At the Edge: Use Cases from Real Life Part II
Artificial intelligence (AI) keeps developing every day and we see more of its use cases around us. From smart retail applications to innovations in healthcare technologies, artificial intelligence is used in a variety of sectors. In this series, we tackle how AI, especially Edge AI, can be deployed in different scenarios to meet various needs. Before taking a deep dive into how AI can be implemented in diverse use cases, let’s start with getting to know Edge AI.
Defining Edge AI
Edge AI refers to systems where artificial intelligence is deployed at the point of data collection, rather than a remote server. As data is processed where it is collected, there is no latency in this type of systems, which makes them ideal for critical missions, sectors where agile decision-making is prioritized and situations where remote control would not be feasible due to infrastructural problems.
Edge artificial intelligence systems do not require to continuously be in contact with the home server, however, they can send and receive the data deemed necessary. In these systems, remote connection is not an obligation which would slow the whole system down. Rather, it is an optional addition that can be employed at will.
Powering artificial intelligence at the edge allows organizations to have faster, more reliable and context-aware decision-making by bringing intelligence closer to where data is generated. This is especially important in sectors like smart city infrastructures, sports or agriculture, where every decision made can have a lasting impact.
How is Edge AI implemented?
To run artificial intelligence at the edge, you need reliable hardware capable of running the desired algorithms. Carrier boards, industrial PCs or ruggedized computers are built for exactly this purpose. You can pick the ideal hardware for your project based on your needs or get it customized to ensure it is the perfect fit.
While picking the right hardware for edge AI projects, it is important to take a few factors into account. First of all, the specifications of the hardware must be compatible with the project. If the project requires rich connectivity options, the carrier board or box PC used must carry the necessary ports. Another crucial point is the performance. NVIDIA Jetson processors offer a wide range of performance metrics, making them ideal for various projects, from simple tasks to complex systems. Last but not least, form factor of the hardware cannot be overlooked. The hardware to be used should fit the physical form of the project and should be able to operate in the environment project is in. Whether it is the underwater conditions of a submarine or the windiest point of a turbine, there is edge AI hardware designed to fit every need.
We have discussed what edge AI is and how it can be implemented on projects. Now, let’s move on to real life use cases from three sectors, which are smart cities, sports and agriculture. All three of these sectors have day-to-day effects on people’s livelihoods, making them important for both today and the future.
Three Edge AI Use Cases in the Real World (Part 2)
Curious about how the future of AI looks? Edge AI is right here and transforms our lives every day. To learn more about edge AI applications around us, you can consult our series where we explain how different sectors implement it. In this part, we will discover how smart city infrastructures, sports players and fans, farmers and products are changed by edge AI.
Smart Cities at the Edge
Smart cities are complex systems where different processes run at the same time to ensure the wellbeing of people. Using digital technologies make cities more efficient, sustainable and responsive to people’s needs. Edge AI systems play a key role on this transformation as they enable artificial intelligence to run at where it is needed, from cameras to sensors. Running at the edge, these systems can make decisions real time, without latency or network issues. This way, edge AI systems not only contribute to smart cities but take them to another level, ensuring they work seamlessly.
Some edge AI applications at the edge include:
- License Plate Recognition (ANPR/LPR): These systems use cameras and AI to automatically detect and read vehicle license plates in real time. Real time plate recognition helps with traffic enforcement, tolling, access control and locating stolen or unauthorized vehicles.
- Emergency response coordination: Being the most crucial one of all smart city applications, emergency response coordination integrates data from cameras, sensors and communication systems to quickly detect incidents and guide responders. This enables faster dispatch, optimized routing and better situational awareness during emergencies, potentially preventing disasters.
- Pedestrian & vehicle counting: Uses vision and sensor data to count people and vehicles across streets and public spaces. The insights help cities improve traffic flow, urban planning and public safety.
- Environmental sensor fusion: Combines data from multiple sensors measuring air quality, noise, weather and other conditions. Fusing this data provides a more accurate, real-time view of environmental health across the city.
