Integration of Spectroscopic and Photosynthetic Analyses in Plants

Dear Colleagues,

In this Special Issue, "Integration of Spectroscopic and Photosynthetic Analyses in Plants," we emphasize the important convergence between spectroscopy and photosynthetic methods for analysis in plant research. This integration provides insights into plant tissues, physiological processes, and biochemical compositions, paving the way for a more comprehensive understanding of the mechanisms of plant biology.

The use of spectroscopic techniques, when aligned with different technologies, including multispectral, hyperspectral, and fluorescence imaging, promotes a better understanding and allows for non-invasive analyses of plants. This is essential for the assessment, classification, and prediction of plant responses to distinct environments. Moreover, aligning photosynthetic analyses with these methodologies can unlock profound insights into molecular photosynthetic mechanisms and ecosystem-level plant dynamics.

We also highlight the increasingly significant role of artificial intelligence, machine learning, and deep learning algorithms in accurately detecting changes induced by stresses, different treatments, and interactions across various contexts, such as laboratory experiments, greenhouses, fields, and environments associated with monitoring by remote sensing in plant research. These advanced computational tools are instrumental in managing and interpreting the extensive datasets generated by optical methods, thereby facilitating more precise and efficient correlations between spectroscopic and photosynthetic analyses.

All discussions and findings presented in this issue will significantly contribute to the ongoing progress of our understanding of plants. This comprehensive, integrative approach promises high-throughput breakthroughs in plant productivity, sustainability, and resilience, with the potential to transform both academic research and practical applications in plant biology.

In this issue, we will explore a wide range of topics, including but not limited to the following:

• Integration of spectroscopy and photosynthesis analyses;
• Applied spectroscopy in plant tissues;
• Biophysical, physiological, biochemical, genetic, and molecular advances in relation to spectroscopy;
• Chlorophyll a fluorescence and leaf optical properties;
• Artificial intelligence, machine learning, and deep learning algorithms in plant research;
• Development and application of chemometrics;
• Classification and prediction parameters in plants;
• Techniques and tools for plant phenotyping.

Prof. Dr. Marcos Rafael Nanni
Dr. Renan Falcioni
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 250 words) can be sent to the Editorial Office for assessment.

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.