Imagine if you can visualize your livestock growth rate and predict their weight several days before being sold. Then, you can sell them at a higher price and improve your business efficiency. This is what happens through applications of machine vision.
Business research company says, From $12.09 billion in 2021, the global market for machine vision is predicted to grow to $13.52 billion in 2022.
Farmers are using new machines and technologies to operate more efficiently and successfully. With precision farming, farmers can increase yields, save money, and develop better stewardship of our natural resources.
While drones and driverless tractors grab attention of many people, machine vision also plays a key role in precision agriculture. In this blog you can see how machine vision works in agriculture.
5 Up to date Machine vision applications in precision agriculture
Source : John Deere
Autonomous tractors are about to revolutionize farming. An autonomous tractor can navigate and drive by itself without human control. It uses sensors to detect environment around itself and interprets data to understand its current location. It then makes a decision regarding its next position based on mission goals. Once decisions are made, it takes action by sending commands to actuators, understanding vehicle to move as instructed. Machine vision is an extensively applied perception task in autonomous vehicles for localization, mapping, obstacle avoidance, lane change, etc.
Identification and monitoring of livestock is a key task in animal husbandry. It is often carried out by people. This process can be more accurate and efficient using machine vision.
The use of machine vision is gaining popularity among agricultural producers globally. It can be implemented in various applications ranging from animal health to food safety to herd management.
There is a huge market for livestock identification. Of course the most obvious target is $100bn livestock industry. Still you could do many other things with machine vision in agriculture.
Source : sifted
Grading and Sorting
Predicting how long crops will last, how many fruits and vegetables are on trees, and whether products will be of high quality is vital in crop management.
While working on a crop sorting line, we often see that workers are too tired to pay attention and sort grain with a quick look at it. We also noticed that some good grains were sorted as bad ones and some bad ones were sorted as standard ones. This process could raise shipment costs and give us lower quality scented grains.
Machine vision can sort good crops from bad crops. In combination with deep learning techniques and robots, seeds can be sorted. Fruits can be graded for market, and restaurants can prepare dishes more quickly.
source : plant-phenotyping
Phenotyping is an integral part of crop breeding. It’s vital to ensure that only the best plants are grown. Phenotyping data helps us identify which plants are most worth cloning.
Phenotyping makes it possible to determine how crops respond to different conditions and picks best seedlings to grow based on results.
We are automating many processes that previously needed humans. Field robots allow farmers to save time and money, as these processes take less time than traditional agricultural equipment. We can also produce more by using field robots.
There is a growing opportunity for field robots to automate harvesting, planting, weeding, fertilizing, etc. Typically, these machines are controlled by machine vision automation technology.
source: NY Times
Automation is key to increasing precision agriculture cash flows, yields, and labor productivity. Machine vision is an ongoing technology developed to automatically recognize and interpret images obtained by one or several cameras. It finds new applications in various domains, notably in precision agriculture, as it progresses.
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