DiscoEPG: A Python package for characterization of insect electrical penetration graph (EPG) signals
Year: 2026
Type: software-paper
Abstract: • DiscoEPG is an open-source Python package to streamlines EPG research for plant-insect studies. • EPG waveform annotation is automated using ML algorithms, achieving high accuracy. • The package features tools for statistical analysis and visualization. • DiscoEPG was validated on 332 8-hour recordings from 4 aphid species. The Electrical Penetration Graph (EPG) technique is a widely recognized tool for monitoring and analyzing the feeding behavior of herbivorous insects with piercing-sucking mouthparts, such as aphids. Traditionally, EPG waveform annotation relies on a skilled practitioner using expert knowledge to compare target waveforms with the standard patterns of feeding behaviors. This process is labor-intensive, often requiring more than 30 minutes to annotate an 8-hour recording, depending on the complexity of the behaviors displayed. Machine learning (ML) has shown significant potential in automating behavioral analysis, including in EPG waveform annotation. However, most publicly available tools that provide automatic annotation suffer from low prediction accuracy due to their simple classification rules. To address this limitation, we developed DiscoEPG , an open-source Python package that offers highly accurate automatic annotation of aphid EPG data generated with EPG Systems hardware and the associated Stylet+ software. We rigorously evaluated multiple ML algorithms, demonstrating superior predictive accuracy compared to existing methods. In addition, DiscoEPG includes novel features to improve usability, such as tools to generate publication-quality visualizations, compute EPG variables, and perform statistical analysis. By streamlining the analysis of aphid EPG data, DiscoEPG aims to make this technique more accessible to researchers studying the feeding behavior of aphids. Our package source code and example interactive workbooks are publicly available at: https://github.com/HySonLab/ML4Insects .
Source: Smart Agricultural Technology
Language: English
Subfield: Insect Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $1,770
OpenAlex ID: W7128952376