OpenMV
Shields
Lens
Robotics
Book
Video
Download
Docs
Forum
OpenMV.io
GithubModern agriculture is accelerating its transformation towards intelligence and automation. Traditional manual harvesting methods are inefficient, costly, and struggle to achieve standardised operations. Particularly in orchard and field settings where fruit and vegetable ripeness is uneven and environmental lighting is complex, mechanical equipment finds it difficult to accurately identify harvesting targets.
To address this challenge, agricultural equipment enterprises have introduced SingTown's OpenMV intelligent camera AI image recognition technology. By leveraging machine vision, AI image recognition, and edge-side AI algorithms, they have developed an efficient and precise intelligent orchard harvesting system, enabling automatic identification, positioning, and accurate picking.

Agricultural equipment enterprises have adopted SingTown Technology's OpenMV intelligent cameras, deploying visual perception modules based on AI machine vision recognition algorithms on harvesting robots, achieving automatic detection and harvesting control of fruits and vegetables.
The system utilises high-performance cameras to capture real-time images of fruits and vegetables in the field. Through image recognition algorithms, it performs target identification, colour segmentation, and morphological analysis to accurately determine fruit ripeness and optimal harvesting timing.
By integrating deep learning models to classify the surface characteristics of fruits, the system can stably identify the positions of mature fruits under varying lighting conditions, complex backgrounds, and instances of fruit occlusion.
Upon completion of identification, the system utilises spatial coordinate calculations and robotic arm control algorithms to achieve three-dimensional positioning and precise grasping of the target fruit.
SingTown Technology's OpenMV smart camera features embedded edge computing capabilities. Its on-device AI can perform recognition and computation directly at the field equipment end, eliminating reliance on cloud networks and ensuring real-time performance and stability. Its automatic exposure and white balance functions can adapt to changes in lighting, sunlight, shadows, and reflective environments, guaranteeing recognition accuracy and image quality.
Leveraging SingTown Technology's integrated hardware and software platform, agricultural equipment enterprises can swiftly integrate the visual module of this embedded AI machine vision system into various models of harvesting robots, enabling automatic identification, precise picking, and yield data statistics.
Through SingTown Technology's OpenMV intelligent camera and smart image recognition technology, agricultural machinery has truly gained the capability to "see, understand fruit, and harvest," driving agricultural production from manual picking experience towards AI vision-driven smart field operations.

AI Sentinel Based on OpenMV: Automatic Alert for Unsecured Key Locations
Automatically detects whether the door of a key location has remained open for an extended period and issues timely alerts.

Personnel crossing boundary triggers an alert—OpenMV defines the “sense of safety boundary”
Detects personnel or equipment crossing virtual perimeter lines.

“AI Urban Management Officer” is here—a system for automatic identification of unauthorised street vending, built on OpenMV
Automatically identifies unauthorised street trading activities to support daily urban governance.

Improper storage of gas cylinders? Use OpenMV to automatically trigger hazard alerts
Automatically identifies non-compliant placement of gas cylinders to proactively detect potential gas safety hazards.

Did you perform live-line work without wearing insulating gloves? OpenMV issues an immediate alert!
Automatically identifies whether personnel are wearing insulating gloves to assist with safe operations.

Utilise the OpenMV smart camera to identify surface defects on aluminium plates in real time
Online identification of surface defects on aluminium sheets, such as scratches and dents, to support quality control.