Emerging Technologies in Smart Agriculture

A special issue of Horticulturae (ISSN 2311-7524).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 544

Special Issue Editors


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Guest Editor
Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: remote sensing; unmanned aerial vehicles; precision viticulture; precision agriculture; multi-temporal analysis; spectral imaging; machine learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Agronomy Department, Scholl of Agrarian and Veterinary Sciences, Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Trás-os-Montes e Alto Douro CITAB-Quinta de Prados, 5000-801 Vila Real, Portugal
Interests: irrigation and water management; drip irrigation; water use efficiency; evapotranspiration; plant water relations; drought; agrometeorology; plant based sensors; precision irrigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart agriculture, driven by the integration of advanced technologies, is redesigning the agricultural landscape to meet the growing demands for food security, sustainability, and climate resilience. This Special Issue delves into the transformative role of emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), machine learning, robotics, and precision farming in modern agriculture. These innovations are revolutionizing traditional farming practices by providing real-time data analytics, automating labor-intensive processes, and optimizing the use of resources like water, fertilizers, and pesticides.

Emerging techniques in vertical farming, hydroponics, and smart irrigation systems further push the boundaries of sustainable agricultural practices by conserving water, reducing land use, and promoting year-round crop production. Precision agriculture, utilizing satellite imagery, GPS, and unmanned aerial vehicles (UAVs), enables site-specific management practices, reducing environmental impacts while boosting productivity.

This special issue aims to showcase cutting-edge research, innovative applications, and case studies that demonstrate the practical impact and potential of these technologies. Additionally, it addresses key challenges such as technological adoption barriers, data privacy concerns, and the economic viability of implementing smart agriculture solutions. By fostering a deeper understanding of these technologies, the issue seeks to chart a sustainable path forward for the global agricultural sector in an era of increasing environmental and economic pressures.

Dr. Luís Pádua
Dr. Anabela A. Fernandes-Silva
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. Horticulturae 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 2200 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

  • precision farming
  • internet of things (IoT)
  • remote sensing
  • crop monitoring
  • controlled-environment agriculture
  • vertical farming

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Published Papers (1 paper)

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Research

28 pages, 2881 KiB  
Article
Segmentation-Based Classification of Plants Robust to Various Environmental Factors in South Korea with Self-Collected Database
by Ganbayar Batchuluun, Seung Gu Kim, Jung Soo Kim and Kang Ryoung Park
Horticulturae 2025, 11(7), 843; https://doi.org/10.3390/horticulturae11070843 - 17 Jul 2025
Viewed by 251
Abstract
Many plant image-based studies primarily use datasets featuring either a single plant, a plant with only one leaf, or images containing only plants and leaves without any background. However, in real-world scenarios, a substantial portion of acquired images consists of blurred plants or [...] Read more.
Many plant image-based studies primarily use datasets featuring either a single plant, a plant with only one leaf, or images containing only plants and leaves without any background. However, in real-world scenarios, a substantial portion of acquired images consists of blurred plants or extensive backgrounds rather than high-resolution details of the target plants. In such cases, classification models struggle to identify relevant areas for classification, leading to insufficient information and reduced classification performance. Moreover, the presence of moisture, water droplets, dust, or partially damaged leaves further degrades classification accuracy. To address these challenges and enhance classification performance, this study introduces a plant image segmentation (Pl-ImS) model for segmentation and a plant image classification (Pl-ImC) model for classification. The proposed models were evaluated using a self-collected dataset of 21,760 plant images captured under real field conditions in South Korea, incorporating various environmental factors such as moisture, water droplets, dust, and partial leaf loss. The segmentation method achieved a dice score (DS) of 89.90% and an intersection over union (IoU) of 81.82%, while the classification method attained an F1-score of 95.97%, surpassing state-of-the-art methods. Full article
(This article belongs to the Special Issue Emerging Technologies in Smart Agriculture)
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