TY - GEN
T1 - A Survey on Crop Disease Prediction Detection
T2 - 7th International Conference on Blockchain, Artificial Intelligence, and Trustworthy Systems, BlockSys 2025
AU - Qiu, Hao
AU - Wang, Xianping
AU - Shen, Jiayue
AU - Yang, Shunkun
AU - Zhao, Wenbing
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - Early detection of crop diseases is critical for safeguarding food security and improving agricultural productivity. In recent years, the integration of advanced imaging modalities—multispectral and hyperspectral sensors—with artificial intelligence (AI) has enabled unprecedented precision in early disease detection. This paper surveys state-of-the-art research published from 2020 to 2025 on crop disease prediction and detection using AI models trained on multispectral and hyperspectral images. We discuss data acquisition platforms (e.g., UAVs, satellites, ground-based systems), outline major AI architectures (including convolutional neural networks, capsule networks, and physics-informed generative adversarial networks), and highlight both promising results and remaining challenges. Future research directions to enhance early detection and management of crop diseases are proposed.
AB - Early detection of crop diseases is critical for safeguarding food security and improving agricultural productivity. In recent years, the integration of advanced imaging modalities—multispectral and hyperspectral sensors—with artificial intelligence (AI) has enabled unprecedented precision in early disease detection. This paper surveys state-of-the-art research published from 2020 to 2025 on crop disease prediction and detection using AI models trained on multispectral and hyperspectral images. We discuss data acquisition platforms (e.g., UAVs, satellites, ground-based systems), outline major AI architectures (including convolutional neural networks, capsule networks, and physics-informed generative adversarial networks), and highlight both promising results and remaining challenges. Future research directions to enhance early detection and management of crop diseases are proposed.
KW - Crop disease detection
KW - UAV
KW - deep learning
KW - hyperspectral imaging
KW - multispectral imaging
KW - remote sensing
UR - https://www.scopus.com/pages/publications/105028245641
U2 - 10.1007/978-981-95-3483-8_14
DO - 10.1007/978-981-95-3483-8_14
M3 - 会议稿件
AN - SCOPUS:105028245641
SN - 9789819534821
T3 - Communications in Computer and Information Science
SP - 182
EP - 195
BT - Blockchain and Trustworthy Systems - 7th International Conference on Blockchain, Artificial Intelligence, and Trustworthy Systems, BlockSys 2025, Revised Selected Papers
A2 - Chen, Jianguo
A2 - Luo, Xiaonan
A2 - Yu, Yuanlong
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 30 May 2025 through 31 May 2025
ER -