Breakthrough Machine Vision Technologies

How the future of machine vision is shaping industries from manufacturing to agriculture

As industrial automation technologies continue to proliferate in industries ranging from logistics and manufacturing to agriculture and automotive, vision systems are tasked with greater responsibility and increased demand. Whether used for inspection, product assembly or identification, traceability, or even automated checkout, machine vision is more prevalent than ever. 

“We’ve seen machine vision move from inline modules on production lines into bigger spaces for bin picking large objects, palletizing operations, and other larger area solutions,” says Steve Kinney, director of training, compliance, and technical solutions at Smart Vision Lights. “With COVID and the massive rise in automated warehousing, machine vision became normal in even larger spaces and began to be networked to interlace into other tactical operations.” 

“This kind of machine vision technology, coupled with the autonomous vehicle technology developed for the various mobile robots moving product within the warehouse, has now become attractive to many agricultural applications, and we see the beginnings of a potentially huge ‘smart farming’ market,” Kinney explains. “Machine vision will now move out and become normal in the largest space of all — the real world.”

As we see more applications for machine vision emerging, the market is growing accordingly. In fact, Interact Analysis projects the machine vision market to grow at an estimated compound annual growth rate (CAGR) of 6.4% through 2028, with revenues reaching $9.3 billion. 

Machine vision vendors continue to innovate to meet the changing needs of the industry, which helps propel growth. From hardware to software, vision technology is advancing at a rapid pace while ensuring more accurate and reliable imaging for the increasingly complex applications to which it’s being applied. 

AI Creates New Possibilities in Machine Vision

Artificial intelligence (AI) is impacting all aspects of life, from graphic design to writing and process optimization, and, of course, machine vision.  Experts agree that AI can be a game-changer for machine vision systems deployed in certain applications. These algorithms can open the door to new applications for visions systems, expanding the overall market’s potential, according to Kinney.

“Properly developed AI routines paired with machine vision will offer solutions and advancements in many market segments, especially in some of the of the newest market segments beginning to adopt machine vision,” he says. “AI continues to offer solutions to machine vision problems that may be otherwise difficult to solve with traditional rules-based processing. Massive parallel processing coupled with the relational nature of AI has advantages over rules-based programming as machine vision expands into new applications that have historically relied on human perception.”

For example, rules-based algorithms can handle the complex task of moving a fixed robot arm. But when that arm is mounted on an automated guided vehicle and tasked with trimming and harvesting in a vertical farming operation, traditional processing is likely insufficient.

“Now imagine that it’s not just a robotic arm in a vertical farming operation, but an autonomous weeder operating in a 100-acre open crop field,” Kinney says. “These new types of applications for vision detection and guidance are made possible with AI and might be limited or not possible at all with traditional programming.”
In such applications, AI can enable better machine vision inspection and defect detection, even with naturally varied products, while improving the overall vision system. 

Massimiliano Versace, CEO and co-founder of Neurala Inc., explains these benefits using Neurala’s latest machine vision AI platform, Cascade, as an example. This system allows users to build data models in minutes and easily modify and redeploy as needed. 

“Users will now be able to not only use AI algorithms to detect objects in a field of view, but also ‘zoom-in’ and do additional AI processing on the object to highlight potential anomalies or defects in a specific area of interest,” he explains. “Manufacturers and integrators can use visual inspection automation (VIA) to create more complex inspection workflows with the same ease, speed of training, and flexibility of the general product.”

Weighing the Benefits and Drawbacks of AI 

With all the hype around AI, it could appear to be the go-to solution for all applications. And although it can open many doors for machine vision applications, it’s not a one-size-fits-all solution. Kinney notes that the biggest challenge for those looking to deploy AI with machine vision is setting realistic expectations for its capabilities. 

“AI will certainly revolutionize many areas. However, that doesn’t mean that it is superior or even the best choice for every case,” he says. “AI is certainly a buzzword in 2025, and many vendors have jumped on the bandwagon with all types of AI products. For a potential user with a particular application to solve, determining which of these will deliver value is challenging.”

Kinney recommends potential users remind themselves of the fundamental best practices when developing a machine vision solution: define your problem, identify elements needed for success, and apply those elements through rigorous testing. “Potential users should be leery of deploying AI without the proper qualification work,” he warns.

Embracing the Edge 

Although cloud computing is a fit for several machine vision applications, experts note that edge computing and edge learning continue to gain traction through the deployment of embedded systems. Bringing computing capabilities into or closer to the device allows for real-time data processing and decision-making — key benefits for many automated applications.

Embedded systems deliver cost and size benefits while also being targeted for specific applications by bringing processing closer to the edge. This reduces the need to transfer large amounts of data to the cloud, improving efficiency and speeding up decision-making processes, according to Jeremy Bergh, vice president of sales – Americas at The Imaging Source. 

Although fragmentation and complexity can be challenging, Bergh notes that embedded solutions enable users to integrate even more sensing options into their systems, which can be especially beneficial in applications including automotive, energy, manufacturing, and mobility. Neurala’s Versace echoes similar sentiments, noting that edge capabilities are expected to continue to improve for vision applications.

