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Agronomy
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Sustainable Precision Agriculture with Wireless Sensor Networks in Crop Monitoring...
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Sustainable Precision Agriculture with Wireless Sensor Networks in Crop Monitoring Aligned with SDGs

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 628

Special Issue Editors

Dr. Anikó Nyéki

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Guest Editor
Albert Kázmér Faculty in Mosonmagyaróvár, Department of Biological Systems and Precision Technology, Széchenyi University, Vár 2, 9200 Mosonmagyaróvár, Hungary
Interests: artificial intelligence; crop modeling; soil variability; site-specific crop management; big data; Internet of Things in crop production
Special Issues, Collections and Topics in MDPI journals
Dr. Attila József Kovács

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Guest Editor
Department of Biological Systems and Precision Technology, Albert Kázmér Faculty in Mosonmagyaróvár, Széchenyi University, Vár 2, 9200 Mosonmagyaróvár, Hungary
Interests: precision agriculture
Prof. Dr. Miklós Neményi

E-Mail Website
Guest Editor
Department of Biosystems Engineering and Precision Technology, Albert Kázmér Mosonmagyaróvár Faculty of Agricultural and Food Sciences, Széchenyi István University, H-9200 Mosonmagyaróvár, Hungary
Interests: sustainable agriculture; crop production; plant nutrition; crop management; precision agriculture; IoT and AI in crop management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of precision agriculture technologies has significantly transformed crop production by integrating wireless sensor networks (WSNs) and data-driven solutions. These technologies are not only improving efficiency and productivity but are also contributing to the global sustainability goals as outlined in the United Nations Sustainable Development Goals (SDGs). Wireless sensor networks play a crucial role in real-time monitoring of environmental factors such as soil moisture, temperature, nutrient levels for optimizing resource usage, improving yield quality, and reducing waste. In crop production, soil sensors, nutrient level detectors, remote sensing via drones or satellites, and automated systems are commonly employed. The result is not only improved crop yields but also reduced input costs and more sustainable farming practices in line with SDGs 12 (responsible consumption and production) and 13 (climate action).

Through real-time data collection and monitoring, farmers can minimize water usage, reduce pesticide and medicine application, and lower greenhouse gas emissions, thus enhancing both economic, environmental and food safety sustainability. The integration of big data from wireless sensors with artificial intelligence models also offers enhanced decision-making capabilities, allowing for better management of crop variability, disease control, and efficient resource allocation.

This Special Issue invites submissions that explore the role of wireless sensor networks, IoT, and AI in enhancing precision agriculture, with a particular focus on how these technologies align with the goals of sustainable development. Research on the use of big data and inter-seasonal databases to predict agricultural outcomes and improve decision-making in crop is particularly encouraged.

Dr. Anikó Nyéki
Dr. Attila József Kovács
Prof. Dr. Miklós Neményi
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

  • sustainable agriculture
  • wireless sensor networks
  • precision farming
  • SDGs
  • IoT in agriculture
  • crop monitoring
  • big data
  • environmental sustainability
  • artificial intelligence in farming

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Published Papers (1 paper)

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Research

22 pages, 4664 KiB  
Article
Aerial Image-Based Crop Row Detection and Weed Pressure Mapping Method
by László Moldvai, Péter Ákos Mesterházi, Gergely Teschner and Anikó Nyéki
Agronomy 2025, 15(8), 1762; https://doi.org/10.3390/agronomy15081762 - 23 Jul 2025
Viewed by 376
Abstract
Accurate crop row detection is crucial for determining weed pressure (weeds item per square meter). However, this task is complicated by the similarity between crops and weeds, the presence of missing plants within rows, and the varying growth stages of both. Our hypothesis [...] Read more.
Accurate crop row detection is crucial for determining weed pressure (weeds item per square meter). However, this task is complicated by the similarity between crops and weeds, the presence of missing plants within rows, and the varying growth stages of both. Our hypothesis was that in drone imagery captured at altitudes of 20–30 m—where individual plant details are not discernible—weed presence among crops can be statistically detected, allowing for the generation of a weed distribution map. This study proposes a computer vision detection method using images captured by unmanned aerial vehicles (UAVs) consisting of six main phases. The method was tested on 208 images. The algorithm performs well under normal conditions; however, when the weed density is too high, it fails to detect the row direction properly and begins processing misleading data. To investigate these cases, 120 artificial datasets were created with varying parameters, and the scenarios were analyzed. It was found that a rate variable—in-row concentration ratio (IRCR)—can be used to determine whether the result is valid (usable) or invalid (to be discarded). The F1 score is a metric combining precision and recall using a harmonic mean, where “1” indicates that precision and recall are equally weighted, i.e., β = 1 in the general Fβ formula. In the case of moderate weed infestation, where 678 crop plants and 600 weeds were present, the algorithm achieved an F1 score of 86.32% in plant classification, even with a 4% row disturbance level. Furthermore, IRCR also indicates the level of weed pressure in the area. The correlation between the ground truth weed-to-crop ratio and the weed/crop classification rate produced by the algorithm is 98–99%. As a result, the algorithm is capable of filtering out heavily infested areas that require full weed control and capable of generating weed density maps on other cases to support precision weed management. Full article
(This article belongs to the Special Issue Sustainable Precision Agriculture with Wireless Sensor Networks in Crop Monitoring Aligned with SDGs)
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