Labeling crops and weeds to help agriculture development
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Agriculture has always been at the heart of civilization’s progress. Weeding, once a labor-intensive chore, evolved through the advent of chemical treatments and mechanized solutions. Today, the company we collaborate with takes this evolution further by integrating Artificial Intelligence.
Their ambition? To contribute to a world where millions gain access to healthier food and improved living conditions.
Context
CONTEXT
Our client, a large AgTech company, facilitates weeding and organic farming.
Example of cultures we had to label
Our client designed an automated weeding machine capable of detecting and weeding out any weed present on the land.
To build their machine, they developed their computer vision capabilities with precise data labeling.
To scale up their data labeling efforts, our client searched for a great data labeling partner and tested multiple annotation providers before choosing People for AI.
THEIR SEARCH
THEIR SEARCH
Our client needed more than just short-term support. With data labeling being a constant requirement in their line of work, they search for a partner who could grow with them.
As they diversified the crops they were studying, expertise, flexibility and scalability became essential—leading to the decision to work with a managed workforce.
Today, we collaborate on data from agricultural lands across the world, each region offering its own unique challenges.
The search of our client was precise:
Experienced data labeling team on agriculture
Social efforts (health insurance, retirement, training) made by their partner
Competitive salaries for all workers and permanent contract for labelers
Matthieu Warnier
Data Labeling Director @ People for AI
« Training skilled teams, setting up a high-performance data pipeline, providing accurate, up-to-date reporting in real time: these were just some of the challenges we met to meet our customer’s expectations. »
OUR IMPACT
OUR IMPACT
Hundreds of thousands of images are labeled each month, on diverse crops in different regions of the world.
A specific data pipeline was developed by People for AI and the client to reduce greatly the time between collection, labeling and model training.
Models are pre-labeling correctly 80% of the data before our labelers and reviewers correct the annotations.
Labeling crops and plants can bring significant challenges:
At an early stage of growth, crops and weeds can be very tricky to distinguish.
Weeding on ‘green-on-green’ lands is especially challenging, as weeds blend in with the crops.
Pre-labeled data makes the life of labelers easier, but they must keep their focus to find errors.
In choosing People for AI, the customer has surrounded itself with a reliable partner it has been able to rely on over the years – and will continue to rely on.
Our relationship has grown stronger as technology has advanced and the AI models developed by the customer have grown. Together, we have been able to grow our businesses, and this has been made possible by effective and continuous data annotation.
CTO of a confidential AgTech company
Client of People For AI since 2021
« Finding a partner aligned with our values, capable of providing genuine expertise while remaining competitive, proved more complex than we had anticipated.
After testing a number of data annotation service providers, only People for AI was able to meet our exacting specifications with rigor and efficiency. »
Choosing the right data annotation partner, especially when annotation is at the heart of your company’s development, is a major challenge.
Conducting trial projects or POCs with the most promising providers – as People for AI proposes – can be a key step in identifying a truly competent partner.
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