Office for Technology Commercialization
http://www.research.umn.edu/techcomm
612-624-0550

Predicting Crop Nitrogen Status with Remote Sensing

Technology #20180415

Questions about this technology? Ask a Technology Manager

Download Printable PDF

Image Gallery
Remote sensing of nitrogen status in potato cropsPotato fieldPredicting Crop Nitrogen Status (CNS)
Categories
Researchers
Carl Rosen, PhD
Department Head and Professor, CFANS Soil, Water & Climate
External Link (www.swac.umn.edu)
David Mulla, PhD
Professor, CFANS Soil, Water & Climate
External Link (www.swac.umn.edu)
Yuxin Miao, PhD
Assistant Professor of Precision Agriculture and Nutrient Management and Associate Director of Precision Agriculture Center, CFANS Soil, Water & Climate
External Link (www.swac.umn.edu)
Managed By
Kevin Nickels
Technology Licensing Officer 612-625-7289
Patent Protection

Provisional Patent Application Filed

In-season prediction of crop nitrogen status

This new technology can estimate CNS (crop nitrogen status) using remote sensing and the nitrogen nutrition index (NNI). These estimates produce actionable insights on the optimum nitrogen fertilizer use efficiency to maximize agronomic production. This new system helps forecast end-of-season crop yield and quality. For example, remote sensing based CNS measurements on a given date can forecast end-of-season crop yield and quality for a given field—and within a field. While this precision agriculture technology was developed for irrigated potato cropping systems, it could be adopted for use in other high-value agronomic or horticultural crops.

Precision nitrogen applications for intensively managed crops

Remote sensing offers superior temporal and spatial resolution that can supplement or even replace existing methods for managing in-season nitrogen applications. While the adoption of remote sensing in precision agriculture is rapidly accelerating, no system to date can directly and accurately determine crop nitrogen status (CNS) from remote sensing alone. Previous methods have relied on imagery to directly predict CNS. However, these methods lack accuracy and may require an in-field reference strip, which is logistically challenging. Remote sensing, on the other hand, can more accurately predict both CNS as well as other parameters (e.g., canopy cover or above ground nitrogen concentration).

Phase of Development

  • Proof of concept. Algorithm developed and tested.

Benefits

  • Optimizes agronomic production and maximizes fertilizer use efficiency
  • Predicts end of season crop yield and quality based on crop nitrogen status
  • Increases temporal and spatial resolution compared to existing methods

Features

  • Precision agriculture solution for intensively managed crops
  • Predicts crop nitrogen status and in-season fertilizer requirement
  • Remote sensing combined with the nitrogen nutrition index
  • Produces actionable insights on the optimum nitrogen fertilizer rate
  • Assists producers in adaptively managing in-season nitrogen

Applications

  • Precision agriculture
  • In-season nitrogen fertilizer applications
  • Yield quantity and quality forecasting
  • High-value agronomic and horticultural crops, crops grown on sandy soils vulnerable to nutrient losses, and irrigated cropping systems


Interested in Licensing?
The University relies on industry partners to further develop and ultimately commercialize this technology. The license is for the sale, manufacture or use of products claimed by the patents. Please contact Kevin Nickels to share your business needs and licensing and technical interest in this technology.