AI-Powered Phenotyping of Horticultural Plants
A special issue of Horticulturae (ISSN 2311-7524).
Deadline for manuscript submissions: 28 February 2025 | Viewed by 90
Special Issue Editors
Interests: phenomics for plant breeding; machine learning; computer vision; robotics
Interests: plant physiology; plant morphology; plant phenology; soil fertility; acclimation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of artificial intelligence (AI) in phenotyping has revolutionized horticultural science, offering innovative solutions to enhance crop productivity, sustainability, and resilience. As the global demand for horticultural products continues to rise, driven by population growth and the need for nutritional security, it is imperative to develop advanced phenotyping techniques that leverage AI to address these challenges effectively.
This Special Issue focuses on the applications of AI-assisted phenotyping in horticulture, aiming to showcase cutting-edge research and developments that push the boundaries of traditional horticultural practices. We invite original research articles, comprehensive reviews, and insightful perspectives that explore the following key areas:
- AI-Driven Image Analysis and Pattern Recognition: Using machine learning and computer vision to analyze plant traits, detect diseases, and monitor growth patterns with high accuracy.
- Precision Agriculture: Implementing AI for site-specific management, optimizing resource use, and minimizing environmental impacts through precision phenotyping.
- Genomic Selection and Breeding: Integrating AI with genomic data to predict desirable traits and accelerate the development of superior horticultural varieties.
- Stress Tolerance and Adaptation: Using AI to understand and improve plant responses to biotic and abiotic stresses, enhancing crop resilience.
- Robotics and Automation in Phenotyping: Exploring autonomous systems and robots for high-throughput phenotyping, reducing labor costs, and increasing data collection efficiency.
- Data Integration and Management: Developing AI-driven platforms to manage and analyze diverse phenotypic and environmental data sets.
Dr. Xu Wang
Dr. Shinsuke Agehara
Dr. Jing Zhang
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
- phenotyping
- artificial intelligence
- precision agriculture
- horticultural science
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