Short communication: Insect detection using a machine learning model




Abstract. Homchan S, Gupta YM. 2021. Short communication: Insect detection using a machine learning model. Nusantara Bioscience 13: 69-73. The key step in characterizing any organisms and their gender highly relies on correct identification of specimens. Here we aim to classify insect and their sex by supervised machine learning (ML) model. In the present preliminary study, we used a newly developed graphical user interface (GUI) based platform to create a machine learning model for classifying two economically important cricket species. This study aims to develop ML model for Acheta domesticus and Gryllus bimaculatus species classification and sexing. An experimental investigation was conducted to use Google teachable machine GTM for preliminary cricket species detection and sexing using pre-processed 2646 still images. An alternative method for image processing is used to extract still images from high-resolution video for optimum accuracy. Out of the 2646 images, 2247 were used for training ML model and 399 were used for testing the trained model. The prediction accuracy of trained model had 100 % accuracy to identify both species and their sex. The developed trained model can be integrated into the mobile application for cricket species classification and sexing. The present study may guide professionals in the field of life science to develop ML models based on image classification, and serve as an example for researchers and taxonomists to employ machine learning for species classification and sexing in the preliminary analysis. Apart from our main goals, the paper also intends to provide the possibility of ML models in biological studies and to conduct the preliminary assessment of biodiversity.


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