Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.7
3.4
Journals
Agronomy
Special Issues
Advances in IoT Applications for Smart Agriculture and Precision Farming
share announcement

Advances in IoT Applications for Smart Agriculture and Precision Farming

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (31 July 2025) | Viewed by 967

Special Issue Editors

Dr. Dae-Hyun Jung

E-Mail Website
Guest Editor
Department of Smart Farm Science, Kyung Hee University, Giheung-gu, Yongin-si 17104, Republic of Korea
Interests: agricultural engineering; smart farming; AI in agriculture; IoT in agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Woo Jae Cho

E-Mail Website
Guest Editor
Division of Agro-System Engineering, College of Agriculture and Life Science, Gyeongsang National University, Jinju 52828, Republic of Korea
Interests: smart farming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advent of the Internet of Things (IoT) has brought about a paradigm shift in various sectors, and agriculture is no exception. The integration of IoT in agriculture, often referred to as smart farming or precision agriculture, has the potential to revolutionize the way we approach farming, leading to more efficient and sustainable agricultural practises.

This Special Issue delves into the topic of the Internet of Things (IoT) in agriculture, focusing on its rationale, current state, and evolution. The integration of the IoT in agriculture, or smart farming, is driven by the need to increase food production efficiency and sustainability. Current applications include sensor networks for real-time monitoring, automated systems, and data-driven decision-making. The evolution of the IoT in agriculture is an ongoing process, with emerging sophisticated solutions and convergence with technologies such as AI, blockchain, and 5G. Despite progress, challenges such as high costs, data privacy, lack of standardization, and the digital divide persist. This Special Issue invites contributions exploring innovative IoT technologies, applications, strategies, and future directions in smart farming that aim to advance sustainable farming systems for the future.

Dr. Dae-Hyun Jung
Dr. Woo Jae Cho
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Things (IoT) in agriculture
  • sensor networks in agriculture
  • smart farming
  • data-driven decision-making
  • AI-driven problem solving for agriculture systems
  • machine learning and deep learning
  • blockchain and 5G technology in smart farming

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 922 KB  
Article
Research on Agricultural Meteorological Disaster Event Extraction Method Based on Character–Word Fusion
by Minghui Qiu, Lihua Jiang, Nengfu Xie, Huanping Wu, Ying Chen and Yonglei Li
Agronomy 2025, 15(9), 2135; https://doi.org/10.3390/agronomy15092135 - 5 Sep 2025
Viewed by 527
Abstract
Meteorological disasters are significant factors that impact agricultural production. Given the vast volume of online agrometeorological disaster information, accurately and automatically extracting essential details—such as the time, location, and extent of damage—is crucial for understanding disaster mechanisms and evolution, as well as for [...] Read more.
Meteorological disasters are significant factors that impact agricultural production. Given the vast volume of online agrometeorological disaster information, accurately and automatically extracting essential details—such as the time, location, and extent of damage—is crucial for understanding disaster mechanisms and evolution, as well as for enhancing disaster prevention capabilities. This paper constructs a comprehensive dataset of agrometeorological disasters in China, providing a robust data foundation and strong support for event extraction tasks. Additionally, we propose a novel model named character and word embedding fusion-based GCN network (CWEF-GCN). This integration of character- and word-level information enhances the model’s ability to better understand and represent text, effectively addressing the challenges of multi-events and argument overlaps in the event extraction process. The experimental results on the agrometeorological disaster dataset indicate that the F1 score of the proposed model is 81.66% for trigger classification and 63.31% for argument classification. Following the extraction of batch agricultural meteorological disaster events, this study analyzes the triggering mechanisms, damage patterns, and disaster response strategies across various disaster types using the extracted event. The findings offer actionable decision-making support for research on agricultural disaster prevention and mitigation. Full article
(This article belongs to the Special Issue Advances in IoT Applications for Smart Agriculture and Precision Farming)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop