Research Topic

Digital Imaging of Plants

About this Research Topic

Several laboratory techniques have been developed to detect plant stress or to understand physiological responses to different stress conditions. Molecular techniques and metabolomics rely on two different acquisition strategies, nuclear magnetic resonance (MR) and mass spectrometry (MS). Techniques based on ...

Several laboratory techniques have been developed to detect plant stress or to understand physiological responses to different stress conditions. Molecular techniques and metabolomics rely on two different acquisition strategies, nuclear magnetic resonance (MR) and mass spectrometry (MS). Techniques based on volatile organic compounds transmit different signals/profiles, which are usually analysed with different and complex detection systems. Such laboratory methods, although very accurate, involve destructive sampling and allow monitoring of a limited number of plants or plant sections.

Recently, a series of innovative approaches have been introduced to overcome the limitation of the above-mentioned techniques. Among the most studied, but still, with ample research potential, remote (or proximal) sensing techniques, using both non-imaging and imaging approaches, have provided new insights into rapid, objective, time-repeated, and non-destructive identification and quantification of plant stress conditions. Also, positron emission tomography (PET), in combination with computed tomography (CT) and magnetic resonance imaging (MRI), provides a repeatable and non-disruptive imaging approach to measure plant metabolic processes, such as, among others, carbon metabolism and transport within the phloem/xylem system.

This innovative and highly cross-disciplinary research subject embraces engineering, physics, mathematics, and plant science, resulting in the widespread dissemination of research across different journals belonging to different research areas. We, therefore, believe it is of interest to the scientific community to collect original reviews and articles on new methods and approaches based on novel imaging techniques, with the specific focus of an early measurement and diagnosis of the effects of abiotic and biotic stress. The long-term vision of this Research Topic is to develop and provide new decision-making tools for digital agriculture, based on the most modern continuous monitoring techniques with AI perspectives, which will play a key role in the management of sustainable crops.

In this article collection, we present interdisciplinary research on the digital imaging of plants in the following subtopics:

• Imaging of functional mechanisms in soft and thin structures of seeds, sprouts, leaves, and plants;
• Functional plant imaging;
• Quantification of mechanisms underlying growth, adaptation, development, and productivity of cultivated plants;
• Water transport dynamics in plants;
• Early prediction of abiotic and biotic stress in plants.

Please note that descriptive studies of plant responses to stress, will not be considered for review unless they are expanded and provide mechanistic and/or physiological insights into the biological system or process being studied.


Keywords: Digital imaging, positron emission tomography, computed tomography, magnetic resonance imaging, plant metabolic processes, plant water transport, digital agriculture, hyperspectral imaging, image processing and machine learning, X-ray imaging


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

31 March 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

31 March 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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