“It would be great to see edge computing for vision systems leveraged more across the board in manufacturing. Instead of sending data to a central cloud server, edge computing enables real-time processing of visual data directly on the device,” he explains. “This is crucial for applications like autonomous vehicles, drones, and real-time quality control in factories, where low-latency decision-making is key. As edge computing devices become more powerful, the efficiency and capabilities of machine vision systems will increase.”

Added Intelligence at the Edge

Coupling edge computing with artificial intelligence offers even greater benefits, Versace explains. While integration with legacy systems can be challenging, when edge computing is paired with AI, latency is reduced, data processing is quicker, and security is enhanced because data is kept on premises. 

“In manufacturing, it benefits applications like real-time defect detection for faster decision-making without relying on the cloud, and smart factories where IoT integration enables automated responses to production line issues,” he says. 

Going a step further, Versace highlights the proliferation of edge learning in conjunction with deep neural networks (DNNs) for improving machine vision applications. Traditional DNNs have been effective in controlled environments, but not as much so in real-world applications utilizing new untrained data, which is common in manufacturing, retail, and logistics because of constantly changing products, Versace explains. 

Integrating DNNs with edge learning, systems can learn directly on the device for real-time adaptation to new products, where traditional DNNs would have fallen short. This opens the door for more flexible systems for vision applications in logistics, inspection, and more.

“The drive behind this growth is the increasing demand for real-time, adaptive, and localized machine vision capabilities, where users need systems that can continuously learn and adapt to new information without relying on centralized computing,” Versace says. “Edge learning is becoming a practical solution for industries that need to stay agile in a rapidly changing environment.”

Lights, Camera, Action: Lighting and Camera Advances

When it comes to machine vision hardware, advances can be seen in almost every part of a vision system, from lighting and cameras to sensors and processing. Smart Vision Lights’ Kinney notes that elevated lighting and cameras will continue to drive advancement in machine vision technology. 

“The truth is that for machine vision cameras and lighting, though there will continue to be advances in the forefront of technology, most of this advancement will be incremental along known lines,” Kinney says. “The biggest expansion of machine vision lighting and cameras is likely going to be in configuring and optimizing products to more efficiently address the many new market areas that machine vison is expanding into.”

With the growing number of sophisticated vision applications comes a demand for clearer, more precise imaging. As a result, all types of machine vision technologies must advance, including lighting solutions.

“In machine vision lighting, the top companies have started moving toward LEDs using larger 2-mm dies, as compared to the 1-mm dies commonly found in the machine vision market now,” Kinney says. “These will further increase light output in continuous modes and offer even more advantages to getting extreme intensities in overdrive modes.”

Camera Technologies Capturing Attention

Complementing advanced lighting solutions are increasingly capable, high performing camera solutions. The Imaging Source’s Bergh notes two trending advancements in machine vision camera technologies: zoom cameras and event-based cameras.

Zoom cameras are ideal for applications with variable working distances, object sizes, and lighting conditions, including inspection, sorting, process control, and metrology. They offer efficiency gains in setup time, reduce the number of components required, and provide flexibility for multiple applications using a single device, Bergh explains. However, adoption can be complicated by mechanical constraints due to their larger size.

“Zoom systems can be highly beneficial in logistics, ITS, general monitoring, and even surgical applications, though they may face challenges like limited throughput during frequent focus shifts,” he says. 

Event-based cameras can capture asynchronous per-pixel changes, rather than traditional frames, to monitor brightness levels, eliminating motion blur while offering high dynamic range and temporal resolution. These cameras are showing potential in applications such as high-speed object tracking, including monitoring the trajectory of a golf ball, Bergh says.

“These are particularly valuable for applications requiring high-speed performance with lower latency,” he says. “They also reduce bandwidth usage while increasing efficiency, making them a great fit for certain computer vision tasks.” 

Sensing Advancement, Affordability

Advancing shortwave infrared (SWIR) sensing is another key piece of machine vision technology that can continue pushing the industry forward, according to Kinney. For example, Sony’s hybrid SWIR sensors and SWIR Vision Systems’ commercialization of quantum dot sensors are especially notable. Both advancements can help to reduce the cost of SWIR imaging and offer a wide spectral response of approximately 300–2,000 nm, Kinney reports. 

“Suddenly, we can now affordably image from UV through visible and all the way through the entire SWIR range using just one camera. Combining that with LED lighting and special filters can now offer unique new possibilities for multispectral imaging in a single camera system and at a price point not possible before,” he says.  Additionally, increasing multispectral imaging innovation and accessibility could help open the door to more challenging applications, like those with natural variations, such as agriculture and farming. 

“When I consider these new multispectral imaging possibilities with agricultural and farming applications, that is what I think will be most interesting for the future,” Kinney says. “A vision system may not just see in monochrome or color. Rather, the system might image in three to 10 spectral bands across UV to SWIR wavelengths that have been custom selected and tuned for the task at hand. Now, imagine pairing that kind of imaging technology with AI.”

These are just some of the most exciting advancements in the machine vision space. To see these and more of the latest machine vision technologies for yourself, join us at FOCUS, A3’s Intelligent Vision & Industrial AI Conference in Seattle this September 24-25.