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Remote Sensing Technology for Early Myrtle Rust Detection

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Recent research indicates that the utilisation of remote sensing technology could be beneficial in combatting myrtle rust. Scion scientists from the Crown Research Institute have achieved a notable milestone by effectively identifying myrtle rust in nursery plants several days prior to the manifestation of visible infection symptoms.

This early detection holds significant potential, as it empowers nurseries to promptly initiate control treatments and effectively halt the progression of the disease. By swiftly implementing preventive measures, nurseries can effectively curtail the spread of myrtle rust and mitigate its detrimental impact on the affected plants. This timely intervention would prevent further spread and mitigate the impact of myrtle rust on the affected plants and surrounding areas.

Myrtle rust was discovered in New Zealand in 2017, and its presence hinders tree regeneration, potentially leading to tree death. The iconic New Zealand native trees such as Pōhutakawa, mānuka, and rātā, are among the plant species that are at risk of succumbing to the detrimental effects of myrtle rust. In addition to these native species, commercially grown varieties including eucalyptus and feijoas also face the potential threat of myrtle rust infection.

This highlights the wide range of plant species that could be adversely affected by this invasive pathogen, emphasising the need for proactive measures to safeguard their health and ensure their long-term survival is the decent way to be concerned.

Principal Scientist Mike Watt highlighted elaborated on the use of advanced precision equipment currently employed to detect the myrtle rust pathogen in deliberately inoculated rose apple leaves during the process of identifying myrtle rust infection.

The advanced technology employed for detection operates by detecting variations in both leaf temperature and light wavelengths in apple trees affected by myrtle rust infection. By analysing these subtle changes, the technology can effectively discern the presence of the pathogen and provide valuable insights for early diagnosis and targeted management strategies.

The temperature observed in infected plants’ canopies was lower due to the impact of the pathogen on the leaves. This leads to increased water loss and transpiration, resulting in a cooling effect on the leaves.

Additionally, another noticeable characteristic was the greater variability in canopy temperature across different areas of the infected leaves, unlike the uniform temperature observed in non-inoculated control plants.

By considering these two variables alone, all the plants were accurately classified with 100% certainty even before visible signs of the disease were apparent. Regarding hyperspectral imagery, Watt explained that they could analyze changes in the wavelength of light reflected from infected leaves.

As highlighted by Watt, A particular blue-green index serves as an indicator of chlorophyll levels, revealing a reduction in chlorophyll content within infected plants. This index proved effective in detecting the presence of the disease approximately three days before any visible symptoms appeared. Thus, pre-visual detection of the disease became feasible.

Stuart Fraser, a forest pathologist and leader of the ecology and environment research group, heads the team dedicated to studying myrtle rust. He expresses his optimism by describing the most recent research findings as “incredibly promising” in their potential to fight the disease effectively.

At present, the existence of myrtle rust exhibits an irregular distribution across the North Island and the northern regions of the South Island. This pathogen is most observed during periods of warm and wet weather conditions.

The characteristic symptoms of myrtle rust include the formation of vibrant yellow-orange powdery pustules on the young leaves, shoots, fruits, and flowers of plants belonging to the myrtle family. These pustules lead to leaf deformation and twig dieback, resulting in their eventual demise.


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