Crop losses due to disease pose a significant threat to global food security. With over 40% of global land dedicated to crop rearing, crops are one of the most important components in the global food chain.1 Crops affected by disease can either be completely lost or have hindered growth reducing their overall yield.
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One of the most important factors in managing and minimizing the impact of crop disease is agricultural practices.1 Plant diseases can be fungal, viral, or bacterial in origin with some being caused by the presence of parasitic organisms such as nematodes.2 Some diseases are noninfectious, so will not be spread from plant to plant, but others, particularly if they are spread by living organisms, can be highly contagious and devastate huge areas of crops if left untreated.
Agricultural strategies to help minimize the devastation that can be caused by plant diseases include pesticide use, crop diversity and rotation, good plant, tool and soil maintenance, and the use of disease-resistant plant varieties. Good crop management also means constant monitoring of plant health for signs of disease.
Disease Indicators on Crops
Many plant diseases result in visual signs on the plant such as abnormal growth of stems or leaves, or discoloration of parts of the plant. The visual signs of disease can help provide a diagnosis for treatment of the plant, though a fully comprehensive diagnosis requires laboratory measurements and analysis.
The use and practicality of visual indicators of disease have led to the development of many automated machine vision applications for the maintenance of crop health.3 Autonomous vehicles can be used to survey the crop area and record images that can then be analyzed for the signatures of diseases. Warnings can be passed to the farmer for intervention.
Manual visual inspections of agricultural land can be immensely labor-intensive, particularly when considering the huge areas that many farmlands span. Automated methods of image acquisition and analysis do not work well in all weather and light conditions and can struggle to image areas of the plant that are obscured by foliage. Some diseases often manifest on the underside of leaves and acquiring images from several angles with automated vehicles and onboard cameras can be challenging.
Therefore, alternative measurements such as plant temperatures or spectroscopic information can be very useful indicators for plant health. Hyperspectral imaging is now becoming widely used as a tool for monitoring crop health4, and developments in Raman spectroscopy might prove useful not just for plant monitoring but understanding some of the underlying mechanisms associated with plant diseases and immunity.5
Raman spectroscopy is a qualitative and quantitative analytical technique that uses the inelastically scattered radiation from a sample to record information on the vibrational structure of the molecular species. As the frequencies of the vibrational modes of a molecule are related to the types of chemical groups present and their structure, a full Raman spectrum provides a ‘chemical fingerprint’ of a molecular species.
Recent work has made use of Raman spectroscopy as a tool for investigating the early-stage immune responses of plants when exposed to diseases.5
Raman spectroscopy has become widely used in the medical community for disease diagnosis due to its ability to detect pathogens and cellular changes associated with tumor growth. The technique can also be adapted for imaging experiments to recover spatially resolved chemical information.
The team investigates whether Raman spectroscopy could help detect early changes in cellular metabolites following infection of the plant by a pathogenic species. Many plants on infection with a pathogenic species show a reduction in the number of carotenoid species present, molecules which have characteristic vibrational frequencies detectable using a Raman measurement.
By looking at the leaves of WT Arabidopsis, the team identified reproducible changes in the Raman spectrum on infection with the pathogen. While the chemical complexity of the leaf and pathogen composition leads to many spectral features in the Raman measurement, the carotenoid bands showed clear, well-resolvable changes and could be used as a proxy for disease status.
Early Diagnosis of Plant Disease
The same trends were observed for plants of several genotypes. The changes in the Raman signals could be observed approximately 10-24 hours after infection and the method showed good discriminatory abilities between the mock and infected leaves. This time window is much shorter than the time taken for visible signs of infection to appear on the plant. This shows that Raman spectroscopy could be an excellent tool for early screening and minimizing the spread of infectious diseases. This would help reduce any unnecessary crop loss where plant removal is necessary versus plant treatment.
There are now many portable Raman spectrometers that can be mounted on automated vehicles, so there is a real possibility of extending these measurements to the field as part of many new data-driven developments in agricultural practice.
References and Further Reading
- Savary, S., & Willocquet, L. (2020). Modeling the Impact of Crop Diseases on Global Food Security. Annual Review of Phytopathology, 58, 313–341. https://doi.org/10.1146/annurev-phyto-010820-012856
- Jones, J. T., Haegeman, A., Danchin, E. G. J., Gaur, H. S., Helder, J., Jones, M. G. K., Kikuchi, T., Manzanilla-lópez, R., Palomares-rius, J. E., Wesemael, W. I. M. M. L., & Perry, R. N. (2013). Top 10 plant-parasitic nematodes in molecular plant pathology. Molecular Plant Pathology, 14(9), 946–961. https://doi.org/10.1111/mpp.12057
- Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science, 7, 1–10. https://doi.org/10.3389/fpls.2016.01419
- Park, B., & Lu, R. (Eds.). (2015). Hyperspectral imaging technology in food and agriculture. New York:: Springer.
- Chung, P. J., Singh, G. P., Huang, C., & Koyyappurath, S. (2021). Rapid Detection and Quantification of Plant Innate Immunity Response Using Raman Spectroscopy. Frontiers in Plant Science, 12, 1–15. https://doi.org/10.3389/fpls.2021.746586