Advances in Artificial Intelligence-Enabled Crop Pest and Disease Detection: A Systematic Review

Abstract

The detection technology of crop diseases and pests is transitioning from single sensor monitoring to intelligent perception and multimodal fusion. This paper follows the PRISMA 2020 standard and systematically reviews the relevant core literature. This paper systematically summarizes the development history of spectral sensing technology and analyzes the physical mechanisms of hyperspectral and multispectral imaging in early identification of crop diseases. The focus is on the architectural evolution of deep learning models, including lightweight convolutional neural networks (CNNs), vision transformers (ViTs) with long-range dependency modeling capabilities, and the efficient computing state space model Mamba. In addition, the research progress of spatial spectral joint learning, heterogeneous data fusion, and vision-language models (VLMs) in improving system robustness and interpretability are introduced. By synthesizing the integrated applications of UAV remote sensing, Internet of Things (IoT) edge computing and intelligent robots in staple and cash crops, this paper summarizes the implementation of the integrated system of perception, decision-making and execution. To address the issues of insufficient cross-domain generalization ability and uneven allocation of computing resources in existing models, this paper provides perspectives on the future development of agricultural artificial intelligence (AI) towards foundation model-driven, edge-intelligent collaboration, and green sustainable direction, which can provide theoretical reference for engineering applications in the field of intelligent plant protection.

Plant phenotyping relevance

作物病害の画像・スペクトル観測による植物の病徴・病害状態推定を中心に、検出技術と計算手法を体系的にレビューしており、植物フェノタイピング手法のレビューに該当する。

Code and data availability

This is a systematic review of AI-enabled crop pest and disease detection. The supplied blocks contain no public phenotype/trait datasets, plant images, sensor data, author analysis code, or trained models specific to this paper; the review only describes search queries, screening criteria, and summarizes prior studies

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