By Aaron Hand
The agriculture industry faces mounting pressures from a growing global population, unpredictable weather patterns brought on by climate changes, and increasing demand for sustainable practices. In this environment, machine vision has an opportunity to transform the industry.
Recent advances in machine vision technologies — such as deep learning, hyperspectral imaging, and real-time processing — have opened new doors for precision agriculture. These innovations are being harnessed in various applications, including crop monitoring, automated harvesting, weed detection, and livestock management. Whether identifying diseased plants before they spread or ensuring that crops are harvested at peak ripeness, machine vision enhances both productivity and sustainability.
The path to full-scale adoption is not without challenges, however. High implementation costs, the need for skilled operators — and even just the natural variability in agricultural environments and output — pose significant hurdles. Despite these obstacles, ongoing research and investment move us toward a future where machine vision becomes an integral part of agriculture.
Machine Vision’s Capabilities
Most advanced farming operations are at least familiar with the proficiencies of machine vision in the agriculture industry, says David Dechow, founder and owner of Machine Vision Source, a machine vision integrator.
But adoption varies significantly across regions and farming types, says Minna Törmälä, global marketing manager for Specim, which specializes in hyperspectral imaging systems. Industrial-scale farms and vertical farming tend to lead the way. “There is still significant educational work to do — particularly for smaller-scale and traditional farms — to understand how machine vision can improve profitability and sustainability,” she says.
Applications and their benefits run the gamut across the agricultural landscape, Törmälä points out. In the field, machine vision can be used to monitor crops to detect nutrient deficiencies, diseases, or pest infestations early, and provide a better understanding of where to apply fertilization or pesticides. It can assess plant health and maturity to estimate yields more accurately and plan harvests accordingly. Soil and water analysis using machine vision can help optimize irrigation and soil amendments.
“Vision has been deployed for some time in the execution of precision agriculture,” Dechow says. “Drones with spectral or hyperspectral imaging scan fields to detect areas of slow growth, disease, and low water availability or consumption.”
Postharvest, machine vision plays an important role in sorting and grading by providing automated inspection for safety and quality standards. “Besides detecting contaminants and defects, hyperspectral imaging can identify spoilage, bruising, internal defects, and measure even taste in fresh fruits and vegetables,” Törmälä notes.
In addition to grading and defect detection, some of the more mature uses of machine vision include guidance for milking operations and quality evaluation in egg production and packaging, Dechow notes.
The Benefits to Be Reaped
One of the biggest reasons farming operations are turning to machine vision is the inability to get enough farm labor to otherwise get the work done. Automation can reduce labor costs, particularly at larger scales. But in many cases, operations simply can’t find the workers.
Nature Fresh Farms, based in Ontario, Canada, is one of North America’s largest greenhouse farms, growing organic berries, peppers, tomatoes, and cucumbers. The company turned to robotics and machine vision for its harvesting operations largely because of its struggle to find labor, according to Cornelius Neufeld, executive vice president for Nature Fresh.
“Our biggest challenge is labor,” he says in a video case study with FANUC and system integrator Four Growers. “As this sector grows more and more and more, we really need more automation to do all the jobs. We just don’t have enough people in this industry anymore.”
But labor issues are certainly not the only reason to turn to machine vision in farming. Imaging techniques can address critical challenges in crop monitoring, yield optimization, and resource efficiency. “The agriculture industry increasingly recognizes machine vision’s potential for providing detailed insights into plant health, soil conditions, pest detection, and post-harvest quality control,” Törmälä says.
Healthier crops, better use of precious resources, and consistent quality are key benefits of using machine vision in agriculture, she adds.
Applications Down on the Farm
Dechow has been involved in a number of traditional agriculture projects, some of which involve sortation and damage detection of eggs prior to packaging and robotic handling of fruits and vegetables (including apples and brussels sprouts) for automated cutting and trimming.
Not all applications yield good results, however, he says. “That’s not necessarily due to limitations in machine vision alone,” he says. “In several cases, the capability of mechanical automation becomes the main challenge, particularly with respect to speed of process, reliability, and cost. In robotic handling, for example, gripping technology has sometimes been an important obstacle.”
In general, in-field vision-guided robot harvesting for fruits and vegetables is not particularly ready for prime time, Dechow contends. Vision still faces some limitations in the field — as do automation and gripping. “Overall, too, the speed of processing using a robot is, in most cases, way too slow for the system to attain a return on investment,” he adds.
Success in a Controlled Environment
Where machine vision is finding more success so far is largely in vertical farming, where more controlled environments provide the consistency needed.
There’s already a push toward controlled environment agriculture, according to Interact Analysis, to counteract various factors brought on by climate change, such as reduced water availability and degradation of topsoil, at a time when we have an increasing number of mouths to feed.
“Controlled environment agriculture enables a more efficient use of key resources,” notes Blake Griffin, research manager for Interact Analysis. “Using carefully controlled water, air, and lighting systems, these growing environments can produce food at a consistent rate and can more easily eliminate pests and disease from plants.”
Such environments also help create the right surroundings for machine vision applications, Dechow notes. “Applications in vertical farming have specific potential in that the product is very constrained while growing,” he says. “Machine vision-enabled planting and harvesting are strong value-added solutions for this industry, and the more organized structures for growing in vertical farming lend themselves to more reliable automation.”
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