Labellerr Helps This Non-Profit Research Institute In Validating Quality of Labels

Labellerr Helps This Non-Profit Research Institute In Validating Quality of Labels
Labellerr Helps This Non-Profit Research Institute In Validating Quality of Labels

Our non profit research institute customer uses AI to its full potential to identify the weak places that underlie the world's most pressing issues and then devise creative fixes for them. They specialize in using technologies like computer vision, data science, cognitive reasoning, and IoT to address issues in infrastructure, agriculture, education, and public health.

They are a leading global AI hub that regularly collaborates with renowned academic institutions, international organizations, think tanks, and other private organizations that are eager to use AI as a force for good. To identify requirements and to execute and expand the solutions, we collaborate with governmental and non-governmental groups.

The Challenge

Our Client was aiming to build an AI-powered mobile application for farmers that quickly distinguish between various pest species. Farmers only need to take photos of the pest that has been caught in a net, and the application will advise them on the appropriate dosage of insecticide.

Based on the images provided, they can identify suitable solutions for the pests. But, it is difficult to manually distinguish between different types of pests. So, they first need to annotate/label all the images to identify the types of pest species. They need to annotate around 1000+ images for their project. So, for that reached out to us.

Pest Management Classification


How does Labellerr help them in image annotation?

Labellerr played a crucial role in helping customer in validating labels that they have collected from different vendors.

  • Our platform helps them save over 70% of the time in validating and comparing annotation quality of 3 different vendors
  • Quick review option for human annotation verification, ensuring data accuracy and consistency.
  • Improved inter-annotator agreement, consensus-based tagging, and accuracy among labelers.

Also, a variety of charts are available on Labellerr's platform for data analysis. The chart displays outliers if any labels are incorrect or to distinguish between advertisements.

We accurately extract the most relevant information possible from advertisements—more than 95% of the time—and present the data in an organized style. having the ability to validate organized data by looking over screens.

The client connected our tool to their cloud storage. Our client built a two-tier review procedure for outside annotation workers using our bespoke annotation pipeline, in addition to a self-review layer. 20+ team members, including labelers and reviewers, work cooperatively on the data.

Once completed, the client produced an export in the desired output format.


Labellerr's platform brought about significant improvements in client's pest management operations, particularly in data analysis, computer vision modeling, and overall performance. With Labellerr's advanced features, client experienced enhanced and expedited data analysis, enabling quicker decision-making and response times in handling pest-related data.

The accurate training dataset and specific tags provided by Labellerr ensured the utmost precision and reliability of customer's computer vision model. This instilled trust among end-users, crucial in the field of pest classification and management where accuracy is paramount.

Moreover, customer's analytical engine produced better and more precise results with the support of Labellerr's assistance, resulting in improved pest management capabilities and higher levels of customer satisfaction. Ultimately, Labellerr's contributions positively impacted customer's efficiency, accuracy, and overall performance in pest classification and identification.

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