Driving the Future of Forestry: How Neousys Edge AI Powers Intelligent Crane Trucks

Introduction

In the forestry and heavy-machinery industry, operating conditions are unpredictable and demanding. Logging often takes place deep in undeveloped areas where operators must maneuver heavy trucks through rough terrain and then control cranes to load logs.

Traditionally, two operators are required — one to drive and another to operate the crane. Both roles demand advanced skills, high concentration, and strong situational awareness. With limited visibility and confined space among dense trees, safety risks are high.

A leading forestry machinery manufacturer set out to change this. Their goal was to design a single-operator crane truck that could be driven and operated by one person safely and efficiently. To achieve that, they turned to advanced machine vision and real-time edge AI computing.

Neousys’ GPU-aided edge AI platforms became the processing core, enabling precise visual assistance and real-time imaging. Combined with multi-camera vision and VR integration, the system provided the operator with full visibility and seamless control from within the cabin.

Problem Identification

Forestry operations face several persistent challenges:

• Complex operating conditions: Uneven ground, tight corners, and poor visibility increase the chance of operational errors.
• Labor shortage and training difficulty: Skilled drivers and crane operators are hard to find and expensive to train.
• Safety risks: Operators often work in hazardous zones where accidents can occur during manual crane operation.
• Inefficiency: Switching between multiple operators slows down the process and increases costs.

The manufacturer needed a solution that improved precision, reduced human risk, and streamlined the workflow — without compromising on rugged reliability.

Solution Implementation

To bridge the gap between human control and machine intelligence, the manufacturer adopted Neousys GPU-aided edge AI platforms as the system’s central processor.

The architecture integrated:

• High-performance 3D machine vision for real-time object and spatial awareness.
• VR-assisted view rendering, allowing the operator to monitor surroundings and equipment positioning with precision.
• Edge AI computing to process high-resolution camera inputs instantly, even in low-connectivity forest environments.
• Multiple industrial interfaces — including ignition control, CAN bus, PoE, USB3.1 Gen2/Gen1, PCIe expansion, and MezIO modules — ensuring seamless communication between sensors, controls, and cameras.

This setup allowed a single operator to both drive and operate the crane with improved accuracy and safety. The system provided 360-degree situational awareness and removed the need for the operator to step outside the cabin.

Results

• Reduced manpower: One operator can now handle both driving and crane operation.
• Enhanced safety: The operator remains inside the protected cabin, away from hazardous zones.
• Improved accuracy: Real-time imaging and edge AI drastically reduce alignment errors when maneuvering or loading logs.
• Operational efficiency: Faster loading cycles and fewer delays during transitions between tasks.
• Lower training cost: New operators can quickly adapt through intuitive machine-vision assistance.

This transformation showcases how rugged edge AI and machine vision can redefine traditional heavy-machinery operations. By merging real-time computing with human control, Neousys and Neteon helped create a smarter, safer, and more efficient forestry workflow.

Conclusion

The integration of Neousys edge AI platforms and machine vision in forestry crane trucks proves how digital transformation can elevate even the most traditional industries. Through advanced visual assistance, the system simplifies complex tasks, reduces risk, and increases productivity.

At Neteon, we believe industrial intelligence should always be practical, reliable, and built for real-world conditions. Together with Neousys, we help manufacturers bring intelligence to the edge — from factories to forests.

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