Advances in Artificial Intelligence-Enabled Crop Pest and Disease Detection: A Systematic Review
- 1School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
- 2School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
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.
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BibTeX
@article{ma2026advances,
title = {{Advances in Artificial Intelligence-Enabled Crop Pest and Disease Detection: A Systematic Review}},
author = {Zhen Ma and Cundeng Wang and Xinzhong Wang and Xuegeng Chen},
journal = {Agriculture},
year = {2026},
month = jun,
doi = {10.3390/agriculture16121262},
url = {https://doi.org/10.3390/agriculture16121262},
note = {CC BY},
}
RIS
TY - JOUR
TI - Advances in Artificial Intelligence-Enabled Crop Pest and Disease Detection: A Systematic Review
AU - Zhen Ma
AU - Cundeng Wang
AU - Xinzhong Wang
AU - Xuegeng Chen
PY - 2026
DA - 2026/06/07
JO - Agriculture
DO - 10.3390/agriculture16121262
UR - https://doi.org/10.3390/agriculture16121262
AB - 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.
SN - 2077-0472
ER -