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Hail Imagery Research

A multi-year research project by Olds College Centre of Innovation (OCCI) shows that high-definition drone imaging can not only help adjusters to better classify hail damage within cropland, but could also potentially identify other types of crop damage. The project was launched in 2021 in collaboration with Agriculture Financial Services Corporation (AFSC).

Researchers in 2023 through to 2025 used drone imagery to pre-select points to scout within fields, navigate to each point, identify the types of damage and calculate the level of hail damage using AFSC adjustment protocols. With these datasets, spatial maps were created to represent the variability of measured hail and other types of damage within fields. 

Funders/Partners: AFS

Results

Since 2023, imagery and respective scouting datasets have been organized into a database that contains 25 fields totalling 3,733 acres. This dataset allows for continued validation and testing of emerging technology, such as machine learning models, that aim to digitally measure and analyze crop damage and determine its causes -- including fertilizers, pesticides, insects, rodents, wildlife or flooding.

Additional imagery and scouting data will be collected if any crops on the Olds College Smart Farm are significantly damaged by hail in 2026.

Three people conducting a hail damage survey in a canola field. They are using a GPS pole and tablets to collect data, standing among tall green plants with yellow flowers. The sky is overcast, and a tree line is visible in the background.

2025 AFSC Hail Survey

High Density Hail Scouting & Remote Sensing Data Collection

  • The annual collaboration between Olds College Centre of Innovation (OCCI) and AFSC uses drone imagery to identify locations within hail-damaged wheat, barley, and canola fields for targeted ground scouting. AFSC hail adjustment measurements, scouting documentation, and drone imagery are collected at predetermined locations to assess hail damage severity and characterize variability throughout each field.
  • Multiple drone flights using multispectral and thermal sensors, supported by ground control points for accurate spatial referencing, provide detailed imagery for selecting scouting locations and validating field-level damage. Ground observations include hail damage severity, crop staging, field notes, in-field photographs, and documentation of other damage such as lodging, drowned crops, seeding and spraying equipment errors, and wildlife damage.

View 2025 Fact Sheet

Past Survey Results

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    2024 AFSC Hail Survey

    High Density Scouting & Iterative Collection of Aerial Imagery for Damaged Fields

    • Collection of high resolution imagery of hail damaged fields by drones, paired with high density scouting, with scouting records documenting all forms of damage observed. The data collection enables the measurement and validation of field damage variability and severity assessments.
    • The use of DJI Mavic 300 paired with Zenmuse XT2 and MicaSense RedEdge-MX Dual Multispectral Sensor, as well as a  DJI Mavic 3 Multispectral (M3M) and DJI Mavic 3 Thermal (M3T) for drone imagery collection over hail damaged fields aided the team in determining field scouting locations.
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    2023 Hail Survey

    High Density Scouting & Collection of Aerial Imagery for Hail Damaged Fields

    • RGB drone imagery was beneficial to the scouters.
    • Satellite imagery is highly dependent on environmental conditions.
    • Differences in hail severity couldn't be visually distinguished using high resolution RGB drone imagery.
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    2022 Hail Survey

    Classification of Hail Damaged Areas using Drone Imagery

    Determining the feasibility of using drone imagery to classify hail damage within a field:

    • Red edge band of multispectral imagery seems to clearly distinguish all damaged areas of a field.
    • GIS tools quickly calculated the area of the damage within a field once the classification is completed.
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    2021 Proof of Concept

    Hail Damage Classification in Barley using Drone Imagery

    Exploring if drone imagery can be used as a tool to classify hail damaged versus undamaged areas within a crop.

    • Initial results of this proof of concept are encouraging; more study is recommended.

Explore Research Projects