How a Leading Agri-Tech Company Cut Labeling Costs in Half Using Model-Assisted Workflows

The Challenge

Manually segmenting full images to distinguish crops from weeds was time-consuming and expensive. With thousands of high-resolution field images, the cost of annotation was becoming a major barrier to scaling AI model training.

The Approach

The company adopted Databrewery’s model-assisted labeling capabilities paired with Brewforce’s collaborative annotation workflows. By combining AI-assisted pre-labeling with human review, they accelerated data creation without compromising accuracy.

The Outcome

The entire labeling process is now centralized, streamlined, and far more efficient. By automating repetitive tasks and improving collaboration, the company reduced annotation time by 50%. This has translated into millions in cost savings while still delivering the high-quality data their models demand.

A leading agri-tech company is building advanced computer vision systems to power the next generation of sustainable farming machinery. At the heart of this innovation is a challenge every farmer knows too well, managing weeds without damaging crops or burning through budgets.

Traditional methods present an impossible choice: automated herbicide spraying that risks harming valuable crops, or manual spraying that’s slow, costly, and labor-heavy. This company’s breakthrough "See & Spray" technology changes the equation by using machine learning and robotics to detect and selectively spray only the weeds, preserving crops while cutting down chemical use.

That’s where Databrewery’s model-assisted labeling and Brewforce’s collaborative tools made the difference. Instead of labeling from scratch, annotators could now simply review and correct pre-labeled outputs generated by AI models. This shift brought two major advantages:

Speed: Labeling time was cut by more than 50%
Focus: Teams could spend time on refinement and accuracy instead of tedious manual work.

With initial masks already in place, human annotators could quickly catch the common errors like unclear edges or complex clusters where crops and weeds overlapped. This led to faster turnaround on even the most complex image segmentation tasks.

By improving the quality and speed of training data creation, the company saw a direct impact on model performance in the field. And the ultimate goal? To reduce herbicide use by up to 80% delivering cost savings and environmental benefits at once. The journey doesn’t stop there. With active learning and deeper automation features from Databrewery, further productivity gains are already on the horizon.