Industries

Agritech

Drivers and challenges for The AgriTech future

Epik is taking a fresh look at the Drivers and Challenges facing Agriculture in the 21st century. One that is focused on the data and the associated “pipework” required in the Integrated application of Yield AI, Water management AI, and Greenhouse Gas (GHG) AI. Implemented on Edge Infrastructure and underpinned by Blockchain Traceability. Epik is “Making Data Work” for our clients.

AI for agritech

  • Challenges

    Farmers must interpret data.

  • Challenges

    Dependency on human Agronomists.

  • Challenges

    Distrust of technology.

  • Driver

    Predictive models to maximize effectiveness and minimize risk.

  • Driver

    Prescriptive recommendations to affect outcome.

  • Driver

    Automation of farming operations.

Yield

Yield

  • Challenges

    Data authenticity – manual entry vs. automated collection.

  • Challenges

    Growing demand for food.

  • Challenges

    Data overload.

  • Driver

    Improve the quality, quantity, and health of crops, animals, and seafood.

  • Driver

    4Rs—Right rate, Right timing, Right source, and Right placement.

  • Driver

    Commerce data enabler of cost/benefit decisioning.

Water

Water Management

  • Challenges

    Weather unpredictability due to climate change.

  • Challenges

    Water scarcity.

  • Challenges

    Water waste.

  • Driver

    Irrigation automation.

  • Driver

    Hyper-local sensor/imaging data.

  • Driver

    Water use efficiency.

GHG

ghg

  • Challenges

    Agriculture’s substantial CO2e driving climate change.

  • Challenges

    Data uncertainty. Model Uncertainty. Scenario Uncertainty.

  • Challenges

    Poor quality, scattered, or outdated, region-limited, datasets and models.

  • Driver

    Carbon Neutrality and Carbon Credits.

  • Driver

    Carbon Footprint labeling.

  • Driver

    Turning captured sustainability metrics into intelligible, actionable, data.

Traceability

  • Challenges

    Data authenticity – manual entry vs. automated collection.

  • Challenges

    Farmer accessibility/participation.

  • Challenges

    Regulation.

  • Driver

    New generation of consumers want to be able to trace their food back to its source.

  • Driver

    Food Safety.

  • Driver

    Farmers looking to use data to increase their role in the value chain.

Edge

  • Challenges

    Data accuracy and security.

  • Challenges

    Data privacy concerns.

  • Challenges

    Connectivity and cost.

  • Driver

    Collecting and accessing data from equipment and sensors in the field.

  • Driver

    Micro-change detection on a zone-by-zone basis.

  • Driver

    IoT.

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