CerealPestAID: Deep Learning Models for Cereal Pest Identification

Sanford Eigenbrode; Arash Rashed; Marek Borowiec; Subodh Adhikari; Lucas Sheneman; Jennifer Hinds; John Brunsfeld

doi:10.7923/ygt6-5815

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CerealPestAID: Deep Learning Models for Cereal Pest Identification

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CerealPestAID: Deep Learning Models for Cereal Pest Identification

Sanford Eigenbrode, Arash Rashed, Marek Borowiec, Subodh Adhikari, Lucas Sheneman, Jennifer Hinds and John Brunsfeld

Hugging Face

02/15/2026

DOI: https://doi.org/10.7923/ygt6-5815

Appears in Artificial Intelligence and Machine Learning Research

Abstract

Deep learning models for classifying 26 cereal crop pest species from images. Three architectures are provided: EfficientNet-B6, MobileNetV3-Large, and InceptionV3, each available in PyTorch, ONNX, and TFLite formats.

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Title: CerealPestAID
Creators: Sanford Eigenbrode - University of Idaho, Entomology, Plant Pathology, and Nematology
Arash Rashed - Virginia Tech
Marek Borowiec - Colorado State University
Subodh Adhikari - Utah State University
Lucas Sheneman - University of Idaho, Research Computing and Data Services
Jennifer Hinds - University of Idaho
John Brunsfeld - University of Idaho
Publisher: Hugging Face
Identifiers: 996895059101851
Academic Unit: Research Computing and Data Services; Entomology, Plant Pathology, and Nematology; Institute for Health in the Human Ecosystem; Center for Research on Invasive Species
Language: English
Resource Type: Dataset