Editorial
Open Access
Inaugural Editorial for the Digital Intelligence in Agriculture
1
Wenzhou Vocational College of Science and Technology, Wenzhou 325006, China
2
Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing 100125, China
3
Canadian Academy of Engineering, Ottawa K2P 2P7, Canada
* Corresponding Author:
Jianbo Shen,
[email protected]
Volume 1, Issue 1
2025
Received: 07 July 2025
Accepted: 25 August 2025
Published: 26 August 2025
Article Information
Published in
Digital Intelligence in Agriculture
Volume/Issue
Volume 1, Issue 1, 2025
Pages
1-5
Cited by
2 (Crossref)
2 (Scopus)
Abstract
This editorial highlights the importance of establishing the journal Digital Intelligence in Agriculture and the typical applications of digital intelligence technology in agriculture such as planting industry, forestry, animal husbandry, and fishery. Digital intelligence technology is driving global agriculture towards a new stage of smart agriculture characterized by "data-driven and intelligent decision-making". Its core is to achieve comprehensive empowerment of agricultural production, operation, management, and services through technologies such as the Internet of Things, big data, artificial intelligence, cloud computing, and blockchain. Smart technology aims to achieve multiple goals in agriculture, including cost reduction and efficiency improvement, quality improvement and income increase, resource conservation, and environmental sustainability. It is a key path to address future food security challenges and achieve agricultural modernization.
Keywords
big data
AI
IoT
blockchain
robotic automation
agriculture
Data Availability Statement
Not applicable.
Funding
This work was supported by the National Key Research and Development Program of China under Grant 2023YFD1501302.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
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Cited By (3)
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Yaojun Zhang, Guiling Wu, Jianbo Shen, Chong Xu. Precise tea leaf disease detection using UAV low-altitude remote sensing and optimized YOLO11 model.
PLOS One, 2026 , 21 (2).
[CrossRef] -
Mengli Zhou, Jianbo Shen, Peilin Pang, Fang Guo, Dongfeng Yan. Structural Characteristics Analysis of Pinus taiwanensis Plantation in Climate Transition Zone.
Plants, 2026 , 15 (12).
[CrossRef] -
Yuansheng Wang, Huarui Wu, Cheng Chen, Gongming Wang. A Review and Design of Semantic-Level Feature Spatial Representation and Resource Spatiotemporal Mapping for Socialized Service Resources in Rural Characteristic Industries.
Sustainability, 2025 , 17 (19).
[CrossRef]
* Citation data provided by Crossref Cited-by.
Cite This Article
APA Style
Shen, J., Wang, Y., & Ling, X. (2025). Inaugural Editorial for the Digital Intelligence in Agriculture. Digital Intelligence in Agriculture, 1(1), 1–5. https://doi.org/10.62762/DIA.2025.106155
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TY - JOUR AU - Shen, Jianbo AU - Wang, Yingkuan AU - Ling, Xiaofeng PY - 2025 DA - 2025/08/26 TI - Inaugural Editorial for the Digital Intelligence in Agriculture JO - Digital Intelligence in Agriculture T2 - Digital Intelligence in Agriculture JF - Digital Intelligence in Agriculture VL - 1 IS - 1 SP - 1 EP - 5 DO - 10.62762/DIA.2025.106155 UR - https://www.icck.org/article/abs/DIA.2025.106155 KW - big data KW - AI KW - IoT KW - blockchain KW - robotic automation KW - agriculture AB - This editorial highlights the importance of establishing the journal Digital Intelligence in Agriculture and the typical applications of digital intelligence technology in agriculture such as planting industry, forestry, animal husbandry, and fishery. Digital intelligence technology is driving global agriculture towards a new stage of smart agriculture characterized by "data-driven and intelligent decision-making". Its core is to achieve comprehensive empowerment of agricultural production, operation, management, and services through technologies such as the Internet of Things, big data, artificial intelligence, cloud computing, and blockchain. Smart technology aims to achieve multiple goals in agriculture, including cost reduction and efficiency improvement, quality improvement and income increase, resource conservation, and environmental sustainability. It is a key path to address future food security challenges and achieve agricultural modernization. SN - 3069-3187 PB - Institute of Central Computation and Knowledge LA - English ER -
BibTeX Format
Compatible with LaTeX, BibTeX, and other reference managers
@article{Shen2025Inaugural,
author = {Jianbo Shen and Yingkuan Wang and Xiaofeng Ling},
title = {Inaugural Editorial for the Digital Intelligence in Agriculture},
journal = {Digital Intelligence in Agriculture},
year = {2025},
volume = {1},
number = {1},
pages = {1-5},
doi = {10.62762/DIA.2025.106155},
url = {https://www.icck.org/article/abs/DIA.2025.106155},
abstract = {This editorial highlights the importance of establishing the journal Digital Intelligence in Agriculture and the typical applications of digital intelligence technology in agriculture such as planting industry, forestry, animal husbandry, and fishery. Digital intelligence technology is driving global agriculture towards a new stage of smart agriculture characterized by "data-driven and intelligent decision-making". Its core is to achieve comprehensive empowerment of agricultural production, operation, management, and services through technologies such as the Internet of Things, big data, artificial intelligence, cloud computing, and blockchain. Smart technology aims to achieve multiple goals in agriculture, including cost reduction and efficiency improvement, quality improvement and income increase, resource conservation, and environmental sustainability. It is a key path to address future food security challenges and achieve agricultural modernization.},
keywords = {big data, AI, IoT, blockchain, robotic automation, agriculture},
issn = {3069-3187},
publisher = {Institute of Central Computation and Knowledge}
}
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