Machine Learning and Spectroscopy for Plant Phenotyping and Physiological Analysis
A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 28
Special Issue Editors
Interests: biochemical and molecular analyses; chlorophyll a fluorescence; gas-exchange; plant phenotyping; photosynthesis; spectroscopy
Special Issues, Collections and Topics in MDPI journals
Interests: data fusion and processing; machine learning; multispectral and hyperspectral sensors; remote sensing; precision agriculture; UAV
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advancements in machine learning (ML) and spectroscopy have revolutionized plant phenotyping and physiological analysis. This Special Issue aims to explore the intersection of these technologies in advancing plant science, offering innovative solutions for plant research, crop management, and environmental monitoring. ML algorithms and spectroscopy techniques, such as hyperspectral and multispectral proximal and imaging sensing, have proven invaluable in enhancing the precision and efficiency of phenotyping, enabling a deeper understanding of plant growth, health, and responses to environmental factors.
In this Special Issue, we invite contributions that address the application of ML algorithms and spectroscopy in plant phenotyping, ranging from the analysis of plant morphology to the study of physiological traits such as photosynthesis, chlorophyll fluorescence, and gas exchange. We also welcome studies on the integration of these tools with remote sensing and UAV technologies, particularly in general plant analysis, precision agriculture, and large-scale crop monitoring. This Issue will serve as a platform to showcase cutting-edge research, offering insights into how these techniques can be applied to a variety of plants and crop improvements and to climate change adaptation through phenotyping and physiological analysis.
Dr. Renan Falcioni
Prof. Dr. Marcos Rafael Nanni
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. 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
- agricultural remote sensing
- chlorophyll fluorescence
- crop yield estimation
- plant analysis
- hyperspectral imaging
- machine learning for phenotyping
- modelling
- multispectral sensing
- pigment estimation
- plant growth prediction
- plant health monitoring
- plant modelling
- plant morphology analysis
- plant phenotyping
- precision agriculture
- stress
- remote sensing
- spectroscopy in plant science
- photosynthesis
- UAV
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