Can Agriculture Benefit from Machine Vision?

By Embedded Advisor | Tuesday, February 18, 2020

Potential use cases of machine vision in the agricultural sector might change traditional farming practices forever.

FREMONT, CA: Images are powerful mediums of information. When machines are supplied with the intelligence to create and study images using analytical prowess, it is tagged as machine vision. The kind of functionality that machine vision brings is unmatched and has helped the technology find application across sectors. In agriculture, machine vision presents some unique advantages that can simplify farming and enhance productivity. With the ability to recognize and classify visual information, machines are now closer to human beings in terms of cognitive ability. Some promising applications of machine vision in agriculture are listed below.

• Detecting Weather Patterns

The metrological applications of machine vision have been around for some time now. Using complex methods of survey and sophisticated imaging technologies, machine vision can be used to predict weather conditions. Weather plays a major role in crop cycles. When machine vision is combined with AI and ML, machines are able to determine weather trends with high degrees of accuracy, enabling better decision-making in agriculture.

• GroundWork for Agriculture

The tasks of sowing seeds or preparing the field for sowing are particularly taxing for farmers. Although the equipment has eased the job substantially, machine vision is about to bring further improvement. By deploying autonomous machines that have machine vision built into them, agricultural work can be automated. Owing to the highly advanced machine vision, farmers can expect robots to sow seeds with precision and deliver efficient results. Machine vision can also equip agricultural robots to identify weeds and thus enable automated weed control and removal efforts as well.

• Picking Ripe Produce

This is probably the most interesting application of machine vision in agriculture since it requires high degrees of image processing capabilities for machines to identify metrics according to which fruits and vegetables can be harvested and sorted. With machine vision, robots are empowered to gauge factors like color, quality, and maturity.

As the agricultural sector prepares to embrace robotics, machine vision will become indispensable to make robots credible.