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This research article presents a machine learning approach to automate the analysis of electrical penetration graph (EPG) data, which is crucial for understanding aphid feeding behavior and insect-plant interactions. The study evaluates six machine learning models, finding that a Residual Network (ResNet) achieved the highest classification accuracy of 96.8%, significantly improving the efficiency of EPG data analysis. The proposed automated pipeline aims to streamline the annotation process, making it acce

Author

turpin

Language

EN