Precision agriculture: part sweat, part smartphone

Technology evolution and agriculture have been closely entwined for millennia and continue to be so as we seek to feed a growing global population while mitigating the impacts of climate change.

Farmers and producers in the agriculture sector are turning to “precision agriculture” to manage challenges such as declining water supply, soil quality and extreme weather events, even as the traditional and sometimes unpredictable challenges to agricultural production remain.

A DEFRA survey shows UK farmers use precision farming to increase productivity or performance (78%), to improve accuracy (59%), and to reduce input costs (55%). The precision agriculture market will reach €3.7 billion worldwide in 2025, says Berg Insight.

The field is a digital workspace

Three primary technologies, GPS, GIS and VRT (variable-rate technology), are used to support a range of use cases, including livestock and asset tracking, field surveillance and management, and precise positioning.

Crops are grown using satellite imaging, drone video-based soil and moisture monitoring; animals carry tracking devices and grazing land managed with precise land management tools.

Precision agriculture also makes use of the same transformative technologies we use across every other element of the enterprise: sensors, big data, machine intelligence, imaging, IoT, networks and more. Modern farmers review, manage and execute decisions based on information provided by these complementary technologies using apps on their smartphones.

The precision agriculture experiments of the last decade are becoming market-ready solutions with the world’s biggest names in agritech and machinery behind them.

For example:

  • Agricultural machinery company John Deere has introduced an app that farmers can use to control field machinery. It exploits historical biomass data to build accurate field maps shared wirelessly with those machines to help guide the sustainable use of fertilizers and crop protection chemicals
  • Supported by AI, Precision Livestock Technology uses an intelligent machine vision platform at cattle troughs to optimize feed and assess animal health. When cattle eat, they are filmed, the images are analyzed for health and eating habits, and the system provides decision support to improve care
  • Agricultural technology firms such as Dacom and Semios offer connected in-field IoT systems. One Semios solution lets you check frost conditions in your orchards. Additional tools that exploit big data and analytics include connected humidity, temperature, transpiration and insect surveillance. Sensors, AI and machine learning enable improved field management decisions
  • Sentera’s FieldAgent uses off-the-shelf drones to build accurate field plans. The system can spot weed outbreaks, assess crop health, figure out plant populations and more. At sea, the UN provides a solar-powered technology platform for fish preservation and processing
  • The robots are coming! A good illustration of this in consumer markets is the solar-powered Tertill gardening and weeding robot from the inventors of Roomba. This $350 robot echoes industrial-grade weeding robots from Naïo Technologies, VitiBot and ecoRobotix already in use on farms. Autonomous farm equipment will reach $150B in market value by 2031, says Fact.MR
  • Blockchain is used to boost transparency across supply chains. Canada’s Grain Discovery helps track the grain lifecycle from field to shop; Starbucks and Microsoft use a blockchain-based supply chain system for coffee beans; and IBM has its own blockchain-based “Thank my Farmer” solution.

What is required for precision agriculture?

Robust networks, education and interoperability are critical to the successful application of precision agricultural techniques.

As the COVID-19 pandemic revealed, broadband infrastructure continues to be lacking in many rural areas, limiting digital transformation. It is hoped the steady rollout of robust mobile networks will improve this, though cost is a factor. The GSMA estimates that it can cost twice as much to build and maintain network infrastructure in rural areas. However, technologies such as solar and fuel cell innovations may mitigate this.

Training of the workforce will also be required for successful deployment, as precision agriculture will require knowledge in a range of new disciplines. These include soil science, crop and livestock genetics, agri-chemicals and general-purpose technologies, such as remote sensors, satellites and robotics.

Another challenge is interoperability. Many first-generation precision agricultural technologies grew in isolation with little concern for interoperability in a trend visible across the IoT sector. That means valuable data is not shared, and the mass analytics opportunities of standardization do not exist.

Work to resolve this is ongoing, with standardization initiatives from trade groups such as Agricultural Industry Electronics Foundation and AgGateway taking place on the fast track – though part of the challenge (as the European Parliament wrote in 2017) is the need to avoid creating vast data monoliths controlled by big tech firms.

An exciting time in the evolution of smart agriculture

These processes promise to boost yields, reduce waste and improve farm and supply chain management while accelerating progress toward sustainability. The World Economic Forum estimates that if 15-25% of the world’s farms adopt such techniques, crop yields could climb by 10-15% by 2030, while greenhouse gas emissions and water use would decline by around 10%.

The need to feed and sustain the planet is driving this business. Finistere Ventures claims $11.6 billion was invested in it in 2020. “Building a sustainable agriculture and food ecosystem is absolutely critical, and it will take a lot of time and even more money,” said analyst Arama Kukutai.

Orange Business is helping companies across the agriculture sector take advantage of new technologies to improve farming yields and sustainability. Read our case study on how we helped Dacom build its IoT-based agricultural yield management systems.