Data Annotation Services for Smarter Agriculture

We provide training data for agriculture to teach your model to perform agriculture-related tasks, paving the way for automated farming of the future.

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Agriculture

We Scale Teams for:

ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX
ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX
ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX

Data Annotation for Agriculture Industry at Label Your Data

Data annotation for the agriculture industry implies labeling of images, videos, or sensor data with information such as crop types, disease detection, pest identification, growth stages, and yield estimation. Precise and comprehensive annotations facilitate the advancement of technologies in smart farming and precision agriculture.

Our skilled annotators at Label Your Data apply their domain knowledge to deliver quality and secure data annotation in farming. Additionally, we employ data labeling tools to ensure consistent and reliable results, providing our clients with custom training datasets to meet their agriculture data annotation needs.

svg On photo: Olha, Karyna

Our Data Annotation Services for Agricultural Companies

The Label Your Data team is skilled in the most sought-after types of data annotation services in agriculture, as well as data processing services for the agribusinesses. Whether you require agriculture data annotation services or any other data processing services, our annotators are here to provide high-quality solutions tailored to your specific project needs for AI in farming.

Our clients mostly require labeled data for Computer Vision for their harvesting or crops selection projects. Hence, we tend to do:

Semantic Segmentation for Crop Monitoring

Semantic Segmentation for Crop Monitoring

Image Categorization for Livestock Management

Image Categorization for Livestock Management

Bounding Boxes for Pest & Disease Detection

Bounding Boxes for Pest & Disease Detection

Polygonal Annotation for Field Mapping

Polygonal Annotation for Field Mapping

Semantic segmentation helps your computer vision 
                        models accurately analyze and monitor different crop regions 
                        in images, enabling precise crop monitoring and targeted 
                        interventions.

Semantic segmentation helps your computer vision models accurately analyze and monitor different crop regions in images, enabling precise crop monitoring and targeted interventions.

Get Semantic Segmentation
With image categorization, your model can 
                        classify livestock images based on breeds, health conditions, 
                        or specific characteristics, supporting effective livestock 
                        management practices.

With image categorization, your model can classify livestock images based on breeds, health conditions, or specific characteristics, supporting effective livestock management practices.

Get Image Categorization
By employing bounding box annotation, the models can 
                        detect and localize pests, diseases, or symptoms in crops, facilitating 
                        early identification and targeted treatment.

By employing bounding box annotation, the models can detect and localize pests, diseases, or symptoms in crops, facilitating early identification and targeted treatment.

Get Bounding Boxes
Polygonal annotation enables AI to map and delineate specific 
                        field areas or boundaries, aiding in crop planning, resource allocation, 
                        and precision farming practices.

Polygonal annotation enables AI to map and delineate specific field areas or boundaries, aiding in crop planning, resource allocation, and precision farming practices.

Get Polygonal Annotation
Semantic Segmentation for Crop Monitoring

Semantic Segmentation for Crop Monitoring

svg
Semantic segmentation helps your computer vision 
                        models accurately analyze and monitor different crop regions 
                        in images, enabling precise crop monitoring and targeted 
                        interventions.

Semantic segmentation helps your computer vision models accurately analyze and monitor different crop regions in images, enabling precise crop monitoring and targeted interventions.

Get Semantic Segmentation
Image Categorization for Livestock Management

Image Categorization for Livestock Management

svg
With image categorization, your model can 
                        classify livestock images based on breeds, health conditions, 
                        or specific characteristics, supporting effective livestock 
                        management practices.

With image categorization, your model can classify livestock images based on breeds, health conditions, or specific characteristics, supporting effective livestock management practices.

Get Image Categorization
Bounding Boxes for Pest & Disease Detection

Bounding Boxes for Pest & Disease Detection

svg
By employing bounding box annotation, the models can 
                        detect and localize pests, diseases, or symptoms in crops, facilitating 
                        early identification and targeted treatment.

By employing bounding box annotation, the models can detect and localize pests, diseases, or symptoms in crops, facilitating early identification and targeted treatment.

Get Bounding Boxes
Polygonal Annotation for Field Mapping

Polygonal Annotation for Field Mapping

svg
Polygonal annotation enables AI to map and delineate specific 
                        field areas or boundaries, aiding in crop planning, resource allocation, 
                        and precision farming practices.

Polygonal annotation enables AI to map and delineate specific field areas or boundaries, aiding in crop planning, resource allocation, and precision farming practices.

Get Polygonal Annotation
Domain Expertise

Domain Expertise

Our extensive experience in AI and agriculture projects allows us to deliver exceptional data annotation services tailored specifically to the needs and requirements of the agricultural industry.

Flexible Business Model

Flexible Business Model

With our flexible business models, we excel in short-term POC or R&D agricultural initiatives. Clients often require precise computer vision algorithms to address specific needs, and we efficiently annotate limited datasets within tight timelines.

Adjustable Timeframes

Adjustable Timeframes

Our agile timeline adaptation ensures a quick project launch (typically 1-3 weeks from initial interaction) and efficient completion within desired timelines. Leveraging expertise and a robust annotation environment, we prioritize timely delivery to meet client requirements.

Why Choose Agriculture Data Annotation Services at Label Your Data?

Our company holds certifications for PCI DSS (level 1) and ISO:27001, and we adhere to the regulations outlined by GDPR, CCPA, and HIPAA. With 10+ years of experience and 500+ specialists on board, we provide customized data annotation services in agriculture for enterprise and R&D projects in 55 languages.

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FAQs

What is the role of AI in agriculture?

The transformative power of AI in agriculture encompasses precision farming, optimized crop management, and data-driven decision-making, revolutionizing the industry. Yet, no AI project can be feasible without high-quality data annotation in the agriculture industry.

What data is important to farmers?

Accurate and timely data is crucial for farmers to optimize agricultural productivity by informing decisions on weather, soil moisture, crop health, and market trends. Professional data annotation services in farming can greatly leverage this data for AI systems.

How are data processing and annotation used in farming?

Data processing and data annotation in agriculture are essential, as they convert raw agricultural data into structured datasets for valuable insights, AI model training, and optimized farming practices like crop management, resource allocation, and pest control.