Predicting and Understanding Plant Properties with Data-Driven Approaches
A special issue of Plants (ISSN 2223-7747).
Deadline for manuscript submissions: 20 December 2024 | Viewed by 920
Special Issue Editor
Special Issue Information
Dear Colleagues,
The state of a plant can be determined by its diverse properties, such as growth factors, yield, and morphological factors. All the data collected during plant growth, such as destructive investigation, sensing environmental changes, capturing crop images, and even recording market trends, has resulted in generating a massive database for plants. These collection tasks provide a basis for data-driven approaches, such as predictive analytics and machine learning algorithms.
This Special Issue aims to cover the general applications of data-driven studies for plants. We would like to explore how the field has shifted from relying on empirical knowledge to adopting advanced data-driven strategies to plant responses. We are interested in manuscripts analyzing plant properties with any data-driven approaches. The goal is not only to highlight recent updates but also record technological advancements for plant science and engineering. Contributions could establish a collaborative archive containing plant-specific methodologies using advanced technologies.
We welcome contributions that focus on the following:
- Trace the historical development of data-driven approaches for plant science;
- Develop and evaluate novel applications of plant growth and yield prediction;
- Integrate plant data, such as crop appearance and growth, into decision-making processes;
- Extract plant phenotypes from collected data.
The included studies are not limited to deep learning or machine learning. Adequate methods using adequate data would be preferred. Every study should contain novel research in plant science and engineering.
Dr. Taewon Moon
Guest Editor
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. Plants is an international peer-reviewed open access semimonthly 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 2700 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
- artificial intelligence
- data analysis
- fata engineering
- deep learning
- machine learning
- crop modelling
- crop yield
- plant growth
- plant morphology
- plant physiology
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