Editorial Feature

Is Raman Spectroscopy a Next-Generation Phenotyping Tool for Agriculture?

Raman spectroscopy has emerged as a promising tool for next-generation phenotyping and looks set to disrupt the agricultural industry.

phenotyping, agriculture, raman spectroscopy

Image Credit: Hryshchyshen Serhii/Shutterstock.com

New Analytical Tools Required for Next-Generation Agriculture

The agricultural industry is in urgent need of innovative new analytical tools to better understand plant growth and the influence of environmental stresses on crops.

A significant knowledge gap currently exists between our understanding of the dynamics of plants grown in the lab and those grown outside in fields. To bridge this gap, scientists have been designing and testing new analytical tools to gather and process data on plant cells, tissues, and organelles in real-time to develop our knowledge of the optimal growing requirements for specific plants.

Scientists aim to establish reliable, scalable, and cost-effective platforms that can be easily deployed to study plants growing over large areas.

Over recent years, research teams have created numerous technologies capable of gathering information on plant growth and growing our knowledge about their adaptive responses to environmental stresses. Unfortunately, these currently available techniques are destructive and demand time-consuming and laborious processing of numerous plant samples and data points.

Attention has now been directed towards the development of innovative tools that can explore the spatiotemporal profile of plant chemical signals in both plants grown in the lab and in the field, allowing scientists to bridge the knowledge gap between the two.

Here, we discuss the limitations of currently available plant phenotyping tools and highlight how Raman spectroscopy offers a valuable, non-destructive, and scalable method of plant phenotyping that stands to significantly impact the agricultural industry.

Limitations of Currently Available Plant Phenotyping Tools

Numerous field-based phenotyping tools have been established in recent years. These new techniques collect high-throughput trait measurements from plants over large areas. Studies show that these newly emerging systems can collect and analyze data about the physical and physiological features of plants in a non-destructive manner, overcoming the drawbacks of traditional systems. However, these new tools still have their limitations.

The use of unmanned aerial vehicles (UAVs) for plant phenotyping and monitoring of plant disease has recently become popular. The technique offers numerous advantages, such as the ability to process data at the spatial resolution of the individual plant, to be paired with analytical devices, and mobile device-based software to produce remote sensing data. On the other hand, while these methods are currently being employed to gather data on environmental stresses and crop health based on plant trait measurements, their success often requires phenotypic traits to have already clearly manifested, visibly displaying the impact of stress on the plant. This generates a time lag between the occurrence of an environmental stressor and the point at which its impact can be measured (at the time of the physical manifestation). This limitation reduces the efficacy of such methods and reduces the capability of implementing strategies in a timely manner.

Other methods, such as post-harvest phenotyping, pocket phenotyping, and remote sensing via drones are all limited by their costly nature, low measuring speed or limited resolution.

With such significant limitations of current methods, there remains a need for a non-destructive method of phenotyping that allows scientists to understand the impact of stressors in real-time so that they can implement specific interventions.

Developments of Raman Spectroscopy Within the Agricultural Sector

Raman spectroscopy offers a non-destructive method of simultaneously interrogating multiple molecular species. The optical technique allows for greater chemical specificity than the alternative imaging methods discussed above.

Research has demonstrated that Raman spectroscopy can help monitor both abiotic and biotic stresses. Evidence has shown that several metabolites that act as markers of stress in plants, including anthocyanins carotenoids, have been effectively measured via Raman spectroscopy, indicating the impact of situations such as drought, light, and heat stress. Raman spectroscopy has also been used to measure carotenoid peaks that are associated with the presence of viral infections.

While many of these early studies were carried out in just a few species of plants, scientists believe that Raman spectroscopy could be developed into a high-throughput tool for the real-time measurement of plant stress, which would be greatly beneficial to the agricultural industry. 

Over recent years, Raman spectroscopy has rapidly progressed as a method of early detection of infection of various key plant species. Studies have shown that the technique has the potential to take the necessary measurements to make such analyses remotely, from distances as far as 100 m.

In the near future, it is likely that Raman spectroscopy will continue to develop within phenotypic applications and will be more widely adopted by the agricultural sector. The success of Raman spectroscopy in this field will also be helped by the falling cost of the necessary hardware, making the technology more accessible to a greater portion of the farming sector.

References and Further Reading

Hong, J., Kim, S., Lyou, E. and Lee, T., 2021. Microbial phenomics linking the phenotype to function: The potential of Raman spectroscopy. Journal of Microbiology, 59(3), pp.249-258. https://link.springer.com/article/10.1007/s12275-021-0590-1

Lew, T., Sarojam, R., Jang, I., Park, B., Naqvi, N., Wong, M., Singh, G., Ram, R., Shoseyov, O., Saito, K., Chua, N. and Strano, M., 2020. Species-independent analytical tools for next-generation agriculture. Nature Plants, 6(12), pp.1408-1417. https://www.nature.com/articles/s41477-020-00808-7

Yang, W., Feng, H., Zhang, X., Zhang, J., Doonan, J., Batchelor, W., Xiong, L. and Yan, J., 2020. Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. Molecular Plant, 13(2), pp.187-214. https://pubmed.ncbi.nlm.nih.gov/31981735/

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Sarah Moore

Written by

Sarah Moore

After studying Psychology and then Neuroscience, Sarah quickly found her enjoyment for researching and writing research papers; turning to a passion to connect ideas with people through writing.


Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Moore, Sarah. (2021, June 22). Is Raman Spectroscopy a Next-Generation Phenotyping Tool for Agriculture?. AZoOptics. Retrieved on May 25, 2024 from https://www.azooptics.com/Article.aspx?ArticleID=1964.

  • MLA

    Moore, Sarah. "Is Raman Spectroscopy a Next-Generation Phenotyping Tool for Agriculture?". AZoOptics. 25 May 2024. <https://www.azooptics.com/Article.aspx?ArticleID=1964>.

  • Chicago

    Moore, Sarah. "Is Raman Spectroscopy a Next-Generation Phenotyping Tool for Agriculture?". AZoOptics. https://www.azooptics.com/Article.aspx?ArticleID=1964. (accessed May 25, 2024).

  • Harvard

    Moore, Sarah. 2021. Is Raman Spectroscopy a Next-Generation Phenotyping Tool for Agriculture?. AZoOptics, viewed 25 May 2024, https://www.azooptics.com/Article.aspx?ArticleID=1964.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this article?

Leave your feedback
Your comment type

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.