## 1 Introduction  
Mosquitoes are the most important disease vector responsible for causing major deaths among children and adult population. A study conducted in 2015 revealed that more than 430,000 people die of malaria annually [1]. The expert in tropical medicine affirms that blood-sucking mosquitoes comprise _Aedes_, _Anopheles_, and _Culex_ species. The activities of mosquitoes differ in terms of their time zones, behavior habits, and mediated infections. The classification of species is important because prevention and extermination of infectious diseases are different for each species.  
The identification of the species of mosquitoes is difficult for a layman. However, if vision-based classification of mosquito species from pictures captured by a smartphone or mobile device is available, the classification results can be utilized to educate and enlighten the population with inadequate knowledge of mosquitoes about their mediated infectious diseases, especially in Africa, Southeast Asia, and Central and South America, where several infectious diseases are epidemic.  
So far, some vision-based studies have been conducted to identify the bug species. [PERSON] et al. [2] presented the design and experimental validation of an automated vision-based mosquito classification module. The module can identify mosquitoes from other bugs, such as bees and flies, by extracting the morphological features, followed by a support vector machine (SVM)-based classification. Using multiple classification strategies, experimental results that involve the classification between mosquitoes and a predefined set of other bugs demonstrated the efficacy and validity of the proposed approach,with a maximum recall of 98%. However, they did not involve the classification of mosquito species. By utilizing the random forest algorithm, [PERSON] et al. [3] identified 7 species and 60 mosquitoes based on classifiers and denoting pixel values, with an accuracy of 83.3%. Moreover, the verification was insufficient because the number of images was approximately 60.  
In 2019, although a convolutional neural network (CNN)-based deep learning method is applied to study numerous vision-based classifications, CNN-based methods are yet to be applied to classify mosquito species. For fine-grained image recognition with a CNN-based deep learning framework, various datasets, such as flowers [4], birds [5], fruits and vegetables [6], animals, and plants [7], are proposed. The dataset of animals and plants [7] contains mosquito images. Because this dataset does not specifically focus on mosquitoes, only a small number of mosquito images are available. Alternatively, a dataset can be constructed to identify the mosquito species.  
In this study, we considered the mosquito species classification method. This study aimed to compare the handcraft feature-based conventional classification method and CNN-based deep learning method. By comparing the accuracy of the two aforementioned methods, we observed an effective method for identifying mosquito species, thereby improving the recognition accuracy.  
The rest of this study is organized as follows. Section 2 constructs the mosquito dataset and Section 3 explains the conventional and deep learning classification methods. The experimental verification and additional experimental validation are presented in Sections 4 and 5, respectively. Section 6 summarizes the main contributions of our study.