- Suspicious activity detection: Applies AI to video and sensor feeds to identify unusual or potentially dangerous behavior. This allows authorities to respond proactively while reducing reliance on constant human monitoring.
- Smart parking optimization: Uses sensors and AI to detect available parking spaces and predict demand. Drivers are guided to open spots, reducing congestion, emissions and time spent searching for parking.
Products for Smart Cities
Sports at the Edge
At first glance, it might seem like sports and edge AI are from two completely different worlds. Though they are leagues apart in essence, edge AI can be integrated in sports for several different functions. Just like every other sector, edge AI can enhance sports by analyzing large quantities of data which would otherwise be impossible to tackle, making instant decisions and ensuring interconnected systems work without issues. Here are some edge AI applications in sports:
- Security breach & suspicious activity detection: Edge AI analyzes live video feeds to identify unauthorized access, abnormal behavior or potential threats in real time. This enables security teams to respond immediately while minimizing false alarms and manual monitoring.
- Stadium entry/exit crowd flow control: Cameras and sensors track crowd density and movement at gates and corridors. Real-time insights help staff reduce bottlenecks, prevent overcrowding and improve the overall fan experience.
- Emergency evacuation optimization: Edge AI monitors crowd movement and environmental conditions during emergencies. It dynamically recommends safer evacuation routes and speeds up decision-making when every second matters.
- Gym equipment usage analytics: Sensors and vision systems track how often and how long equipment is used. These insights help facility managers optimize layout, maintenance schedules and member experience.
- Athlete performance analytics: Wearables and vision-based systems analyze movement, speed and biomechanics in real time. Coaches can use this data to refine technique, prevent injuries and personalize training programs.
- Fan behavior analytics: Edge AI processes in-venue data to understand fan engagement, movement and preferences. This helps teams and venues tailor experiences, improve services and increase revenue opportunities.
You can check out ScoreBreak & FORECR Success Story to see how real-time sports analysis and video integration for teams is implemented. The collaboration between FORECR and ScoreBreak demonstrates how edge AI enables low-latency video processing, seamless data integration and actionable insights that help teams improve performance, decision-making and competitive advantage.
Products for Sports
Agriculture at the Edge
Modern agriculture benefits from technology for improved productivity, sustainability and efficiency. Developments in this sector have a big impact on people by strengthening food security, reducing environmental impact, supporting farmers’ livelihoods and ensuring a more resilient food supply for a growing global population. Edge AI supports the sector in all these, providing analysis, decision making and tracking capabilities. Some real-life use cases of edge artificial intelligence in agriculture include:
- Soil and water condition sensor analysis: Sensors continuously monitor soil moisture, nutrients and water quality at the field level. Edge AI processes this data in real time to optimize irrigation and prevent over- or under-watering.
- Smart greenhouse management: Cameras and environmental sensors track temperature, humidity, light and plant growth. Edge AI automatically adjusts conditions to maximize yield while reducing energy and water use.
- Autonomous farming vehicles: AI-powered tractors and harvesters navigate fields, avoid obstacles and perform tasks with minimal human intervention. Edge AI enables real-time decision-making for safe and precise operation.
- Field analytics with drones: Drones capture high-resolution images and sensor data across large fields. Edge AI analyzes this data on-board to quickly identify crop stress, growth patterns and problem areas.
- Plant disease detection: Vision systems inspect crops for early signs of disease or pest infestation. Edge AI enables fast, localized detection so issues can be treated before spreading.
- Precision spraying: AI systems identify exactly where fertilizers, pesticides, or herbicides are needed. Edge AI ensures targeted application in real time, reducing chemical use and environmental impact.
Products for Smart Agriculture
Not sure yet?
Looking for smart solutions in smart cities, sports or agriculture but don’t know how to implement artificial intelligence? Let our professional team help you. You can reach out to our team to learn more about possible implementation of edge AI in your field, FORECR’s products and more. To learn more, you can also check out our blog post on smart retail, transportation and industrial automation systems enabled by edge AI.
