Application of Multi- and Hyperspectral Imaging Technologies in Horticultural Production
A special issue of Horticulturae (ISSN 2311-7524).
Deadline for manuscript submissions: 10 February 2026 | Viewed by 24
Special Issue Editors
Interests: deficit irrigation; water use efficiency; soil management; crop modeling; cropping systems
Interests: remote sensing; crop modeling; machine learning; multispectral; abiotic stresses
Interests: plant physiology; mycology; irrigation; water management
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Interests: tree crops ecophysiology; precision tree crops management; tree crops sustainability; tree crops water and soil management
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Special Issue Information
Dear Colleagues,
Technological advances in multi- and hyperspectral imaging technologies are transforming horticultural production by enabling detailed and non-invasive monitoring of crop status. These approaches enable the accurate assessment of plant health, early detection of diseases, monitoring of water and nutrient status, and identification of physiological stresses, potentially with high spatial and temporal resolution. In pursuit of more sustainable horticultural practices, integrating multi- and hyperspectral imaging technologies could significantly optimize resource use, reduce chemical inputs, and minimize environmental impact. The ability to monitor crops frequently and non-destructively supports data-driven decision-making, allowing for targeted interventions that enhance yield and quality.
This Special Issue aims to provide a platform for the latest research and reviews on the application of multi- and hyperspectral imaging in horticulture. We welcome contributions that cover the following topics:
- Novel methodologies and case studies that employ remote and proximal sensing for crop monitoring, management, and modeling;
- Advances in image acquisition, analysis, and data interpretation specific to multi- and hyperspectral data for horticulture;
- Integration of imaging technologies with Artificial Intelligence (AI), Internet of Things (IoT), and precision agriculture tools;
- Comparative assessments of multispectral versus hyperspectral approaches for horticultural monitoring;
- Impacts of the aforementioned technologies and methodologies on sustainability, resource efficiency, and reduction in environmental footprint in horticultural production systems.
We invite researchers to submit original articles and comprehensive reviews exploring how these cutting-edge technologies are transforming horticultural production. Your research will advance the science and application of multi- and hyperspectral technologies as a next-generation approach to horticulture.
Dr. Pasquale Campi
Dr. Simone Pietro Garofalo
Dr. Beatriz Lorente Pagán
Dr. Giuseppe Lopriore
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
- disease detection
- water status
- physiological stress detection
- precision horticulture
- image processing
- spectral analysis
- fertilizers
- artificial intelligence in agriculture
- remote sensing
- proximal sensing
- vegetation indices
- crop modeling
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