Precision Agriculture

DATA ANNOTATION FOR Precision Agriculture

The use of AI in agriculture has exploded. Smart agriculture, otherwise known as smart farming, now deals with the complete cycle of growing and harvesting.

BOUNDING BOXES FOR OBJECT
DETECTION

POLYGON FOR IRREGULAR SHAPES
DETECTION

POLYLINE ANNOTATION FOR LANES
AND BOUNDARIES

BOUNDING BOXES FOR OBJECT DETECTION

POLYGON FOR IRREGULAR SHAPES DETECTION

POLYLINE ANNOTATION

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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 in many other applications. We handle tasks of any complexity by using various annotation techniques such as bounding boxes, polygon annotation, semantic segmentation, cuboid annotation, key points, and polylines.

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

AERIAL CROP MONITORING - AERIAL IMAGERY

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 have become widely used in precision agriculture. The data collected from drones helps farmers to achieve the best possible yields
Drones are often deployed to monitor plant health. This allows farmers to analyze crops as they grow, so that any problems can be quickly remedied.

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

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.

DRONES AND AERIAL CROP MONITORING USE CASES:

Scouting/Monitoring Plant Health

Monitoring Field Conditions

Planting & Seeding

Spray Application

Drone Pollination

DRONES AND AERIAL CROP MONITORING USE CASES

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GROWTH MONITORING

Monitoring crop growth and performance is an important aspect of agricultural management. It enables growers to implement timely interventions that ensure that
optimal yield is obtained at the end of the season.

Automation of growth monitoring by computer vision AI makes farms more efficient and is proven to be very effective in detecting issues in a timely manner whilst covering large territories.

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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

RIPENESS MONITORING AND RIPENESS DETECTION

Fruit maturity can be seen from skin color and size. Color is one of the most easily recognizable ways of determining whether fruit is ripe. Keymakr can label levels of ripeness, and classify growth stages. The quality of training data directly affects the performance of final models, therefore we take great care to precisely label your training images and video.

Different crops have different ripening processes, therefore ripeness detection training data has to be custom made for each ML model.

Keymakr specializes in image and video annotation solutions for AI in agriculture.

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CROP DISEASE DETECTION

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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 accurate training data is essential, as the quality of the training data directly affects the performance of crop disease detection AI.