About Me


I'm a graduate student passionate about sustaining agricultural productivity.
My work focuses on evaluating remote sensing and other precision agriculture tools as a means to reduce fertilizer loss and keep our soil and water resources clean and productive.

I believe there is work to be done to make the implementation of the 4Rs of nutrient stewardship (i.e., Right source, Right rate, Right time, and Right Place) easier and more effective for growers and other stakeholders. As a society, we must be more proactive in establishing a balance among the economic, environmental, and social ecosystems that are influenced by agriculture. My work involves conducting research and developing tools to establish a working balance among these ecosystems.

Projects


Here you'll find a sample of some of my past and current projects. Please get in touch if you have questions, feedback, or would like to just start a discussion!


HS-Process.png
HS-Process Python Package
An open-source Python package for geospatial processing of aerial hyperspectral imagery
19 February 2020

HS-Process is an open-source Python package for geospatial processing of aerial hyperspectral imagery. It emphasizes the ability to batch process datacubes, with the overall goal of keeping the processing pipeline as “hands-off” as possible.

Please see the HS-Process Documention/API for more information.

Feel free to reach out if you have questions or would like help getting started! We have a paper using this package to be published soon...
EONR.png
EONR Python Package
A Python tool for computing the optimum nitrogen rate and its confidence intervals from agricultural research data
30 March 2019

EONR is a Python package for computing the economic optimum nitrogen fertilizer rate using data from agronomic field trials under economic conditions defined by the user (i.e., grain price and fertilizer cost), and it can be used for any crop (e.g., corn, wheat, potatoes, etc.).

Please see the following for more information:
1) EONR Documention/API
2) Methodology and use case in Computers and Electronics in Agriculture (peer-reviewed)

Feel free to reach out if you'd like help getting started! I think you'll find it rather easy to use, even if you're just beginning with Python.
Poster_ASA2018_Nigon.pdf
ASA International Conference, Baltimore, MD - Nigon et al. (2018)
Independent calibration of the EPIC model using in-season aerial imagery
5 November 2018

I presented this poster at the Agronomy and Crops International Annual Meetings in Baltimore, MD.
  • I used remote sensing to estimate plant biomass at the early growth stages.
  • Using the estimated biomass from remote sensing, I performed a calibration on the EPIC model by fitting the EPIC leaf development parameters.
  • Results indicate that this method provides an opportunity to account for within-field spatial variability to make improved in-season nitrogen fertilizer decisions.
  • Future work will focus more attention on validating simulation results with ground truth measurements, including nitrogen uptake and grain yield.
  • 1st place recognition in the graduate student poster competition (of 7 posters).
More Projects
Poster_NCEISFC2017_Nigon.pdf
47th North Central Extension-Industry Soil Fertility Conference, Des Moines, IA - Nigon et al. (2017)
Active and passive spectral sensing for predicting the optimum nitrogen rate and timing in corn
15 - 16 November 2017

I presented this poster at the North Central Extension-Industry Soil Fertility Conference organized by the International Plant Nutrition Institute in Des Moines, IA.
  • I looked at the ability of remote sensing to predict early season corn nitrogen uptake.
  • Results indicate that remote sensing can predict nitrogen uptake reasonably well by the V10 growth stage (when crop height is ~3'), but sensor type and calibration are important considerations.
  • Until there is an economic value placed on pollution caused by N fertilizer loss, it is difficult to justify precision N applications from an economic perspective.
  • 2nd place recognition in the graduate student poster competition (of 29 posters).
ASA2017_Biomass.pdf
SSSA/ASA International Conference, Tampa, FL - Nigon et al. (2017)
Spectral imagery to estimate leaf area index and above-ground biomass in maize
23 October 2017

This is a poster I presented at the Agronomy, Crops, and Soils International Annual Meetings in Tampa, FL.
  • I looked at the relationships among maize height, above-ground biomass, leaf area index, and spectral reflectance during early growth stages (V5 - V10).
  • The approach used is a viable option for reliably informing remote sensing algorithms and crop models.
  • However, variability of such estimations should be considered when interpreting final precision nitrogen fertilizer recommendations.
  • 3rd place recognition in the graduate student poster competition (of 16 posters).
Weather station
Building a low-cost weather station that publishes data to the internet in real-time
April - June 2017

I built a weather station that measures rainfall, temperature, humidity, soil moisture, wind speed and gust, then publishes data to the cloud over a 3G network via the Particle Electron microcontroller. This was my first microcontroller project and was certainly a learning experience for me. See how I built it!











TetraPy_ASAPoster2016.pdf
SSSA/ASA International Conference, Phoenix, AZ - Nigon et al. (2016)
Tetrapy: A Python package for cleaning and preprocessing Tetracam multispectral imagery
May - November 2016

This is a poster describing a Python project that allows Tetracam users to process their aerial imagery.
  • Decodes RAW images, vignetting, and radial distortion correction.






  • Tour of Nigon-View Dairy
    Tour of Nigon-View Dairy - where I grew up and my parents currently operate their dairy farm
    Fall 2015

    I made a story map for a Web GIS course I completed. Story maps allow you to combine maps and geographic information with narrative text and photos to tell a story.






    TrackingPotatoN_ASAPoster2012.pdf
    SSSA/ASA International Conference, Cincinnati, OH - Nigon et al. (2012)
    Plant-based approaches for in-season detection of nitrogen stress in potato
    24 October 2012

    This is a poster I presented at the Agronomy, Crops, and Soils International Annual Meetings in Cincinnati.
    • Potato tissue nitrogen (i.e., petioles and/or leaflets) change throughout the growing season.
    • Total nitrogen and nitrate-nitrogen were correlated to non-invasive remote sensing techniques.
    • Results demonstrate the ability of remote sensing to predict nitrogen uptake in potato.
    HyperspecThermalImagery_NCEISFCPoster2011.pdf
    41st North Central Extension-Industry Soil Fertility Conference, Des Moines, IA - Nigon et al. (2011)
    Hyperspectral and thermal imagery for the detection of nitrogen and water stress
    16-17 November 2011

    This is a poster I presented at the 41st North Central Extension-Industry Soil Fertility Conference in Des Moines, IA.
    • The main objective of this research was to evaluate the ability of canopy level hyperspectral and thermal imagery to detect nitrogen and water status of a potato crop.
    • The specific wavelengths and/or spectral indices that best predict nitrogen stress are highlighted.