Agriculture is one of the biggest industries in the world which undergoing an automation revolution, always keeping abreast of new technological advances. Precise methods and computers in farming increase the efficiency and profitability of agriculture by improving quality of yields and reducing operating costs.

The agricultural industry is highly dependent on human hands, because it requires constant supervision of crops and the collection of products. Farmers must systematically monitor the health and behaviour of animals in order to provide us with healthy meat and dairy products. But human capabilities are not unlimited, so losses in agriculture are often due to untimely decisions or inattention of workers. Therefore, the entire agricultural industry is beginning to switch to technologically sustainable solutions, such as AI and computer vision.

With the help of artificial intelligence solutions, we can reduce the time farmers have to work in the fields and allow them to focus on more pressing matters. The return on investment in the development of Computer Vision models will not have to wait long - because with this technology, production occurs on a large scale and efficiently. Technology has always had its place in agriculture, especially on a grand scale. Now, it is becoming more enhanced than ever.

Thus, artificial intelligence opens up new challenges, processing a large number of objects by using image classification - for example, various types of vegetables and fruits, or identifying defects, pests and diseases in them.

Smart agriculture uses many types of technologies for automation, but AI with computer vision is at the heart of many of the latest application developments.

Tractors drive automatically, drones determine the state of the soil, robots help milk, feed and harvest. Checking the quality of seeds and products also takes place using computer vision technology.

In this article, we will tell you about artificial intelligence technologies and what kind of applications farmers use.

Before moving on to technology, let's take a quick look at what precision farming is.

Table of content:

• Introduction
• What is Precision Agriculture?
• Computer Vision in Agricultural Automation
• Machine Vision for Agriculture Robotics
• Application of Computer Vision in Agriculture
• Computer Vision Startups in Smart Farming
• How can farmers implement Computer Vision Technology?
• Conclusion

What is Precision Agriculture?

The term precision farming came into use in the 90s, which means the agricultural automation of processes with the help of latest technologies.

The daily life of agricultural automation includes satellites, sensors and GPS navigators. And over the past few years, deep learning technology started developing in this area.

A large number of devices require additional technological solutions for being flexible and precise, and this is where artificial intelligence in smart farming has taken its place of honor.

Why it's called Precision?

Applications for agriculture must meet the specific requirements of different types of crops and animal species, being dynamic and collecting a large amount of data, which will ensure their health and good harvest. Collecting data must be precision for making better solutions.

Benefits of precision farming

- The purpose of smart and precise farming is to make the work of the staff easier by relieving them of the routine of daily work, automating repetitive processes improves the productivity of people, which leads to better harvests.

- Agricultural automation and robotics help keep the soil healthy, because with the help of the accumulated data, farmers know more precisely what state it's in. Healthy soil and precision farming solve the problems of sustainable development - environmental and economic. It is the way how we can fight the problem of hunger.

Computer Vision in Agricultural Automation

Computer vision is a technology that allows robots and machines to perceive the world around them pixel by pixel and mathematically, creating algorithms and models for a more precise understanding of images.

Computer vision models are trained using datasets for processing images, for example, animals. After that, they define algorithms that help them determine the image of the animal. With precision vision, you can remotely access your farm at any time with accurate insight into what's going on. Computer vision automation will help you get the necessary data about your animals, fields, or garden, allows you to track, annotate, identify, predict and evaluate specific objects using visual elements.

Deep learning technology is constantly evolving, and in the last few years, its algorithms can be relied upon, because they guarantee absolute accuracy. By imitating the human perception of the external world, robots and cameras are able to take into account all the characteristics of an object, their location, movement and appearance characteristics. You will know when pests appear on your field or when an animal gets sick and you can react immediately.

Machine Vision for Agricultural Robotics

Machine learning is key technology to the use of robots. With the help of its algorithms, robots are able to carry out and automate precise agricultural processes, from plowing fields to sorting and packing fruits and vegetables.

Robots need to see and feel space to navigate and avoid obstacles, which is why machine vision works in conjunction with GPS technology and 3D modeling. The image recognition process takes place with the help of classical algorithms that help to distinguish the necessary objects by color, shape and size. Convolutional neural networks (CNN) are trained by deep learning and help in the automatic navigation of agricultural robots.

Very popular are robots that are used to handle weeds. Machine Vision helps to maneuver between crops, identify and classify weeds in order to further control them by spraying or removal.

Cameras are installed from different sides for a more accurate understanding of the location of the robot in the field and rapid detection of weeds. Machine learning algorithms are calibrated and registered early on to communicate synchronously and accurately.

Applications of Computer Vision in Agriculture

Livestock farming

An important aspect of farming is not to prevent animals from living their normal lives in order to be able to watch them in peace. The cameras collect real-time data without disturbing the animals at all. With the help of analytics, you can make decisions about further improvements and treatment. You will always have the most accurate information about your animals, without missing a single event.

What farm can do with computer vision analytics?

• Targeted treatments

You can easily access the health of your livestock at any time with super accurate behavioural analytics. With Computer Vision, you optimise the process of using antibiotics, hormones and vaccines, knowing who needs to be given them and who does not. This, in turn, will protect the health of livestock from unnecessary harm from pills.

• Tracking the activity of animals at different stages of growth

It is important to know what stage the animal is at - fertilization, gestation or maternity, in order to monitor their activity and detect abnormal behaviour.

Crop monitoring

Drones are one of the key assistants in precision agriculture. The ability to see a field from a bird's eye view opens up new horizons for crop health analysis, a deeper understanding of the condition and potential threats. Drones with computer vision models cover vast areas in a short time, which significantly saves human resources.

