SustaiN: A Decision Support System for Sustainable Nitrogen Management in Corn and Sorghum using Satellite Remote Sensing

Progress report for GNC22-343

Project Type: Graduate Student
Funds awarded in 2022: $14,966.00
Projected End Date: 06/30/2024
Grant Recipient: Saint Louis University
Region: North Central
State: Missouri
Graduate Student:
Faculty Advisor:
Dr. Vasit Sagan
Saint Louis University
Faculty Advisor:
Dr. Andrea Eveland
Donald Danforth Plant Science Center
Dr. Todd Mockler
Donald Danforth Plant Science Center
Dr. Stephen Moose
University of Illinois Urbana-Champaign
Dr. Nadia Shakoor
Donald Danforth Plant Science Center
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Project Information

Summary:

SustaiN seeks to address the pressing issue of excessive Nitrogen (N) use in corn and sorghum production, which can be a significant financial burden for farmers and pose environmental risks. To provide a viable solution, we propose utilizing high-resolution satellite remote sensing to develop an interactive web-based decision support system (DSS) that visualizes the N-status of crops in near real-time. We are collaborating with our partners at the Donald Danforth Plant Science Center and the University of Illinois to establish experimental fields with variable N rates in plots of corn and sorghum. During the growing season of 2023, we will use a hyperspectral sensor mounted on an unmanned aerial vehicle (UAV) to collect remote sensing data and create an N-status index that can explain N-variability. We will then apply this index to PlanetScope (PS) satellite images and integrate the results into a fully interactive and user-friendly web based DSS.

Our research approach will involve the development of a suitable N-status index that can accurately explain N-variability from crop canopies. Drawing on our previous experiments conducted in 2021 and 2022, we have been able to create a N-status index from UAV data that is well-suited for the PS satellite. To ensure accessibility, we have established a dedicated project website (https://sustaincrops.net/) that allows farmers to easily subscribe and log in to the system. Our DSS allows users to input their field's location and desired time of data visualization, which provides a comprehensive N-status map highlighting areas of N-variability that can reduce costs for in-season N-application. In addition, we are developing educational videos and materials to help farmers understand how remote sensing can be integrated into high-throughput decision-making for their farming. The beta version of our web-based app is already running in the cloud, and we are excited to see how our project will benefit farmers by providing access to vital information that can inform their management decisions.

As the project is still in its early stages of development, we will continue to collect more data from the upcoming season. To promote wider adoption of SustaiN, we will reach out to farmers through social media and farmers' associations to encourage subscription. Our final report will include a more detailed research conclusion and a summary of the farmer adoption actions that resulted from the education program.

Project Objectives:

Learning outcomes

The learning outcomes will be shared to farmers and researchers by peer-reviewed journal publication and hosting the results in a project website.

  1. Farmers will learn how satellite data can explain in-season N-variability for corn and sorghum and gain insight into any environmental stress that may have affected overall N-uptake.
  2. The project will provide information on which corn and sorghum genotypes have better nitrogen use efficiency in different environmental conditions.
  3. Researchers will have access to unique crop datasets and project source codes that will enable them to reproduce the research outcomes and the DSS.

Action outcomes

  1. Farmers will make informed decisions about optimal in-season N-requirement without relying on drone-based flights or ground-based sensors, leading to increased profitability and reduced operational costs while improving the overall soil quality of their fields.
  2. Based on the knowledge of which corn and sorghum genotypes efficiently utilize N in specific environmental conditions, farmers can make informed decisions when selecting the appropriate genotype for future cultivation.

Cooperators

Click linked name(s) to expand/collapse or show everyone's info
  • Dr. Nadia Shakoor (Educator and Researcher)
  • Megan Dwyer (Researcher)
  • Dr. Stephen Moose (Educator and Researcher)

Research

Materials and methods:

The overall methodological framework for this study is illustrated in Figure 1.

Overall process of the proposed methodology.
Figure 1: Overall process of the proposed methodology which comprises of two major tasks, i.e., N-Status index identification and development of the Decision Support System (DSS)

N-Status index development

Experimental sites

The goal of this project is to develop a suitable N-status index to accurately explain N-variability from crop canopies. Our collaboration with the Donald Danforth Plant Science Center granted us access to their corn experimental sites in Ofallon, MO in 2021 and 2022. These experimental fields consisted of two corn fields, each established with around 50 distinct genotypes. Each field included a 3m by 2m plot with two rows of corn. Additionally, we will have access to the experimental sites of corn and sorghum in Fischer Farm, MO, for the 2023 growing season. Our collaborators will establish several variable N-treatment plots to understand the N-variability from the analysis.

Data collection

UAV data collection

We collected UAV-hyperspectral data above the experimental fields in 2021 and 2022 using the Headwall NanoHyperspec 12 mm push-broom sensor mounted on the DJI Matrice 600 Pro hexacopter. This sensor provides high spatial and spectral resolution data across the very near-infrared region (400 nm to 1000 nm). We selected the hyperspectral sensor for our study since the new PS satellite has 8 bands and the UAV hyperspectral bands can be easily matched with the PS data. Table 1 provides the dates of data collection from different fields and corresponding sample data information.

Table 1: Data collection dates in the 2021 and 2022 season

Date Number of ground truth samples collected
July 20, 2021 50
August 04, 2021 50
August 11, 2022 100

Nitrogen data collection

To validate the different spectral features from remote sensing data and crop nitrogen status, we measured the amount of nitrogen in the plant leaves using the Dualex Scientific 4 instrument. This instrument accurately measures the Nitrogen Balance Index, an indicator of overall nitrogen status in plant leaves. We randomly selected different sample plots in the same UAV data collection dates and collected four Dualex readings from well-sunlit leaves. We then averaged the four sample readings and corresponded them to the plot average N-status.

