Agriculture is both a major industry and a foundation of the economy. New technologies are starting to be widely used in traditional agriculture to increase crop yields and profitability. As a result, Artificial Intelligence is steadily emerging as part of the industry’s evolution.

Keymakr creates custom agriculture training datasets that can be used in agricultural robotics, crop health and soil monitoring, field monitoring, growth progress detection, ripeness detection, unwanted plants and weeds detection, and many more. We handle tasks of any complexity by using various annotation techniques such as bounding box, polygon annotation, semantic segmentation, cuboid annotation, key points, and polylines.

Keymakr is experienced in agriculture image data annotation, agriculture video data annotations. Leave your details below to speak with us and schedule a free demo!

Fruit maturity can be seen from skin color and size. Color becomes one of the easily recognizable traits to determine whether the fruit is ripe. Keymakr will label the levels of ripeness and classify growth stages. The quality of the training data directly affects the performance of the therefore we take a great care in precise labeling and creating highest quality training data for computer vision AI.

Different crop’s ripening process differs from each other and fairly unique, therefore ripeness detection training data has to be custom - made for the ML model.

Keymakr is specializing in AI solutions for agriculture with image and video annotation and is experienced in agriculture visual data annotation.

Drones Vs Satellite Imagery

Many farmers already use satellite imagery to monitor crop growth, density, and colouration, but accessing satellite data is costly and not as effective in many cases as closer drone imaging. Because drones fly close to fields, cloud cover and poor light conditions matter less than when using satellite imaging.

The information gathered by drones on farms is important for making better agronomic decisions and is part of a system generally referred to as ‘precision agriculture’.

Drones had become widely used in precision agriculture. The data collected from drones helps farmers to achieve the best possible yields.
Drones are widely used for Monitoring plant health. This allows farmers to monitor crops as they grow so any problems can be dealt with fast enough to save the plants.

Providing accurate field mapping including elevation information that allow growers to find any irregularities in the field.
Pollination sprayers are able to navigate very hard to reach areas, such as steep tea fields at high elevations.

Drones and aerial crop monitoring use cases:

• Scouting/Monitoring Plant Health
• Monitoring Field Conditions
• Planting & Seeding
• Spray Application
• Drone Pollination