AI has made the lives of companies across industries a lot easier since it allows them to automate a lot of time-consuming and mundane processes.
However, these robots need to be trained with data that allows them to recognize and function in the physical world. This is where data annotation services come in.
Today we will take a look at how data annotation speeds up the development of AI robots and the types of data annotation that are necessary.
Why is Data Annotation Necessary for the Development of AI Robots?
When a company decides to implement an AI robot to perform a specific task, the robot needs to know what to look for and how exactly the task needs to be done.
For example, let’s take a look at a robot that can pick apples from a tree. The robot first needs to navigate its way to the tree, then identify an apple, determine if it’s ripe, pick it from the tree, and put it in the basket.
The robot needs to be trained on how to do all of these tasks, which is why companies obtain sizable datasets to train the robot to understand all of the possible circumstances it can encounter. Let’s take a look at some of the many data annotation types that are necessary to train such a robot.
What Types of Data Annotation are Necessary to Train AI Robots in the Agriculture Industry?
Let’s start with the navigational aspect. For the robot to understand its surroundings, 3D Point Cloud annotation is needed. This 3D Point Cloud is created by a LiDAR and is a digital representation of how the robot sees the physical world.
Human data annotators would need to label all of the objects in the images, such as trees, various types of fruits, vegetables, and anything else in the image.
They might also need to color code the images depending on the proximity of the objects to the robot. For example, objects that are close will be in blue since this color has a short wavelength. Conversely, objects farther away will be in orange or red.
In addition to this, image annotation will be necessary since the robots need to know which fruits or vegetables are ripe. For example, it’s not enough to say that a tomato needs to be red because there are many shades of red.
Therefore, human data annotators would need to label the ripe fruits and vegetables in the images. Also, they would need to perform semantic segmentation to differentiate the various shades of colors. Since this is a very time-consuming process, a lot of companies choose image annotation outsourcing providers to perform these tasks.
Data Annotation is One of the Most Important Aspects of AI Robotics Development
The usefulness of AI robots is only as good as their accuracy. If the robot cannot locate the fruit or distinguish between ripe and unripe fruits or vegetables or properly perform a critical task, it does not offer a lot of value to the users.
Therefore, quality data annotation is important since the outcome of the project is at stake. It is also very important to choose the correct data annotation services provider from the very beginning to avoid costly mistakes and having to redo some or all of the project.