Published: 10 December 2023
Contributors: Alice Gomstyn, Alexandra Jonker
Smart farming, also known as smart agriculture, is the adoption of advanced technologies and data-driven farm operations to optimize and improve sustainability in agricultural production. Technologies used for smart farming include artifical intelligence (AI), automation and the Internet of Things (IoT).
While new technologies and tools have long been integral to farm management and food production, urgent concerns drive the development and adoption of smart farming technologies today. Chief among them is food security: food production must increase by 70% by 2050 to keep pace with global population growth, according to the International Monetary Fund.1
Climate change is making it harder to secure enough food. It reduces crop yields and endangers the availability of natural resources such as water for irrigation. In addition to climate issues, the agricultural sector also faces profitability challenges amid the rising costs of inputs like fertilizer, volatile commodity prices and increasing regulatory requirements.
“Through smart farming, we can better adapt to the uncertainties brought by climate change, mitigate environmental impacts and promote resilience in agricultural production.”
— The International Organization for Standardization2
With ESG disclosures starting as early as 2025 for some companies, make sure that you're prepared with our guide.
Register for the ESG reporting guide
Early agricultural practices centered on the use of human labor, animals and simple tools. Some notable advancements in agricultural technology were the invention of the seed drill for more efficient planting in 1701, steam traction engines that powered grain threshing in the 1800s, and gas-powered tractors in the early 1900s.
The introduction of farm machinery greatly reduced the need for physical work in farming, while data collection and analysis allowed farmers to improve their crop and livestock outputs. This method, called precision agriculture or precision farming, began in the early 1980s with Dr. Pierre Robert, also known as "the father of precision agriculture". He studied how different areas of a field need various amounts of nutrients for the best crop growth. His work led to the creation of farming systems that apply different amounts of resources across a field.3
In the 1990s, agri-business technology advanced even further with the creation of the digital crop yield monitor and the growing use of satellite-based global positioning systems (GPS). By combining yield data with GPS, farmers could map their yields, giving them important information about crop characteristics and quality in real time during harvest. Later, GPS technology led to another big breakthrough: automation. The self-driving tractor emerged from a partnership between farm equipment company John Deere and NASA in the early 2000s.
Smallholder farmers produce about one-third of the world's food supply.
Learn how IBM and Texas A&M AgriLife equip farmers in need
Advanced technologies that are revolutionizing agricultural production at various agri-businesses power today’s modern farming.
The US Department of Commerce’s National Institute of Standards and Technology defines information and communications technology (ICT) as the capture, storage, retrieval, processing, display, representation, presentation, organization, management, security, transfer and interchange of data and information. Data collection on everything from soil content to weather conditions has become a key facet of smart farming and ICT is helping farmers organize and transfer that data.
IoT refers to a network of physical devices, vehicles, appliances and other physical objects that are embedded with sensors, software and network connectivity that allows them to collect data. In the case of smart farming, IoT devices include many kinds of IoT sensors, including sensors for monitoring crops, tracking livestock and observing the condition of farm equipment. Unmanned aerial vehicles (UAVs) or drones equipped with light detection and ranging (LiDAR) also collect agricultural data through remote sensing.
AI and machine learning (ML) can help farmers derive insights from the big data—large, complex data sets—stemming from IoT initiatives. Data analytics and modeling through cloud-based AI and ML tools can inform decision-making and smart farming techniques. For example, predictive analytics, weather data sets and agriculture forecasting models powered by ML can help the agricultural industry manage the production process, including crop production, land utilization and supply chain planning.
Automation and robotics figure prominently in modern smart farming practices. In addition to autonomous tractors, farmers use robots for tasks like seeding, harvesting and pruning. They can also deploy UAVs to spray fertilizer, pesticides and other agricultural inputs in a manner that can be more efficient and precise than traditional methods. The more precise and limited application of fertilizer, in particular, can have a notable environmental impact: fertilizer is a significant source of greenhouse gas emissions.
The agricultural sector and technology providers can help create a better future of agriculture with smart farming techniques and innovations. Here are just a few examples of farm productivity optimization around the world, due to smart farming:
Smart soil sensing for water optimization
In Texas, sensors linked to a smartphone app are gathering real-time information on soil conditions, including soil moisture. The app combines this information with other data, including weather forecasts, for an AI-powered analysis that results in watering recommendations. The app sends the recommendations to farmers' mobile devices to help them efficiently deploy water resources for better crop growth in areas affected by droughts and climate change.
Cloud-based irrigation for vine stress
In California, where efficient water use is also a major concern, a winery implemented a cloud-based tool that ingests information from weather forecasts, satellite imagery and sensors to measure vine stress. Analysis of the data yields watering recommendations tailored to the needs of each vine. Since putting the tool in place, yields have increased by 26% while reducing water usage by 16%.
AI-driven climate control in greenhouses
In Kazakhstan’s Almaty region, a five-hectare smart greenhouse facility is equipped with IoT technology and AI. These technologies monitor conditions within the greenhouses and automatically adjust temperatures, light, humidity and irrigation levels as necessary to create the optimal environment for crop growth.4
Monitoring animal behavior for improved dairy production
In the United Kingdom, researchers attached sensors to cattle at dairy farms to track their activity, including steps taken and time spent eating and lying down. Since more active cattle generally display more positive behavior, such information can help farmers determine whether interventions are necessary—namely, changing the animals’ environment to raise their contentment levels, which tend to improve milk yields.5
Integrate smart farming technology with accurate agricultural forecasting to help minimize disruptions and maximize crop production.
Use a modular solution built on blockchain, benefiting all network participants with a safer, smarter and more sustainable food network.
Manage real estate portfolios across their lifecycle with an intelligent asset management and integrated workplace management system (IWMS).
IoT refers to a network of physical devices, vehicles, appliances and other physical objects that are embedded with sensors, software and network connectivity, allowing them to collect and share data.
IBM and Texas A&M AgriLife are working together to help farmers receive insights for water usage.
Geospatial data is time-based data that is related to a specific location on the Earth’s surface. It can provide insights into relationships between variables and reveal patterns and trends.
LiDAR is a remote-sensing technology that uses laser beams to measure precise distances and movement in an environment, in real time.
Digital transformation takes a customer-driven, digital-first approach to all aspects of a business, including its business models, customer experiences, processes and operations.
This CIO Insights white paper explores how IBM is helping the agriculture industry continue to feed the world through the power of data and AI.
IBM Environmental Intelligence Suite is a SaaS platform used to monitor, predict and respond to weather and climate impact. It includes geospatial and weather data APIs and optional add-ons with industry-specific environmental models—so your business can anticipate disruptive environmental conditions, proactively manage risk and build more sustainable operations.
¹“Helping Feed the World’s Fast-Growing Population” (link resides outside ibm.com), Rabah Arezki, IMF Blog, 31 January 2017.
²“Smart farming: the transformative potential of data-driven agriculture” (link resides outside ibm.com), ISO.
³“The Evolution of Precision Agriculture and Policy Implications” (link resides outside ibm.com), Bernt Nelson, American Farm Bureau Federation, 23 August 2023.
⁴“How a “smart” greenhouse helps Kazakh farmer grow vegetables all year round” (link resides outside ibm.com), Food and Agriculture Organization of the United Nations, 2 August 2023.
⁵“Robocow: Sensors attached to cattle giving farmers a head start on keeping them happy” (link resides outside ibm.com), Yahoo News, 14 August 2023.