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Special Issue "Strategies to Improve Grapevine Performance and Fruit Quality"
A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Fruit Production Systems".
Deadline for manuscript submissions: 30 June 2023 | Viewed by 854
Special Issue Editors
2. Geosystems Research Institute, Mississippi State University, Starkville, MS 39762, USA
Interests: digital and precision agriculture; UAV and satellite remote sensing: viticulture; wireless sensor networks; crop modeling; hyperspectral and thermal imagery; machine learning and geostatistics
Special Issues, Collections and Topics in MDPI journals
Special Issue in Remote Sensing: Remote Sensing for Agroforestry
Special Issue in Remote Sensing: Remote Sensing in Viticulture
Special Issue in Remote Sensing: Digital Agriculture
Special Issue in Forests: Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2020
Special Issue in Remote Sensing: Precision Agriculture Using Hyperspectral Images
Special Issue in Agriculture: Sensors and Remote Sensing in Precision Horticulture
Interests: viticulture; precision viticulture; climate change
Special Issue Information
The greatest challenge for winegrowers currently is combining improved yield and fruit quality with the minimization of management cost and environmental impact. In recent decades, climate change has enhanced temperatures and reduced water availability, resulting in an earlier onset of phenological stages, severe yield losses, and altered grape quality. Grapevine performance and fruit quality can be assessed according to destructive and non-destructive methods for measuring several parameters such as yield, grape composition, and health status. Due to a rising number of geomatics, variable rate technologies, and robotics and artificial intelligence solutions, novel technologies based on rapid, cost-effective and non-destructive tools have become available to support winegrowers in the movement towards digital agriculture. Moreover, new cultural practices concerning canopy and floor management can help growers improve grapevine performance and vineyard efficiency. The aim of the current Special Issue is to disclose case studies describing a wide range of methods and tools developed worldwide to improve grapevine performance and fruit quality under a climate change scenario, with special emphasis on the application of digital technologies.
Dr. Alessandro Matese
Dr. Matteo Gatti
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 1800 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.
- fruit health
- fruit quality
- climate change
- soil quality
- water stress
- cultural practices
- digital agriculture
- data analysis
- artificial intelligence (AI) and machine learning (ML) methodologies
- high-throughput field phenotyping (HTFP)
- vineyard zoning
- variable-rate technologies
- disease detection