Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions.
The electrical penetration graph (EPG) is a well-known technique that provides insights into the feeding behavior of insects with piercing-sucking mouthparts, mostly hemipterans. Since its inception in the 1960s, EPG has become indispensable in studying plant-insect interactions, revealing critical...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0319484 |
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| _version_ | 1850216092980477952 |
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| author | Quang Dung Dinh Daniel Kunk Truong Son Hy Vamsi Nalam Phuong D Dao |
| author_facet | Quang Dung Dinh Daniel Kunk Truong Son Hy Vamsi Nalam Phuong D Dao |
| author_sort | Quang Dung Dinh |
| collection | DOAJ |
| description | The electrical penetration graph (EPG) is a well-known technique that provides insights into the feeding behavior of insects with piercing-sucking mouthparts, mostly hemipterans. Since its inception in the 1960s, EPG has become indispensable in studying plant-insect interactions, revealing critical information about host plant selection, plant resistance, virus transmission, and responses to environmental factors. By integrating the plant and insect into an electrical circuit, EPG allows researchers to identify specific feeding behaviors based on their distinctive waveform patterns. However, the traditional manual analysis of EPG waveform data is time-consuming and labor-intensive, limiting research throughput. This study presents a novel Machine Learning (ML) approach to automate the annotation of EPG signals. We rigorously evaluated six diverse ML models, including neural networks, tree-based models, and logistic regression, using an extensive dataset from multiple aphid feeding experiments. Our results demonstrate that a Residual Network (ResNet) architecture achieved the highest overall waveform classification accuracy of 96.8% and highest segmentation overlap rate of 84.4%, highlighting the potential of ML for accurate and efficient EPG analysis. This automated approach promises to accelerate research in this field significantly and broaden insights into insect-plant interactions, showcasing the power of computational techniques for insect biological research. The source code for all experiments conducted within this study is publicly available at https://github.com/HySonLab/ML4Insects. |
| format | Article |
| id | doaj-art-b9eaa18d627043f0b988b8ce25e71f40 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-b9eaa18d627043f0b988b8ce25e71f402025-08-20T02:08:24ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01204e031948410.1371/journal.pone.0319484Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions.Quang Dung DinhDaniel KunkTruong Son HyVamsi NalamPhuong D DaoThe electrical penetration graph (EPG) is a well-known technique that provides insights into the feeding behavior of insects with piercing-sucking mouthparts, mostly hemipterans. Since its inception in the 1960s, EPG has become indispensable in studying plant-insect interactions, revealing critical information about host plant selection, plant resistance, virus transmission, and responses to environmental factors. By integrating the plant and insect into an electrical circuit, EPG allows researchers to identify specific feeding behaviors based on their distinctive waveform patterns. However, the traditional manual analysis of EPG waveform data is time-consuming and labor-intensive, limiting research throughput. This study presents a novel Machine Learning (ML) approach to automate the annotation of EPG signals. We rigorously evaluated six diverse ML models, including neural networks, tree-based models, and logistic regression, using an extensive dataset from multiple aphid feeding experiments. Our results demonstrate that a Residual Network (ResNet) architecture achieved the highest overall waveform classification accuracy of 96.8% and highest segmentation overlap rate of 84.4%, highlighting the potential of ML for accurate and efficient EPG analysis. This automated approach promises to accelerate research in this field significantly and broaden insights into insect-plant interactions, showcasing the power of computational techniques for insect biological research. The source code for all experiments conducted within this study is publicly available at https://github.com/HySonLab/ML4Insects.https://doi.org/10.1371/journal.pone.0319484 |
| spellingShingle | Quang Dung Dinh Daniel Kunk Truong Son Hy Vamsi Nalam Phuong D Dao Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions. PLoS ONE |
| title | Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions. |
| title_full | Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions. |
| title_fullStr | Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions. |
| title_full_unstemmed | Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions. |
| title_short | Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions. |
| title_sort | machine learning for automated electrical penetration graph analysis of aphid feeding behavior accelerating research on insect plant interactions |
| url | https://doi.org/10.1371/journal.pone.0319484 |
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