A new generation of computer vision based AI applications is making agriculture more efficient and more profitable. Computer vision models can monitor crops and livestock and accurately determine how well crops are growing and how healthy animals are. Precision agriculture technology has exciting potential.
AI supported farming can play an important part in securing the food supply as the global climate changes. Precision AI models need precisely annotated image and video data to function well. By adding information to images and videos human annotators help AI models to learn what to look out for in real world farming environments.
This blog will focus on polygon annotation, a specific annotation technique that is crucial for precision agriculture AI.
Firstly, we will define polygon annotation. Secondly, we will highlight some of the promising use cases that this annotation technique enables. Finally, we will look at annotation tools and show how the right platform can make precision agriculture development simpler.
Polygon annotation definition
AI developers use polygon annotation to highlight the outlines of complex or irregular shapes in digital. Annotation tools are used for polygon annotation. With the help of these tools annotators connect multiple small lines together around the edge of target objects.
This annotation technique is more precise than bounding boxes because it locates the exact pixel edge of an object. In training images and videos for precision agriculture AI, the polygon annotation technique is used to precisely show the shape of fruits, vegetables, and even livestock.
Polygon annotation supports these use cases
Polygon annotation can accurately outline any shape. In the agricultural sector polygon annotation is being used to train models with a number of important applications:
- Livestock management: Livestock management systems are helping to increase yields and improve animal welfare on farms. AI models can identify individual animals and find missing herd members. AI powered cameras also know if an animal is sick or injured.
This information can be used by herd managers to prevent further harm. Polygon annotation is used to train livestock management AI because it can accurately define the irregular shapes of different species and breeds of animals.
- Aerial crop monitoring: AI powered drones allow farmers to monitor larger areas of crops. Drones can chart plant growth, identify diseases, gauge field conditions, and tell farmers where to spray.
However, the success of this technology relies on polygon annotation. Annotators use polygon annotation to highlight target crop areas in training images.
- Ripeness monitoring: AI models can be used to gauge when fruit is ripe and ready to be harvested. They do this by identifying the colour of the fruit and working out how ripe it is according to that colour.
Polygon annotation is used to highlight the outline of individual fruits in digital images. Highlighted fruits are then colour coded according to the ripeness levels.
Data annotation tools for precision agriculture
Image and video annotation tools help precision agriculture developers to create exceptional training datasets. The future of agriculture depends on high functioning computer vision models.
Keylabs is a data annotation tool designed by image and video labelling experts. Keylabs offers precision agriculture AI companies the following key benefits.
- Quick outlining functions: Augmented annotation algorithms recognise the shape of a selected object and snap a polygon annotation to fit automatically. This function increases annotation speed.
- Workforce analytics: Keylabs has unique workforce analytics features. Managers can look at annotation performance in detail, and give annotation tasks to the highest performing workers.
Fast options: Keylabs is designed with speed in mind. For example, each annotation operation is coupled with efficient keyboard hot keys.