Leveraging Smart-Phone Cameras and Image Processing Techniques to Classify Mosquito Species
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Leveraging Smart-Phone Cameras and Image Processing Techniques to Classify Mosquito Species Mona Minakshi Pratool Bharti Sriram Chellappan University of South Florida University of South Florida University of South Florida Tampa, Florida, USA. Tampa, Florida, USA. Tampa, Florida, USA. [email protected] [email protected] [email protected] ABSTRACT concerns across the globe today. To mitigate the spread of mosquito- Mosquito borne diseases continue to pose grave dangers to global borne diseases, it is vital to combat the spread of mosquitoes. Of health. An important step in combating their spread in any area critical importance in this mission is the identification of species is to identify the type of species prevalent there. To do so, trained prevalent in an area of interest. This is important, because there personnel lay local mosquito traps, and after collecting trapped are close to 3; 500 different species of mosquitoes present in the specimens, they visually inspect each specimen to identify the world today [1], and with increasing globalization and warming, species type and log their counts. This process takes hours and is they are spreading to newer locations, with most of them acting as cognitively very demanding. In this paper, we design a smart-phone vectors for several diseases. In any given location, multiple species based system that allows anyone to take images of a still mosquito are usually found at the same time. However, the process of species that is either alive or dead (but still retaining its physical form) identification is not at all easy. We present details below. and automatically classifies the species type. Our system integrates image processing, feature selection, unsupervised clustering, and 1.1 Current Trends in Species Identification an SVM based machine learning algorithm for classification. Results As of today, to derive populations of mosquitoes in any area, trained with a total of 101 mosquito specimens spread across nine different professionals lay traps, and pick them soon after to sort trapped vector carrying species (that were captured from a real outdoor trap specimens. Sometimes, hundreds of mosquitoes can be trapped in in Tampa, Florida) demonstrate high accuracy in species identifica- a single day. Then, to identify each specimen trapped, it is placed tion. When implemented as a smart-phone application, the latency under a microscope, and visually identified, which takes hours each and energy consumption were minimal. With our system, the cur- day for all specimens. Depending on location and time of year, this rently manual process of species identification and recording can process can repeat multiple times in a single week, and is cognitively be sped up, while also minimizing the ensuing cognitive workload demanding. We also point out that such kinds of mosquito control of personnel. Secondly, ordinary citizens can use our system in facilities are expensive to manage, and they are very few even in their own homes for self-awareness and information sharing with advanced countries. In low economy countries, where mosquitoes public health agencies. pose a greater danger, such facilities are even more scarce. With rising temperatures and population migrations, mosquitoes are CCS CONCEPTS believed to be invading newer areas across the world, and detecting • Computing methodologies ! Machine learning; Computer them early is a huge challenge today. vision; • Human-centered computing ! Smartphones; • Applied computing ! Health care information systems; 1.2 The Lack of a Citizen-Science Approach KEYWORDS Experts at mosquito control facilities acknowledge that, depending on location and time of the year, they can receive hundreds of Machine Learning, Image Processing, Public Health, Mosquitoes, calls each day from concerned citizens about mosquitoes in their Smart-phones, Computer Human Interaction neighborhoods. Due to limited resources, knowledge of mosquito species types can play a vital role in prioritizing schedules for 1 INTRODUCTION trap placement and spraying repellents during peak times, since Mosquito borne diseases (e.g., Malaria, Dengue, West Nile Fever, different mosquito species are vectors for different diseases. Sadly, and most recently Zika Fever) are amongst the biggest health care despite citizens willing to assist in this process, there is no way to enable that now. One practice recommended by experts is to ask citizens to collect a few mosquitoes (after spraying on them), and Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed store them in a transparent bag for the experts to identify them for profit or commercial advantage and that copies bear this notice and the full citation later. But this process is cumbersome, and the need for technology on the first page. Copyrights for components of this work owned by others than ACM based solutions to empower citizens in this effort became clear. must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. MobiQuitous 2018, November 5-7, 2018, New York City, United States 1.3 Our Contributions © 2018 Association for Computing Machinery. Based on the facts mentioned above, and coupled with the increas- ACM ISBN 978-x-xxxx-xxxx-x/YY/MM...