Be first to read the latest tech news, Industry Leader's Insights, and CIO interviews of medium and large enterprises exclusively from CIO Applications
As precision agriculture evolves, machine vision is sure to play a vital role in the automation of agricultural operations.
FREMONT, CA: Agriculture is experiencing a revolution. Through improved yields and lower operating costs, precision techniques are creating greater efficiencies and profitability in agriculture. While there are many automation technologies used, machine vision is at the core of many of the latest technological developments in agriculture. In agriculture, machine vision offers some unique advantages that can ease farming and enhance productivity. Three promising applications of machine vision in agriculture are listed below.
• Predicting Weather Trends
The metrological applications of machine vision have been around here for years. Using complex methods of survey and sophisticated imaging technologies, machine vision can be used to predict weather conditions. Weather plays a significant 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.
• Preparing the Field
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.
• Sorting Harvested Crops
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.
Machine vision is enabling automated processes in agriculture, opening the door to greater efficiency and profitability.