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USE CASE

Computer Vision on Plantations

Segmentation of walnut tree plantations
Computer Vision The ability to recognize and analyze images and videos
Segmentation The division of an image into semantically independent segments to determine object boundaries
Data labeling The process of marking objects in photos to train a system to recognize and interpret them
2000
photos
2
weeks

CASE DESCRIPTION

Semantic segmentation of roads, fruits, and tree canopies using polygons

Detailed annotation of each polygon in CVAT

The data is used for training an automated fruit picking system

APPLICATION AREAS OF THE DATASET

Harvesting Automation:

emantic segmentation of plantations for training fruit-picking systems

Plant Health:

Classification of visual disease symptoms in plants for timely treatment

Weed Control:

Automated detection of weeds in plantations for immediate removal

FACTORS AFFECTING THE FINAL PROJECT COST INCLUDE

Our data quality guarantee is 95%. For annotation orders requiring quality above 95%, we offer enterprise solutions

Why
Training Data

  • Quality Assurance:
  • Enhanced Data Accuracy
  • Consistency in Labels
  • Reliable Ground Truth
  • Mitigation of Annotation Biases
  • Cost and Time Efficiency
  • Data Security and Confidentiality:
  • GDPR Compliance
  • Non-disclosure agreement
  • Data Encryption
  • Multiple data storage options
  • Access Controls and Authentication
  • Expert Team:
  • 6 years in industry
  • 35 top project managers
  • 40+ languages
  • 100+ countries
  • 250k+ assessors
  • Flexible and Scalable Solutions:
  • 24/7 availability of customer service
  • 100% post payment
  • $550 minimum check
  • Variable Workload
  • Customized Solutions

Team leads project

Anton Tseluiko
Operations manager
Arthur Kazukevich
Python-developer
Daria Yurkevich
Quality Control Manager
woman

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