CerealPestAID: Deep Learning Models for Cereal Pest Identification

University of Idaho | Research Computing and Data Services 2026 Dataset Open access

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Authors: Luke Sheneman, Marek L. Borowiec, Sanford D. Eigenbrode, Arash Rashed, Subodh Adhikari, Jennifer J. Hinds, John Brunsfeld
DOI: 10.7923/ygt6-5815
Cited by: 0

Abstract

CerealPestAID 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. Model Description These models were trained to identify 26 species of cereal crop pests from field images. The goal is to support automated pest monitoring and integrated pest management (IPM) in cereal crop agriculture.

Fields of Study & Subjects

Smart Agriculture and AI Advanced Neural Network Applications Insect-Plant Interactions and Control

Publication details

Journal / source
University of Idaho | Research Computing and Data Services
Publication date
27 February 2026
Language
EN
Document type
Dataset