• “The Puerto Rico Vector Control Unit (PRVCU) was established to leverage Puerto Rico’s capacity to control the Aedes aegypti mosquito, the vector for the diseases Zika, Chikungunya, and Dengue in Puerto Rico.”

    Science and Technology Research │ Artificial Intelligence

Modernizing Solutions Together

The world’s deadliest animal is not a shark or a snake, it is the mosquito. The tiny creatures have killed 32 times as many people as every war in human history combined. The Puerto Rico Science, Technology & Research Trust and its Vector Control Unit (PRVCU) has been working to help prevent and manage diseases spread by mosquitoes, to gain an understanding of why many mosquitoes have become immune to insecticides approved by the FDA.

Gaining Greater Visibility and Leveraging the Power of Data

Researchers have spread out across the island, capturing different mosquito species in traps; monitoring and testing them for viral presence and insecticide resistance; and labelling and classifying them. Manually capturing and classifying thousands of mosquitoes in different locales can take many months before any specific patterns can emerge.

Wovenware partnered with the PRVCU to develop an RPA solution composed of deep learning models and other processes to automate the identification and classification of mosquito species. It significantly reduced manual work, allowing entomologists to spend more time in more advanced research activities.

Our Challenges
and Learnings

• Classifying very small objects in an image.

• Correctly classifying a large number of mosquitoes that are mutilated by the weather or traps.

• Experimenting with new architectures that could change the technical approach to the problem.

• Designing a human + machine RPA-based workflow to integrate the model with day-to-day research operations conducted by scientists.

The Wovenware Approach

Wovenware proposed an RPA solution to automate counting the total number of mosquitoes in traps, classifying the species and the gender.

Technologies we take advantage of

  • RESNET

  • Tensor Flow

  • RETINANET

Building it Right

In order to create the deep learning solution capable of counting and classifying the mosquitoes, the Wovenware team had to:

  • Built a Convolutional Neural Network (CNN) to count total number of mosquitoes.
  • Built two CNNs to identify mosquito species and gender.
  • Trained the models with a dataset of 5,000 images.
  • Wovenware trained models on our state-of-the-art local 8 GPU cluster server optimized for deep learning training.

❝The deep learning solution was a success, capable of detecting and classifying mosquitoes four times faster than with the previous manual methods.❞

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