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Cloud Perspectives

Sustainable farming field with robot

In the 1950s and 1960s, plant biologist Norman Borlaug famously led the “Green Revolution,” developing high-yield grains that helped drive up global food production when paired with innovations in chemical fertilizers, irrigation, and mechanized cultivation. By so doing, Borlaug and his peers helped save a billion people from starvation. However, this new form of farming was not sustainable and created multiple environmental issues.

Today, farmers are using technology to transform production again, driven by the need to feed more with less and to address the impacts of industrial farming on the environment. Currently, nearly half of current food produced, or 2 billion tons a year, ends up as waste, while an estimated 124 million people in 51 countries face food insecurity or worse. In addition, new sources of arable land are limited, fresh water levels are receding, and climate change puts pressure on resources and will lower agricultural production over time. Governments need to solve these issues swiftly, as the world’s population is slated to grow from 7.6 billion to 9.8 billion 2050. Agencies and companies will need to team with growers to drive a 70 percent increase in food production.

The good news is that we’re now in the midst of a second Green Revolution that’s part of the Fourth Industrial Revolution. Here’s how technology innovation, driven by big data, the Internet of Things (IoT), artificial intelligence (AI), and machine learning, will reap a more bountiful harvest.

A vision for AI in agriculture

Farmers are deploying robots, ground-based wireless sensors, and drones to assess growing conditions. They then capitalize on cloud services and edge computing to process the data. By 2050, the typical farm is expected to generate an average of 4.1 million data points every day.

AI and machine learning interpret findings for farmers, helping them continually tweak crop inputs to boost yields. Farmers can use AI to determine the optimal date to sow crops, precisely allocate resources such as water and fertilizer, identify crop diseases for swifter treatment, and detect and destroy weeds. Machine learning makes these activities smarter over time. It can also help farmers forecast the year ahead by using historic production data, long-term weather forecasts, genetically modified seed information, and commodity pricing predictions, among other inputs, to recommend how much seed to sow.

Such precision farming technology augments and extends farmers’ deep knowledge about their land, making production more sustainable. Advanced technology can increase farm productivity by 45 percent while reducing water intake by 35 percent. However, the key is ensuring equitable access: Often the communities that need AI the most lack the physical and technology infrastructure required to support it.

Connecting communities with broadband

Access to high-speed connectivity and reliable power are still challenges in many parts of the world. That’s one reason Microsoft and its partners are bringing affordable broadband to rural communities in countries such as Colombia, India, Kenya, South Africa, and the United States through the Airband Initiative.

When communities are connected, farmers can benefit from AI and machine learning, even if they lack internet access to their individual farms. Microsoft employee Prashant Gupta and his team used advanced analytics and machine learning to create a Personalized Village Advisory Dashboard for 4,000 farmers in 106 villages and a Sowing App for 175 farmers in a district in the southeastern coastal state of Andhra Pradesh in India. Farmers with simple SMS-enabled phones can access Sowing App recommendations, which apply AI to data such as weather and soil conditions to optimize planting times. Farmers who followed the AI-driven advice increased yields by 30 percent over those who adhered to traditional planting schedules.

Using IoT and AI on individual farms

Farmers with connectivity can use IoT to get customized recommendations. The Microsoft FarmBeats program, driven by principal researcher Ranveer Chandra, has developed an end-to-end IoT platform that uses low-cost sensors, drones, and vision/machine learning algorithms to increase the productivity and profitability of farms. FarmBeats is part of Microsoft AI for Earth, a program that provides cloud and AI tools to teams seeking to develop sustainable solutions to global environmental issues.

In the United States, FarmBeats solves the problem of internet connectivity by accessing unused TV white spaces to set up high-bandwidth links between a farmer’s home internet connections and an IoT base station on the farm. Sensors, cameras, and drones connect to this base station, which is both solar- and battery-powered. To avoid unexpected shutdowns due to battery drain, the base station uses weather forecasts to plan its power usage. Similarly, drones leverage an IoT-driven algorithm based on wind patterns to help accelerate and decelerate mid-flight, reducing battery draw.

IoT data processing—for bandwidth-hogging information like drone videos, photos, and sensor feedback—is done by a PC at the farmer’s home. The PC performs local computations and consolidates findings into lower-memory summaries, which can be distributed over bandwidth more easily, while also serving as a backup during network outages.

AI for everyone means more food for the world

Over time, AI will help farmers evolve into agricultural technologists, using data to optimize yields down to individual rows of plants. Farmers without connectivity can get AI benefits right now, with tools as simple as an SMS-enabled phone and the Sowing App. Meanwhile, farmers with Wi-Fi access can use FarmBeats to get a continually AI-customized plan for their lands. With such IoT- and AI-driven solutions, farmers can meet the world’s needs for increased food sustainably—growing production and revenues without depleting precious natural resources.

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