artificial intelligence (AI) in agriculture has transformed the way the world’s farming operations work by giving food producers significantly improved access to data about their operations.
AI provides farmers with real-time insights about crop conditions, livestock activity, and the locations of their farm machinery. Looking ahead, many scientists believe AI in agriculture will play a pivotal role in increasing food production globally, particularly in areas where food insecurity is the norm.
By 2050, the world’s population will grow by 2 billion people, according to United Nations (UN) data on population and hunger. The world will require a 60% increase in food production to keep the global population fed.
Advances in AI and machine learning (ML) in agriculture are powering innovations that have the potential to improve food production supply chains in more affordable, sustainable ways.
AI and agriculture today
Spending on AI technology will grow from $1 billion in 2020 to $4 billion in 2026, a compound annual growth rate (CAGR) of 25.5%, according to Markets & Markets.
AI applications in agriculture tend to focus on one or more of four primary goals, according to the ITRex Group.
- Yield improvement
- Cost reduction
- Profit increase
- Alignment with sustainable farming practices
Here are several examples of exactly how AI is being used throughout the food production supply chain:
See more: Artificial Intelligence Market
5 examples of AI in agriculture
In his Forbes article, “10 Ways AI has the Potential to Improve Agriculture in 2021,” Louis Columbus addresses a range of successful AI applications in the industry:
1. Drone data is helping producers optimize the use of pesticides
Intelligent sensors, combined with visual data streams from drones, use AI to detect areas most infected with pests. This data helps farmers optimize the right mix of pesticides and allows them to zero in on only the field areas that need treatment. The result, Columbus says, is a reduction in overall costs and an increase in yields, two key drivers fueling AI in agriculture adoption.
2. Linear AI programming is enabling farmers to conserve more water
AI can help farmers locate irrigation leaks, optimize irrigation systems, and measure the effectiveness of crop irrigation approaches. Conserving water is becoming increasingly vital as the world’s population grows and drought conditions become more widespread and impactful. Using water efficiently can significantly impact a farm’s profit and contribute to the global effort to conserve water. Columbus says linear AI programming is being used to calculate the optimal amount of water a specific field or crop needs to reach the desired yield level.
3. IoT sensors are providing real-time insights into previously untraceable data sets
Farmers today have access to IoT sensors that can keep track of virtually every aspect of food production — a huge technological leap over agriculture methods from even a few years ago. It’s now possible for farmers to track data about soil moisture and nutrient levels to analyze crop growth patterns over time. Columbus points to a specific branch of AI — machine learning — as the key to using IoT sensor data to arrive at data-driven predictions about potential crop yields.
4. AI-powered yield mapping is improving crop-planning accuracy
Yield mapping is an agricultural technology that uses supervised machine learning algorithms to uncover patterns hidden within large-scale data sets that can be used for crop planning. Columbus notes this technique involves the collection of drone flight data, combined with IoT sensor data, to make predictions about potential crop yields before the vegetation cycle has begun.
5. AI-enhanced livestock monitoring is improving animal health and increasing profits
Being able to monitor livestock at a high level gives producers an edge over competitors who have yet to invest in AI-enhanced agriculture technology. Farmers, Columbus says, can monitor food intake, activity levels, and vital signs to develop a better understanding of the optimal conditions for better milk or meat production. Real-time health insights also allow farmers to quickly separate livestock infected with contagions from healthy animals as well as promptly address injuries and unexpected livestock behaviors.