Image-based insect species and gender classification by trained supervised machine learning algorithms

• Published in: Ecological informatics 2020-11, Vol.60, p.101135, Article 101135
• Main Authors: Tuda, Midori, Luna-Maldonado, Alejandro Isabel
• Format: Article
• Language: English
• Subjects: AdaBoost; Artificial intelligence; Image-based machine learning; Insect sex classification; Object-based and pixel-based features; Random forest; Simple logistic regression
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2020.101135

Classification of specimens is the important first step to characterize populations and species assemblages. Although species-level classification has been a popular goal, the sex difference and sex ratio are also an important property in ecology and pest control. Here we focus on the images of mixed sex specimens of a stored product pest beetle (Callosobruchus chinensis) and its parasitoids (parasitic wasps; Anisopteromalus and Heterospilus) in various postures and classify them into species and sex, by training supervised machine learning programs: logistic model trees (LMT), random forest, support vector machine (SVM), simple logistic regression, multilayer perceptron and AdaBoost (adaptive boosting). Both object-based features and pixel-based features were extracted from each image. Simple logistic regression, LMT and AdaBoost (employing simple logistic regression as base learner) performed well to classify sexes or species/sexes; average true positive rates (prediction accuracy) of 88.5–98.5% were achieved for within-species sexing of beetles or wasps, 97.3% for two species sexing and 93.3% for three species sexing. For most datasets, the best performed models incorporated both object-based features and pixel-based features. LMT models were identical to simple logistic regression models in most cases. Robust performance and small variation in prediction accuracy of simple logistic regression, irrespective of classification target (sexes or species), was shown, and this is probably because of the efficient feature selection implemented in the algorithm. This study is one of the earliest to classify the gender of insects using machine learning based on still images. •Insect sex has rarely been subjected to image-based machine learning.•We compared machine learning algorithms to classify insect sexes and species.•Simple logistic performed best in sex (and species) classification.•Overall accuracy of 88.5–98.5% was achieved for sexing of beetles or wasps.•Both object-based and pixel-based features were selected from most datasets.