用深度学习自动识别农作物害虫，提升农业监测效率
Crop Pest Classification Using Deep Learning Techniques: A Review
- 整合37项研究，按作物、害虫、模型分类梳理
- 基于视觉变换器的混合模型准确率更高，更懂上下文
- 适合农业AI研发者参考，尤其关注小害虫检测
昆虫害虫持续威胁全球作物产量，传统监测方法往往耗时、人工且难扩展。近年来，深度学习成为有力解决方案，卷积神经网络（CNN）、视觉变换器（ViTs）及混合模型在自动化害虫检测中日益流行。本文综述2018至2025年间37篇精选的人工智能害虫分类研究，按作物类型、害虫种类、模型架构、数据集使用及关键技术挑战进行组织。早期研究多依赖CNN，最新进展则转向混合与基于变换器的模型，显著提升准确率并增强上下文理解能力。然而，数据不平衡、小型害虫难检测、泛化能力弱以及边缘设备部署等问题仍是主要挑战。该综述系统梳理了领域进展，指出关键数据集，并明确未来发展方向。
原文摘要 · Abstract (English)
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.
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