Computer Vision in Autonomous Harvesting

An even more amazing intelligence system is the implementation of deep learning algorithms for robots that automatically pick vegetables and fruits. With the help of datasets, the computer vision model is trained to identify any type of fruit or vegetable, you just need to upload a huge number of their photos, and the model will create an algorithm for its image detection. You can set different times for autonomous harvesting individual classes of food, and train the model to detect unripe or diseased vegetables.

Harvesting with the help of computer vision technology covers all the necessary needs and exhausts the need in human analysis.

Yield Forecasting

Smart farming involves not only analysis, but also forecasting. Computer vision methods contribute to accurate yield forecasting, which in turn will help to avoid unnecessary waste in logistics and delivery.

By sharing raw data from robots and cameras, you can always calculate the amount of harvest, optimize the method of cultivation and engage in disease prevention.

Grading and Sorting

Manual traditional sorting requires a lot of labor, especially if it's a large farm. In addition, the image identification and classification of fruits and vegetables requires time and attention, which quickly exhausts workers.

With just a few cameras installed on your farm, you can evaluate different grading characteristics - by color, size and ripeness. Computer vision helps recognize crop needs for antibiotics or water.

Phenotyping in farming

Computers in farming are used not only for agricultural automation, but also for research purposes.

Agricultural machinery cameras can analyze and measure the necessary characteristics on how best to grow plants, what kind of environment suits them and general additional data for research.

Climate change is detrimental to agriculture, and phenotyping using computer vision will help preserve the crop, make it health and sustainable.

Sustainable Pest Management

Particularly noteworthy are Computer Vision models that detect pests, because they threaten crop fertility as much as drought. According to statistics, about 40% of the annual crop production in the world is damaged by pests. Diseases spread by pests cost the economy more than $540 billion.

Various insects live off the leaves, extracting food from them, because of this, the process of photosynthesis is poor, the plants begin to get sick, which leads to huge crop losses.

Pest monitoring is usually done manually by workers, but this method is extremely damaging to crops because detection occurs too late. It is difficult to notice small bugs with human eyes, but this task is easily accomplished with camera.

Using Computer Vision technology on farms will allow you to produce products with less damage, that will help to increase earnings.

Species detection

We still need to track down a huge number of rare species, and their survival depends on our deep research. Of the total number of rare animal species, we have studied only 17%.

Computer vision helps to capture even the rarest animal species by training a model with photographs of that species.

Also, the use of manual research threatens species due to contact with people and stress for animal or deliberately allow access to poachers.

Implementation of such sustainable technology as Computer Vision eliminates all of this risks, cameras can reach the most inaccessible places, can work day and night and do not disturb animals at all.

The way Computer Vision works  

Video Cameras

It is not necessary to buy expensive cameras with built-in technology in order to start monitoring your animals in real time and conduct appropriate analytics. The uniqueness of Computer Vision models is that they can be installed on any low-cost CCTV camera.

The installation of models is implemented in two ways:

Server Installation

Thus, the model is installed on the server, it can be Azure, AWS or Digital Ocean. The connection is implemented through the interface provided by the model. After training and setting up the model, data is exchanged on the server, you provide video, and in return you receive ready data about the object.

Installation on the edge

This type of installation provides instant data processing, as they are processed directly in the device. Particularly useful type where you need to receive real-time data and receive notifications. Supports various types of devices - Raspberry Pi, Google Coral або NVidia Jetson.

Drones  

Turn your drone into a smart assistant. Flying robots with embedded computer vision technology will help solve problems 5 times faster than manual work. Manual video analysis from a drone takes a long time, and with the help of computer vision technology, you will immediately receive outgoing data in a report.

Farming Robots  

Robots can do more if they are intelligent. You can extend the capabilities of your existing robot by introducing models to help it with image processing, classification, and segmentation. Make your robot flexible in making decisions, let it perform more tasks, thereby automating agricultural processes.

Startups with Computer Vision in Agriculture industry 

Serket

A technology company that was created by farmers is perfectly aware of all the needs and needs of industry. Serket is developing a sensor less artificial intelligence that uses a video camera to improve farm performance in real time, with a particular focus on pig production.  

The main functions of video surveillance are to monitor changes in the health of pigs, reproduction and the general environment in which pigs live. Computer vision technology helps convert visual information into data that helps make further improvement decisions.  

Cattle Eye

An Irish startup that, like no one else, knows the need for robot automation, because the creator is the son of a farmer. Their main goal is to make video analytics available to every farmer, because any cheap camera can be used to install a computer vision model, and all data is processed in the cloud. This startup specializes in cows, artificial intelligence is able to identify cows, giving each of them a special ID, and then monitors their behaviour and health, collecting all the information into analytics.  

Onecup  

Agriculture startup that has patented its 360 visual live id platform specially designed for the farm, which effectively recognizes the faces of animals and is able to move around the entire field. The main advantage of this technology is that it can recognize a large number of animals at a very long distance and accurately track the activity of each of them - health, activity, nutrition.  

Conclusion     

There is not a single industry that has not been touched by Artificial Intelligence. And as we learned from this article, the use of these sustainable and precise technologies helps our ecology and society achieve their goals.  

We at ByteAnt have been developing Computer Vision models for the past few years and have experience with such a fast-growing industry as agriculture.

Contact us to discuss your ideas for developing Сomputer Vision models individually for your case and start automating your business to achieve goals and reduce costs. And also you can take a look at our ready models on our landing page.