Streamlining Agricultural AI with Multi-Dataset Annotation

Client's Challenge & Our Solution

A leading agricultural technology firm focuses on developing AI-driven solutions for livestock monitoring and crop health management. They required annotated datasets to train two AI models: one for tracking and analyzing livestock and another for assessing fruit growth and health.

Dataset 1: 15,000 images of livestock, including animals like cows, goats, sheep, chickens, horses, and pigs, required bounding box annotations to localize and identify individual animals for behavior and health tracking.

Dataset 2: 8,000 images of fruit-bearing plants (apples, bananas, oranges, grapes, and cherries) needed polygon annotations to precisely delineate fruits for ripeness assessment and yield prediction.

FutureBeeAI leveraged its domain expertise and proprietary tools to execute these tasks efficiently. Our global annotator community ensured precise labeling with domain-specific accuracy.

Outcome & Features:

Delivered 23,000 fully annotated images across both datasets within 10 weeks.

Ensured annotation accuracy through stringent quality checks.

Helped AgroVision Analytics enhance AI model performance, enabling real-time livestock monitoring and fruit yield analysis.