AGRICULTURE

Agriculture is critical to the economic sector. Agriculture automation is the primary concern and a rapidly growing subject throughout the world. The population is growing at a breakneck pace, which increases demand for food and employment. Farmers’ traditional methods were insufficient to meet these requirements. As a result, new automated methods were developed. These new methods met food requirements while also creating jobs for billions of people.

Agriculture has been transformed by artificial intelligence. This technology has insulated crop yields from a variety of factors such as climate change, population growth, labor shortages, and food security concerns.

According to BI Intelligence Research, global spending on smart, connected agricultural technologies and systems, including AI and machine learning, is expected to triple in revenue by 2025, reaching $15.3 billion.

Spending on AI technologies and solutions in agriculture alone is expected to increase from $1 billion in 2020 to $4 billion in 2026, representing a 25.5 percent compound annual growth rate (CAGR).

According to PwC, IoT-enabled Agricultural (IoTAg) monitoring is the fastest-growing technology segment in smart, connected agriculture, expected to reach $4.5 billion by 2025.

According to United Nations population and hunger projections, the global population will grow by 2 billion people by 2050, necessitating a 60% increase in food productivity to feed them. Growing, processing, and distributing food is a $1.7 trillion industry in the United States alone, according to the Economic Research Service of the United States Department of Agriculture. AI and machine learning are already demonstrating the potential to assist in meeting the anticipated food needs of an additional 2 billion people globally by 2050.

Use cases of AI in agriculture

1. Monitor real-time video feed for every crop

AI and machine learning significantly reduce the risk of domestic and wild animals accidentally destroying crops or committing a break-in or burglary at a remote farm location. With the rapid advancements in video analytics enabled by AI and machine learning algorithms, anyone involved in agriculture can protect the perimeters of their fields and buildings.

2. Enhance crop yield prediction with real time sensor data and drone visual analysis data.

The volume of data collected by smart sensors and drones providing real-time video streaming provides agricultural experts with access to previously unavailable data sets. It is now possible to analyze growth patterns for individual crops over time by combining in-ground sensor data on moisture, fertilizer, and natural nutrient levels.

3. Yield Mapping to detect patterns in big data sets and to grasp their orthogonality in real time.

It is possible to predict the potential yield rates of a given field prior to the initiation of a vegetation cycle. Agricultural specialists can now forecast potential soil yields for a given crop by combining machine learning techniques with 3D mapping, social condition data from sensors, and drone-based data on soil color.

4. Pioneering drone to improve the control of pesticides

Agricultural teams using AI can predict and identify pest infestations before they occur by combining infrared camera data from drones with sensors on fields that monitor plants’ relative health levels.

5. AI and machine learning-based smart tractors, agribots and robotics for remote agricultural operations. Due to a lack of available employees, large-scale agricultural businesses rely on robotics to harvest hundreds of acres of crops while also providing an element of security around remote locations. Programming self-propelled robotics machinery to apply fertilizer to individual rows of crops helps reduce operating costs and increases field yields.
6. Optimizing and restricting the correct combination of biodegradable pesticides to only regions that require treatment.

Agricultural AI applications can now detect the most infected areas in a planting area by combining intelligent sensors with visual data streams from drones.

7. Price forecasting for crops based on yield rates

Understanding crop yield rates and quality levels enables agricultural firms, co-ops, and farmers to negotiate more effectively for the best price for their harvests.

8. Monitoring livestock’s health, including vital signs, daily activity levels and food intake

Understanding how each type of livestock reacts to diet and boarding conditions is critical for determining the best long-term treatment strategy.

Regional Outlook (Revenue, USD Billion, 2021 – 2027)

• North America
  • The U.S.
  • Canada
  • Mexico
• Europe
  • Germany
  • The U.K.
• Asia Pacific
  • China
  • Japan
  • India
• South America
  • Brazil
• Middle East and Africa (MEA)

Major players

• International Business Machines Corp. (IBM) (US),
• Deere & Company (John Deere) (US),
• Microsoft Corporation (Microsoft) (US),
• Farmers Edge Inc. (Farmers Edge) (Canada),
• The Climate Corporation (Climate Corp.) (US),
• ec2ce (ec2ce) (Spain),
• Descartes Labs, Inc. (Descartes Labs) (US),
• AgEagle Aerial Systems (AgEagle) (US), and
• aWhere Inc. (aWhere) (US)

Topics this report will cover:

• Market size from 2019 to 2027
• Revenue Forecast from 2020 to 2027
• Market segmentation and active players
• Market trends and opportunities
• Competitive landscape 
• Regional analysis 
• Related Industries 

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