Identifying Mosquito Species Using Smartphone Cameras

Identifying Mosquito Species Using Smartphone Cameras

The goal of this project is to enable the non-expert person by leveraging their smartphone to detect harmful vs non-harmful mosquitoes.

Artificial Intelligence

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Description

According to WHO(World Health Organization) reports, among all disease transmitting insects mosquito is the most hazardous insect. In 2015 alone, 214 million cases of malaria were registered worldwide. Zika virus is another deadly disease transmitted from mosquitoes. According to CDC report, in 2016 62,500 suspected case of Zika were reported to the Puerto Rico Department of Health (PRDH) out of which 29,345 cases were found positive. There are 3500 different species of mosquitoes present in the world out of which 175 types is found in United States. But only few of them are responsible for these above mentioned fatal disease. Therefore classification between hazardous and regular mosquitoes are very important. For regular person with no expertise in this field would be almost impossible to identify the difference. Even for the mosquito expert, identifying different species is a very tedious and time consuming job. Hence in this paper, we have tried to classify 7 different species of dead mosquitoes with total 60 samples collected from Hillsborough County Mosquito and Aquatic Weed Control Unit,Tampa Florida by capturing image from smart phone cameras. With our approach we want to enable nonexpert population to early identify the risk and act pro-actively. We pre-processed the image for removing noise and applied random forest classification algorithm to distinguish different species. Achieved good precision,recall,F1 measure and aggregate 83:3% accuracy. We are also planning to develop a smart-phone application which will leverage this learning model and help in empowering population to identify mosquito species without any knowledge in this field.

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Edwin M. created project Farm Assist

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Farm Assist

With everything going digital, there’s need for a digital platform that allows farmers to automate their farm activities such that they have visibility of the farm progress anytime anywhere on single click. Coming up with a digital online platform, Farm Assist (FA), an internet application software, will help farmers to solve these problems in a more efficient and less costly way leading to increase in production.

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Tushaar G. updated status

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Tushaar Gangarapu

Hello everyone, I am Tushaar. I am currently pursuing my bachelors at NITK. I developed passion towards AI and Machine Learning, had mentored the same at a mentorship program and currently was working on "Real Time Sensor based Weather Analysis and Prediction using Cloud Analytics to Improve Agricultural Yield". At the moment all we have are basic regression models which actually work with an 80 percent efficiency approximately. The repositories related to both the mentorship program and the regression models can be found on my GitHub account.

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Rafael S. created project Data Stream Mining

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Data Stream Mining

If the application data is very large and the recent data is the most important, by using data stream mining techniques we can extract knowledge to facilitate and simplify decision making. This project intends to study data streaming mining in internet networks, IoT sensors and social media. The machine learning algorithms need modifications to be adaptive and incremental. Modern code is also required to optimize computational resources.

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Alison Moura

Av. Costa e Silva, s/n - Cidade Universitária, Campo Grande - MS, 79070-900, Brazil

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Nitin Mane

Aurangabad, Maharashtra, India

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Rafael Santos

R. da Consolação, 930 - Consolação, São Paulo - SP, Brasil

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Gordon Hendry

Research Triangle Park, Durham, NC, USA

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