$15.00 ing global spread of mosquito-borne diseases, public health experts we spoke to during this study were highly receptive to any technol- 2 RELATED WORK ogy based solution for mosquito species identification and recording We briefly present important related work on technology based that is accurate, comfortable and fast, so that a) human resources solutions (image and others) to identify mosquitoes, while also in public health can utilized more effectively, and b) citizens can clarifying the significance of our system proposed in this paper. be better informed and hence better served. Towards this extent, a). Image based Techniques using Digital Cameras: In [10], in this paper, we design a smart-phone based system that enables a solution is proposed to detect Aedes aeдypti species using images anyone to take images of a still mosquito that is alive or dead (after taken from a 500x optical zoom camera, and a support vector ma- possibly spraying or trapping), but still retaining its physical form, chine classification algorithm. Using a sample of 40 images, seven and then processes the captured images for species identification. textural features, and a support vector machine classification al- Our specific contributions are listed below. gorithm, an accuracy of 92:5% was demonstrated in classifying a). A Database of 303 mosquito images spread across nine Aedes aeдypti species from others. This solution though is expen- different species: In Fall 2016 and Spring 2017, we visited the sive, and addresses a binary classification problem only. mosquito control board in Hillsborough County, Florida to collect Work in [14] and [13] discuss machine learning techniques to specimens of female vector carrying mosquitoes that were captured classify mosquitoes from insects like flies and bees using images in outdoor traps. When a trap is set, specimens of dead mosquitoes taken from digital cameras. The problem addressed in these papers are collected the next day to prevent their decaying (that will com- is too generic though. In a recent paper [32], the authors address plicate visual identification). Then, the personnel there helped us a problem similar to ours, but sufficiently different. Specifically, visually identify the species type of 101 mosquito specimens that 12 adult mosquito specimens from 3 genera (Aedes, Anopheles and were evenly distributed across nine different species, details of Culex) were collected, and the right wing of each specimen was which are presented in Table 1. We immediately took one image of photographed using a sophisticated digital camera coupled with a each specimen using a Samsung Galaxy S5 Smart-phone in three microscope. Then, using coordinates at intersections of wing veins visually distinct backgrounds (with a different camera orientation as a feature, followed by a Neighbor Joining Tree classification for each background). As such, we obtained a total of 303 mosquito method, the accuracy in genus identification (among three) was image samples for model development. To the best of our knowl- 90%. This technique again is expensive and requires expertise. edge, this is the first instance of a dataset containing tagged images b). Using Techniques other than Imaging: In [8], the authors of mosquito species taken via a smart-phone camera (which we will attempt to use optical (rather than acoustic) sensors to record publicly release soon, and is also available upon request). In Figure the “sound" of insect flight from a small distance, and then de- 1, we present one representative smart-phone image for each of the sign a Bayesian classifier to identify four species of mosquitoes nine species we attempt to classify in this paper. (Aedes aeдypti, Culex quinquefasciatus, Culex stiдmatosoma, b). Feature Extraction, Dimensionality Reduction, Clus- and Culex tarsalis), and achieve an accuracy of 96%. Similarly, tering and Classification: Our proposed technique for species the work in [23] also leverages smart-phone microphones to cap- identification incurs multiple steps. First, we reduce the image size ture and process acoustics of mosquito flight, along with loca- from around 2988 × 5322 pixels per image to 256 × 256 pixels for tion and time of observation. The claim is that these features are faster processing, followed by employing median filters to remove unique to classify mosquito species. More innovative techniques noise. Second, we employ contour segmentation and Gaussian mix- like hydrogel-based low-cost microfluidic chips, baited with odor- tures to carefully extract only the mosquito portion from each ants to capture saliva droplets of mosquitoes are being designed by image and segmenting out the background. Third, we then con- Dr.