Precision Agriculture
Training Data for AI in Agriculture



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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.
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AERIAL CROP MONITORING - AERIAL IMAGERY
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
Growth monitoring
Monitoring crop growth and performance is an important aspect of agricultural management. It
enables the farmer to implement timely interventions that ensure that optimal yield is obtained
at the end of the season.
Automating growth monitoring AI can improve the identification of issues in time and allow the
appropriate interventions to be implemented.
Automation of growth monitoring by computer vision AI saves time and is proven to be very
effective in detecting issues in a timely manner and covering large territories.
- Real-time information on crop development
- Monitor and label stages of growth
- Monitor plant health
- Immediate detection of:
- Poor water availability
- Nutrient deficiency (e.g. artificial fertilizer or manure)
- Uncontrolled use of chemicals (toxicity)
- Fungal, bacterial or viral infection
- Attack from insects or other organisms, above or below the ground



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Ripeness monitoring and ripeness detection



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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.
Crop disease detection
Precision agriculture uses AI technology to aid in detecting diseases in plants, pests, and poor
plant nutrition on farms.
AI sensors can detect and target weeds and then decide which herbicides to apply.
Preparing correct training data for AI is a very important part as the quality of the training
data affects the results and performance of the crop disease detection AI.



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