New Trends in Smart Horticulture
A special issue of Horticulturae (ISSN 2311-7524).
Deadline for manuscript submissions: 30 September 2025 | Viewed by 76
Special Issue Editors
Interests: remote sensing; UAV; precision agriculture; digital agriculture; spray drones
Interests: digital agriculture; agricultural machinery; agricultural mechanization
Special Issues, Collections and Topics in MDPI journals
Interests: irrigation and water management; machine vision and deep learning; map and sensor based variable rate technology; precision harvesting technologies
Special Issues, Collections and Topics in MDPI journals
Interests: data-driven decision making; controlled environment agriculture; crop monitoring and decision making; sustainable horticulture; yield prediction; high-throughput phenotyping
Special Issue Information
Dear Colleagues,
The rapid advancements in technology are reshaping horticultural practices, enabling more efficient, sustainable, and data-driven crop management. This Special Issue, New Trends in Smart Horticulture, aims to highlight cutting-edge research and innovations that integrate precision agriculture, artificial intelligence, remote sensing, high-throughput phenotyping (HTP), automation, and robotics into horticultural systems.
We welcome contributions addressing topics such as smart sensors for plant and soil monitoring, UAV and satellite-based imaging for crop analysis, AI-driven decision-making tools, and novel approaches to controlled-environment agriculture. Additionally, studies exploring IoT applications, digital twins, and machine learning models to optimize resource use and improve yield prediction are encouraged.
By gathering recent scientific advancements, this Special Issue will serve as a platform for researchers and practitioners to discuss how smart technologies are transforming horticulture. We invite original research articles and reviews that demonstrate the impact of these innovations on productivity, sustainability, and resilience in horticultural production systems.
We look forward to your contributions to this exciting field of research.
Dr. Adão Felipe dos Santos
Dr. Rouverson Pereira Silva
Prof. Dr. Aitazaz A. Farooque
Dr. Edgard Henrique Costa 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
- smart horticulture
- precision agriculture
- remote sensing
- smart harvesting
- artificial intelligence
- machine learning
- Internet of Things (IoT)
- automation and robotics
- UAV and satellite imaging
- digital twins
- smart sensors
- data-driven decision making
- controlled-environment agriculture
- crop monitoring and decision making
- sustainable horticulture
- yield prediction
- high-throughput phenotyping
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