Precision Agriculture

Training Data for AI in Agriculture

Move the bar to see the annotation magic happen

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!

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

Move the bar to see the annotation magic happen

Ripeness monitoring and ripeness detection

Move the bar to see the annotation magic happen

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.