An Ultra-Specific Image Dataset for Automated Insect Identification

TL;DR

This paper introduces a highly diverse, imbalanced image dataset of tiger beetles for automated insect identification, providing a benchmark for evaluating various classification algorithms, especially transfer learning models.

Contribution

It presents a novel, diverse insect image dataset with challenging variations, and evaluates multiple classification approaches including transfer learning, highlighting their performance on this complex dataset.

Findings

Transfer learning models with softmax classifier performed best.

The dataset's diversity challenges existing classification algorithms.

The dataset enables development of new feature extraction methods.

Abstract

Automated identification of insects is a tough task where many challenges like data limitation, imbalanced data count, and background noise needs to be overcome for better performance. This paper describes such an image dataset which consists of a limited, imbalanced number of images regarding six genera of subfamily Cicindelinae (tiger beetles) of order Coleoptera. The diversity of image collection is at a high level as the images were taken from different sources, angles and on different scales. Thus, the salient regions of the images have a large variation. Therefore, one of the main intentions in this process was to get an idea about the image dataset while comparing different unique patterns and features in images. The dataset was evaluated on different classification algorithms including deep learning models based on different approaches to provide a benchmark. The dynamic nature of the dataset poses a challenge to the image classification algorithms. However transfer learning models using softmax classifier performed well on current dataset. The tiger beetle classification can be challenging even to a trained human eye, therefore, this dataset opens a new avenue for the classification algorithms to develop, to identify features which human eyes have not identified.