator: Jordan Sinclair

Research Institution: Veritas

Timeline: April 2019 – December 2022   

Objectives:

  • Determine how to utilize Remote Piloted Aerial System (RPAS) / Unmanned aerial system (UAS) to measure biomass at the field level and incorporating spatial variability.
  • Determine if clover plant counts and biomass can be categorized into production groups that correlate to consistent nitrogen credits for the following corn crop.
  • Determine if the nitrogen credit for the following crop is consistent enough to utilize for variable rate nitrogen applications.

Impacts:

  • By understanding how nitrogen accumulation correlates to clover count and/or biomass we will be able to inform policy and best practices at a provincial level.
  • The development of machine learning (ML) and artificial intelligence (AI) algorithms that can determine nitrogen credits from clover will allow farmers to confidently estimate and fully realize nitrogen credits on a case by case basis.
  • UAS technology allows for a geospatial attribute to the nitrogen credit which can be translated into variable rate nitrogen prescriptions, optimizing the 4R philosophy.

Scientific Summary:

For 35+ years double cut red clover (DCRC) has been considered the “gold standard” cover crop in winter wheat. There has been extensive research conducted that demonstrates the benefit of adding DCRC into Ontario crop rotations, including calculations of the nitrogen credit DCRC can supply as a legume to the following corn crop. Additionally, research has determined that the amount of time that the DCRC is allowed to grow prior to termination has a strong correlation to the amount of nitrogen that can be credited. What is not fully understood is if the value of the nitrogen credit should be related to DCRC biomass, the number of DCRC plants, or a combination of these variables and how to effectively incorporate spatial variability of DCRC growth into the credit process. As a result, current practices necessitate a conservative approach when assigning nitrogen credits from DCRC.

Over the past several years, Veritas has utilized UAS technology to determine crop characteristics. In 2018, we combined UAS technology with artificial intelligence and machine learning to identify and count weeds and crops at the species level. This drone technology now has the potential to classify clover plants and measure biomass at the zone level within a field. Nitrogen credits can then be geospatially validated using a pre-side dress nitrogen test (PSNT). The results of PSNT can then be used to create a variable rate nitrogen prescription for the corn crop. The data generated from the nitrogen prescription application equipment and the yield data from combine yield monitors can then be used to validate any response from the nitrogen credit.

External Funding Partners:

Veritas

Green Eye Technologies

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