Spectral sensitivity analysis

To evaluate the spectral response to N-variability, we selected 8 channels that best represented the wavelength of PS scenes and calculated different spectral indices based on the combination of these bands. We computed the difference index (DI), ratio index (RI), and normalized difference spectral index (NDSI) for all pairwise combinations of the bands using the following equations:

DI = ρa - ρb

RI = ρa / ρb

NDSI = (ρa - ρb)/(ρa + ρb)

where, ρ is the reflectance of wavelengths a and b. To identify the potential N-Status index for the decision support system (DSS), we calculated Pearson's correlation coefficient between the spectral indices and the corresponding NBI data collected from the field. This analysis allowed us to identify the most promising spectral indices for accurately determining N-variability and developing the N-Status index for our DSS.

Decision support system

The decision support system (DSS) for SustaiN was developed using Python and utilizes different frontend and backend technologies. The frontend of the DSS provides an interface for user interaction, while the backend is the actual computation platform that performs all the calculations.

PlanetScope (PS) images

The key idea behind the DSS is the automated download and processing of PS images. The PS images have eight narrowband channels (Table 2) available from August 2021, providing high spatial resolution data (3-m) that allows researchers to monitor crops more accurately. PS images are collected by a constellation of approximately 185 mini satellites known as Planet Doves that together collect images of the entire earth almost daily. However, the use of daily images is often hindered by cloudy conditions during data collection. Still, PS offers one of the best solutions specifically for crop-related studies.

Bands Wavelength (nm)
Band 1 (Coastal Blue) 431 - 452
Band 2 (Blue) 465 - 515
Band 3 (Green I) 513 - 549
Band 4 (Green II) 547 - 583
Band 5 (Yellow) 600 - 620
Band 6 (Red) 650 - 680
Band 7 (Red Edge) 697 - 713
Band 8 (Near Infrared) 845 -885

Access to the DSS

Access to the DSS is limited to registered farmers and researchers only. To register, farmers can fill out a questionnaire available on the project website. Based on their answers, the user will receive login information via email (login ID and a randomly generated password). Each time the user intends to access the DSS, the login credentials are required. The user IDs and passwords are securely hosted in the database services provided by deta.sh. Since this project is a pilot approach to democratize remote sensing data, open access for everyone is out of the scope of this project.

Frontend

The frontend of the DSS is built using the Streamlit framework and provides an interface for user interaction (Figure 2). Once logged in, users provide two major pieces of information: 1) a boundary defining the location of their intended field or area of interest (AOI), and 2) the date for which the information is requested. The boundary can be drawn on an interactive map powered by the Folium maps. An address finder in the top right corner of the map helps pinpoint the location of the farmer's field, powered by the Open Street Map. The boundary must be less than 5 square kilometers; otherwise, the process cannot be started. The date is limited to the years 2022 and 2023, so the focus can be given only to recent farming practices. Once the two inputs are defined by the user, the process can be submitted, and the backend of the application handles the rest of the work.

Interface of the DSS
Figure 2: Frontend interface of the DSS that explains different features of the application.

Backend

The backend of the DSS was developed using a combination of open-source geospatial libraries in Python, enabling seamless integration with the frontend of the application. Upon receiving the user's inputs, i.e., geometry and date, the backend uses the Planet API and Python to automatically identify suitable PS satellite images by filtering out images that do not meet the user's criteria. The API searches for images within a 15-day timeframe from the user-provided date and selects the best image based on visibility percentage provided by the PS satellite, which is calculated based on the availability of cloud and haze within each scene. It should be noted that the highest visibility score does not guarantee that the requested geometry will not contain any cloud or haze.

After downloading the image, the backend calculates two vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the N-status index developed earlier, from the PS image. The NDVI is commonly used to highlight healthy vegetation from remote sensing imagery and can be used to mask out unwanted objects within the user-defined geometry, such as roads, buildings, waterbodies, and forests. However, the range of NDVI values for healthy vegetation can vary depending on the specific crop and environmental conditions. Therefore, in the results section, the user can dynamically adjust the NDVI range to mask out unwanted objects within the result.

Using the adjusted NDVI range, input geometry, and date, the backend generates an interactive map of the N-Index overlayed on top of the basemap imagery. The user can toggle between the generated map to highlight major N-deficient areas suggested by the N-Index. The user can also interactively adjust the NDVI range to adjust the number of unwanted pixels within the AOI. Finally, the information can be downloaded as an HTML page in the local drive by clicking the download button. The output will include the interactive map along with other metadata information.

Cloud integration

The DSS is hosted within the Heroku framework, which is a popular cloud platform that allows easy deployment of web applications. The Heroku platform provides a wide range of services, such as automated deployment, scalability, and database integration. Therefore, when the traffic increases for the application, we can easily scale it up to match the demand. In addition, the Heroku platform provides built-in monitoring and logging services, which allow for easy identification and resolution of any issues that may arise during the deployment and operation of the DSS.

Participation Summary

Educational & Outreach Activities

1 Consultations
1 Curricula, factsheets or educational tools

Participation Summary:

Education/outreach description:

The project is currently in its nascent stage, and as a result, we have not yet been able to conduct extensive outreach efforts. However, the project website (https://sustaincrops.net/) contains information on how to participate in the project and use the DSS. We have already reached out to our collaborators from the Donald Danforth Plant Science Center, Sorghum Checkoff, and the Illinois Corn Growers Association. It is likely that farmers will subscribe once the application is fully developed. We anticipate that the application will be released by the end of March 2023. Following the application's release, we plan to use social media and our partners' networks to reach out to interested farmers and encourage them to subscribe. The application will include at least two YouTube videos that demonstrate how to use SustaiN to make informed decisions and how satellite remote sensing works in the background of the application.

Information Products

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.