Agriculture

High-quality data labeling for agriculture and smart farming - from ripeness monitoring to crop management, pollination control, and other applications.

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Expert Labeling
of Agricultural Data

Agricultural Data Annotation services for a wide range of computer vision applications.

Agriculture is both a major industry and a foundation of the economy. New technologies are starting to be widely used in the industry to increase crop yields and profitability. Artificial Intelligence is steadily becoming commonplace.

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, pest control, weeding, and many other applications.

We are experienced in agriculture image data annotation and agriculture video data annotation. Take a look at these different annotation types that may help your project:

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Agriculture
Annotation Types

01. Automatic Annotation

Fast AI-assisted labeling for your smart farming systems - our team will validate every single image for quality control.

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Automatic Annotation

02. Bounding Box

Label individual objects such as fruits and vegetables with the help of bounding boxes to help your AI understand them.

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03. Oriented Bounding Box

Place objects in your farming images or videos at the right angle for additional accuracy during the training process.

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Oriented Bounding Box

04. Cuboid

Extrapolate additional dimensions such as the width and height of your crops to more accurately reflect the real world in your computer vision.

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05. Polygon

Outline irregular shapes for relevant objects such as vines, weeds, leaves, stems, pests, non-standard crops, and so on.

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06. Semantic Segmentation

Catalog entire scenes from your farming data by classifying objects - helps your systems distinguish between sets of items.

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Semantic Segmentation

07. Instance Segmentation

An advanced form of segmentation for individually labeling every single instance of an object found in your farming data.

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Instance Segmentation

08. Skeletal

Uses connected lines to label human-like shapes such as your farm workers - helps AI understand position, activity, etc.

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09. Key Points

Label individual points of interest in your farming images or videos for a detailed analysis of tiny pests, invasive species, and so on.

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10. Lane

Label lanes such as your crop fields or footpaths so that your AI can better understand the infrastructure it’s working with.

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11. Bitmap

Annotate separate parts of the same object such as parts of the same field cut in half by a different object in the foreground.

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12. Custom

Combine different types of data annotation to create custom agricultural training data. Teach your AI specific things you need it to understand.

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12

Need
custom solution

Professional Data Annotation
for Agriculture

Get accurate data for your smart farming systems from experienced annotation teams!

01. Aerial Crop Monitoring

Information gathered by drones on farms is important for making better agronomic decisions. It’s a major part of the system generally referred to as ‘precision agriculture’.

Drones are a staple of precision agriculture. Data collected from drones enables farmers to achieve better yields and powers all sorts of automated systems. Drones are commonly used to monitor plant health, which helps create a sort of emergency response system - any crop-related problems can be quickly remedied.

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

02. Drones vs Satellite Imagery

Drones vs Satellite Imagery

Many farmers already use satellite imagery to monitor crop growth and track their density and coloration. However, accessing satellite data is costly and often not as effective as clear aerial imaging . Because drones fly close to fields, cloud cover, and poor light conditions matter less than when using satellite imaging. That said, all types of data have their place in agricultural AI training.

03. Drone and Aerial Crop Monitoring Use Cases

Drone and Aerial Crop

Access to data enables farmers to do all sorts of powerful things with AI-based automation:

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

04. Growth Monitoring

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 promptly whilst covering large territories.

Real-time information on crop development
Monitor and label stages of growth
Monitor plant health

Immediate detect situations like:

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

05. Ripeness Monitoring and Ripeness Detection

Ripeness Monitoring

Fruit maturity can be seen from skin color and size. Color is one of the easiest ways of determining whether fruit is ripe and ready for collection. Keymakr can label levels of ripeness and classify growth stages for you. The quality of your training data directly affects the performance of final models, so we take great care to precisely label your training images and videos.

Different crops have different ripening processes, so our ripeness detection training data has to be custom-made for each ML model. We specialize in image and video annotation solutions for AI in agriculture, so chances are we already have access to useful datasets that apply to your niche.

06. Crop Disease Detection

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.

Accurate training data here is even more crucial than in other areas, as the quality of your dataset directly affects the performance of your crop disease detection systems.

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"Delivering Quality and Excellence"

The upside of working with Keymakr is their strategy to annotations. You are given a sample of work to correct before they begin on the big batches. This saves all parties time and...

"Great service, fair price"

bility to accommodate different and not consistent workflows.
Ability to scale up as well as scale down.
All the data was in the custom format that...

"Awesome Labeling for ML"

I have worked with Keymakr for about 2 years on several segmentation tasks.
They always provide excellent edge alignment, consistency, and speed...

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Keylabs

We created a proprietary annotation platform with enterprise-grade tools capable of achieving pixel-perfect labeling for all your AI training datasets.

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Keylabs can help you handle annotation projects of any complexity. Our platform was originally created for internal use, so usability and quality are main priorities. Keylabs features a full suite of annotation techniques that can be adapted for your specific needs.

You can work with projects of any size and label vast quantities of data. The in-built management tools will help you collaborate on projects and get the most out of your data.

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