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01Environment-independent cyber agriculture and intelligent farming

Technology-enabled, geographically dispersed, environmentally sustainable food production

Introduction

Agriculture today
Agriculture continues to profoundly impact our environment and resources as we cultivate, process and distribute across the planet. The challenge agriculture faces over the next few years is to feed a growing, increasingly urban population while dramatically decreasing its ecological footprint - in short, to grow more with less impact.

What will change
Emerging technologies such as connected sensors, machine learning and automation accelerate the development of a new kind of environment-independent agriculture. Instead of a global supply chain distributing mono crop harvests from distant farms, soil-free vertical farms dispersed throughout urban environments will shorten growing times and delivery distances while using a fraction of the land, water, and soil of traditional farms.

Emerging technologies to watch
Detective sensors will collect data from plants and feed this into systems monitoring production. This information will be collected from decentralised farms into centralised services that can learn from the results and share back learnings. Modern farming techniques such as aeroponics, aquaponics and hydroponics will mean crops can be grown anywhere, bringing production closer to populations and improving food security.

Opportunities to consider
9m2 of vertical farming produces harvests similar to 280m2 of horizontal farming at an astonishing yield rate of 95% - traditional farming averages a yield rate of 55%. These new approaches have the potential to meet food demands in all climates while shrinking land usage by 99% and using 95% less water. Corporate VCs such as Google Ventures & Softbank are already investing in this area. Technology and intelligent solutions will be an enormous business opportunity as the digitalisation of agriculture starts to accelerate.

This is where the open-minded, the visionaries, the rebels and disruptors design new digital customer experiences for the world to come. Are you one of us?

Opportunities
  • Sustenance

    LED vertical farming units can enable locally automated food preparation, impacting production value and shortening supply chains.

  • Logistics

    Shorter supply chains shape logistics, pushing last mile deliveries.

  • Energy supply

    Distributed vertical farm networks will encourage localised renewable energy production.

Why is this relevant

Total market 2017

11780 Billion USD

Look for these 2024

1) Smart agriculture
$20,6 bn / CAGR: 15% (modern innovative technologies in soil analysis, crop management, cultivation and harvesting of crops)

2) Vertical farming
$8,6 bn / CAGR: 21% (aero, hydro, aquaponics including lighting, control, hardware)

Top 18 unicorn sniffing smart money VCs, 1376 investments 2018-2020

out of 1376 investments by top Smart Money VCs, 3% of startups define sustainability, and 2% mention farming in their company descriptions, and 7% involved in biotechnology. (From Crunchbase pro, 02/20)

Special focus

Leveraging new hardware and software technology with sensors to monitor and automise optimal plant care-taking with fertilisers, light, air quality, and other parameters. Collaborating and competing with new players entering from surprising areas.

Things to consider
Start-ups
Technologies that will enable the shift
  • Lighting

    LED (light-emitting diodes) lights are electric lights with a longer lifespan than other forms of lamps. By adjusting the wavelengths to be suitable for plants, LEDs have paved the way for profitable indoor farming. The next step in lighting will be lasers for plants, which are even more efficient than modern LED growth lights.

  • Robotics

    Both partially and wholly autonomous robots in applications such as logistics, storage, and industrial settings. Robotics Automation could be used for harvesting and transporting plants in vertical farms, making it more safe for humans.

  • AI/ML

    Artificial intelligence (AI): computer systems that accomplish goals in some task(s). It is not a single thing; it is a combination of technologies such as machine learning. Machine Learning (ML): toolbox of algorithms and techniques that learn rules from data. Used to implement AI in narrow tasks. Not magic; learns from the data by minimising a cost function.

Bet 02
Get in touch with our team
  • Tuğberk Duman

    Business Consultant

    +358503266121

    tugberk.duman@futurice.com

  • Niina Uusi-Autti

    VP, Emerging Business

    +358 40 546 2335‬

    niina.uusi-autti@futurice.com

Get in touch

You can find individual contact details for the friendly folk above.

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