## 1 Introduction
In this feature extraction method, body area ratio, eccentricity, upper body length ratio, width ratio, body shape parameter and color complexity parameters of the insects that attribute the uniqueness of the insects are taken into account. Numerous works have been identified for formulating effective methods for image retrieval and classification. A novel computer chip defect detection method was discussed in [16] that is more effective and efficient than typical template image comparison methods. In the above-mentioned work, the authors introduce a new methodology that uses phase only Fourier transform (POFT) for saliency detection followed by a local discrepancy evaluation between the test images and defect free images. The work mentioned in [17] presents a novel subspace learning framework called conjunctive patches subspace learning (CPSL) for effective semantic subspace learning. In this work, the authors also demonstrate the effectiveness of the proposed framework in improving the performance of content-based image retrieval (CBIR). Similarly, the research work put forward by [PERSON] et al. introduces two methods called biased maximum margin analysis (BMMA) and semi-supervised biased maximum margin analysis (SemiBMMA) that reduces the downsides of conventional SVM-based relevant feedback scheme in CBIR [18]. In addition to the image retrieval, significant research works have been identified in formulating classification methods and improving existing methodologies for classification. The work mentioned in [19] presented an effective method for complexity reduction and performance elevation of neural network-based classifications. A novel neuro-SVM-based classification strategy had been proposed for the classification of LIDAR backscatter intensity data in the work mentioned in [20]. From the authors' perspective, the proposed architecture shows a high prediction accuracy, hence it is most suitable for LIDAR data classification. The work of [21] discusses the usage of a semi-supervised neural network for classification of hyperspectral images. The authors are able to achieve better accuracy in hyperspectral image classification problems. Similarly, the design of efficient artificial neural networks in the context of multi-sensor remote-sensing image classification is mentioned in [22]. Even though both ANN- and SVM-based methodologies are used in classification and image retrieval scenarios, it is identified that the SVM-based classification could provide more accurate results [23; 24; 25]. This paper details the design and development of an automated mosquito classification system, concluding with experimental results using the prototype sensor module that validates the proposed approach in classifying mosquitoes from a set of other predefined bugs. The developed system can classify mosquitoes from a set of other predefined bugs such as bees and flies. The main challenges in designing an automated mosquito surveillance system includes the choice of extracted features, classification algorithm and the non-trivial process of implementing theoretical designs generated analytically into physical mechanisms. The proposed approach is environment-friendly, in terms of mosquito surveillance in open or closed drain perimeter, narrow channels and reservoirs, where the satellite mapping and manual examination methods are highly difficult. We analyze the morphological characteristics that distinguish the mosquitoes from other insects and provide color-based analysis for distinguishing the mosquito species. Our long-term objective is to deploy the developed mosquito classification system on a mobile robotic platform, capable of autonomously synthesizing mosquito-vector-density maps across larger areas. The rest of the paper is organized as follows: Section 2 presents the method used in this paper. Section 2.1 shows the system overview of the developed sensor module. Section 2.2 discusses the feature extraction method implemented. Section 2.3 introduces the SVM-based classification process used.