Vision and Imaging Technologies Continue Growth Beyond the Factory Floor
Vision and imaging have long been key enabling technologies in widely varying markets. Machine vision systems can significantly improve industrial processes and help drive productivity, efficiency, and quality while reducing costs over many diverse use cases.
The growth of industrial machine vision is at an all-time record pace. Alex Shikany, VP of Marketing and Member Services for the Association for Advancing Automation (A3) reports that the machine vision market in North America expanded 26% in the first half of 2021 to a record $764 million. In an A3 survey, more than 95% of responding companies and analysts expect the market will not decline over the next six months. Dr. Chris Yates from Vision Ventures notes that the key drivers advancing the continued implementation of machine vision technologies are increased awareness of capabilities and value; decreasing component, software, and engineering costs; broader technology compatibility and interoperability; and greater focus on and success in ease-of-use.
It is clear that both mature and emerging industries continue to embrace vision and imaging. But what are the next industrial use cases for machine vision?
Out of the Factory, Into the Fields
Agriculture is one industry beginning to see significant benefit from broad implementation of advanced automation technologies such as machine vision. According to the USDA, US gross farm income is forecast to increase 7.3% in 2021 to $486 billion, with technology advancements having a big influence on the bottom line. A fast-growing sector is vertical farming, where plants (vegetables, fruits, herbs, and more) are grown primarily indoors in vertical towers under controlled environments.
“Precision agriculture” in farming has been a rich target for automation for some time, with many different applications under development and/or research. The use of autonomous robots in the planting and harvesting of crops is an area seeing some growth and acceptance. Driverless tractors can be guided primarily using GNSS/GPS (Global Navigation Satellite Systems and Global Positioning Systems). However, full autonomy requires the ability to detect and react to unexpected obstacles, particularly humans and animals. Perception systems using technologies such as infrared imaging, lidar, and 3D Time of Flight are being successfully implemented in farming robots. Autonomous robots with vision and deep learning models have even been used for chemical-free weeding of some crops.
Harvesting automation systems depend strongly upon vision and imaging. For instance, cobots (collaborative robots) and machines equipped with autonomous motion use cameras and AI to differentiate ripe fruit from nonripe fruit and provide guidance for automated pick. Abundant Robotics (www.abundantrobotics.com) reported initial success with their apple-picking robot, which uses vision to detect ripe fruit. Vegetable harvesting is also on the table. Cambridge University has demonstrated a lettuce-picking robot using multiple cameras and machine learning to detect healthy and ripe heads and guide the pick.
Vertical farming is uniquely suited to automation due to the well-constrained nature of the crops and the environment. Vision-guided robots are key to productivity during the planting process to capture seedlings and place them in the vertical growing towers. Harvesting also can be similarly automated.
In general agriculture, machine vision can be used to inspect crops and detect drought or disease. Multispectral and hyperspectral imaging, either at plant level or field level using drones, is a widely used technology with proven success. Infrared imagery also can be used depending on the inspection and crop.
Perception for Precision Machining
New applications for vision and imaging are gaining more widespread acceptance in the precision machining industry. The precision machining industry (CNC machining) is quite mature and prospering, with market size from some sources at about $400 billion and projected growth in 2021 of about 7%. Along with the adoption of advanced automation to improve quality and productivity, key drivers in this marketplace that can contribute to growth are finding and efficiently using manpower, reducing operating costs, eliminating machining defects, and catching defects early to avoid waste. These and other factors are pointing this industry to new adoption of vision and imaging as well as robotics in the production process.
While robotic machine tending is not completely new in the precision machining industry, it is gaining broader acceptance, and the task is being advanced with machine vision to provide further autonomy. Robots for machine load and unload can be guided for part pick and place, delivering greater efficiency for small-batch, high-mix runs common to precision machining. And 3D imaging is emerging as a potential enabling technology in supporting random bin pick of parts or blanks to be machined, thereby eliminating the need for hard fixturing and operator load.
These new industry examples are just a small piece of the markets benefiting from vision and imaging technologies. As emerging industry use cases develop, these technologies will continue to improve processes and quality.
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