Vol. 64, 2025

A Systematic Review of Species Classification Using Deep Learning Algorithms and Gender Identification of Tribolium castaneum Using Convolutional Neural Networks

Anurupa Mistry¹, Chetas Hedaoo², Archana Sharbidre^3,*, Jayashri Bagade^4,*, and Sangeeta V. Pandit⁵

¹Department of Zoology, Savitribai Phule Pune University. Pune, Maharashtra, India. E-mail: anurupamistry@gmail.com (Mistry)
²Department of Electronics and telecommunication, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India. E-mail: chetas.22111109@viit.ac.in (Hedaoo)
³Department of Zoology, Savitribai Phule Pune University. Pune, Maharashtra, India. *Correspondence: E-mail: aasharbidre@unipune.ac.in (Sharbidre)
⁴Department of Information Technology, Vishwakarma Institute of Technology, Pune, Maharashtra, India. *Correspondence: E-mail: jayashrihedaoo@rediffmail.com (Bagade)
⁵Department of Zoology, Savitribai Phule Pune University. Pune, Maharashtra, India. E-mail: drpanditsv@unipune.ac.in (Pandit)

(Received 1 September 2023 / Accepted 16 April 2025 / Published -- 2025)
Communicated by Sheng-Feng Shen

Machine learning (ML) constitutes a division of artificial intelligence (AI) that aims to train computers how to perform specific tasks without explicit programming. Traditional ML tools are widely used for classification and identification of animals. However, these methods have some drawbacks because of the extensive manual reliance and the delay in data interpretation. To overcome this, Applied Deep Learning algorithms are used with Artificial Neural Networks (ANN) and Convolution Neural Network (CNN) models introduced to address species classification, characteristics detection, and pattern recognition tasks helping in accurate identification and classification of animals. In this paper, we have tried to compile and deliver a recent comprehensive information on latest available investigations in the field of life sciences particularly used for animal identification. We have also accentuated the diverse applications of machine learning models including other parameters like, features, accuracy gained, database used and their limitations. The red flour beetle, Tribolium castaneum (Coleoptera; Tenebrionidae) is a prevailing and detrimental secondary insect pest of stored grains along with derived products causing 7% to 35% annual loss. Despite of that, nowdays it is also extensively considered as a model organism for genetic disease investigation. While using it in scientific research, exact sex identification of these insects becomes a crucial preliminary step. Generally, pupal stage is used to sort these insects according to their sex and needs expert humans. It is crucial to employ image processing and ML algorithms to quickly identify gender of this insect which is not done yet. We have used a CNN-based smart technique to recognize and categorize gender differences in T. castaneum using microscopic images in order to build an intelligent system for applied research. For this study, a dataset is created by taking 116 microscopic images of both the dorsal and ventral sides of pupae of two different sexes. In this algorithm, a 2D matrix of feature map is selected sequentially and the maximum value in the matrix is selected to generate a pooled feature map. The Rectified Linear Unit (ReLU) activation function is used for the CNN. The classification model has an accuracy between 97 and 98% with an F-score of 0.67. These results demonstrate the robustness of the classification model, which does not rely heavily on manual intervention compared to traditional machine learning (ML) tools and automates the processes of feature extraction and gender classification regardless of the position of the pupae in the images.

Keywords: Species Classification, Deep Learning, CNN, Tribolium, Castaneum, Gender Identification

Citation: Mistry A, Hedaoo C, Sharbidre A, Bagade J, Pandit SV. 2025. A systematic review of species classification using deep learning algorithms and gender identification of Tribolium castaneum using convolutional neural networks. Zool Stud 64:24.