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AI-driven mosquito surveillance and control through on-demand manufacturing and release of sterile male mosquitoes

Periodic Reporting for period 1 - RoboSIT (AI-driven mosquito surveillance and control through on-demand manufacturing and release of sterile male mosquitoes)

Reporting period: 2020-12-01 to 2021-11-30

Vector-borne diseases, such as malaria, are infections transmitted by infected mosquitoes and other arthropod species. In Europe, vector-borne diseases are widespread. This makes mosquito surveillance important for controlling outbreaks. However, current approaches require the manual identification of hundreds of insects to send them for pathogen analysis. To improve efficiency, the EU-funded RoboSIT project has developed a robot for this task. Specifically, it will enable mass-rearing factories and sex-sorting using artificial intelligence. Pathogen detection will then trigger the release of millions of sterile males that will mate with the females, which will no longer produce offspring. The aim is to phase out the use of highly hazardous pesticides.

We expect to launch RoboSIT in the EU and US in 2023. We will expand to over 100 people in 5 years. Each new RoboSIT facility will generate 15 indirect jobs. Mosquito vector control is a stepping-stone for scaling into other vectors and pest. The potential of reducing pesticides in agriculture is huge; each year 2.5m tonnes of pesticides are used around the globe
Towards implementing a complete solution, the company worked in the past year on automating monitoring and surveillance of mosquitoes, along advancing the state-of-the-art sorting and packaging of mosquitoes and prototyping a novel drone release solution, first of its kind.

Specimen of mosquitoes from across the USA, Europe and Israel were sent to the company, realizing partnerships and relations the company established with leading mosquito control departments, for imaging the mosquitoes, creating a unique high resolution digital repository of mosquitoes from different countries. The company further progressed with its sex separator and packaging unit for its second generation, exploring capacity to process several millions of mosquitoes per week by a single unit, an unprecedented capability by any company, globally. Deep learning algorithms were used to train the machine to identify mosquito genus, species, and sex, supporting both the automated monitoring and sorting process. A cloud-based web-platform was developed, providing insights for the user on trap results allowing them to take sound decisions in their mosquito monitoring and control operations.
Mosquitoes infect millions of people annually with disease such as malaria, dengue, zika and chikungunya. Meticulous mosquito surveillance is critical for controlling outbreaks, but today’s approaches are highly ineffective. Experts need to manually identify and pool hundreds of insects and send them for pathogen analysis. We have developed an AI-driven robot to do this task. Vector control is commonly done using insecticides. 4,500 tonnes of DDT are still used every year to fight malaria due to the large unmet need in endemic areas. The insecticide-approach is unsustainable due to resistance, environmental contamination and severe impact on human health.
Our approach to vector control is to industrialize the most environment-friendly pest control method ever developed: the Sterile Insect Technique (SIT). SIT builds on the principle of releasing sterile insects in vast numbers, which mate with wild females, but do not give rise to offspring. Despite the great success that has been demonstrated using SIT in pilot studies, the technique is not scalable as sex-sorting of millions of insects is done manually. Consequently, the global impact of manual SIT is negligible. Our RoboSIT solution will encompass mass-rearing factories and sex-sorting using AI-driven robots. Pathogen detection will trigger the release of millions of sterile males in designated areas. Optimizing reaction times of outbreaks can save societies millions of € in healthcare cost. Bringing RoboSIT to market will incentivize the set-up of automated SIT facilities and reduce usage of insecticides.
Mosquito is standing stationary without ability to fly away, due to pressure difference on the net.
Senecio Drone system in mid-air during testing
Senecio novel automated monitoring, identification and pooling robot
A group of mosquitoes inside the Senecio machine with the neural network identifying a gender.