Crop Pest Classification Using Deep Learning Techniques: A Review

TL;DR

This review surveys recent deep learning methods for crop pest classification, highlighting advancements, datasets, challenges, and future research directions in AI-based pest monitoring.

Contribution

It provides a comprehensive overview of 37 studies from 2018 to 2025, emphasizing the shift from CNNs to hybrid and transformer models for pest detection.

Findings

Transformer-based models outperform CNNs in accuracy.

Key challenges include dataset imbalance and small pest detection.

Hybrid models show promising results for real-world deployment.

Abstract

Insect pests continue to bring a serious threat to crop yields around the world, and traditional methods for monitoring them are often slow, manual, and difficult to scale. In recent years, deep learning has emerged as a powerful solution, with techniques like convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid models gaining popularity for automating pest detection. This review looks at 37 carefully selected studies published between 2018 and 2025, all focused on AI-based pest classification. The selected research is organized by crop type, pest species, model architecture, dataset usage, and key technical challenges. The early studies relied heavily on CNNs but latest work is shifting toward hybrid and transformer-based models that deliver higher accuracy and better contextual understanding. Still, challenges like imbalanced datasets, difficulty in detecting small pests, limited generalizability, and deployment on edge devices remain significant hurdles. Overall, this review offers a structured overview of the field, highlights useful datasets, and outlines the key challenges and future directions for AI-based pest monitoring systems.