Previsual and Early Detection of Myrtle Rust on Rose Apple Using Indices Derived from Thermal Imagery and Visible-to-Short-Infrared Spectroscopy

Abstract

Myrtle rust, caused by the fungus Austropuccinia psidii, is a serious disease, that affects many Myrtaceae species. Commercial nurseries that propagate Myrtaceae spp. are prone to myrtle rust and require a reliable method that allows pre-visual and early detection of the disease. This study uses time-series thermal imagery and visible-to-short-infrared spectroscopy measurements acquired over 10 days from 81 rose apple plants (Syzygium jambos (L.) Alston) that were either inoculated with myrtle rust or maintained disease free. Using these data the objectives were to (i) quantify the accuracy of models using thermal indices and narrowband hyperspectral indices (NBHI) for pre-visual and early detection of myrtle rust using data from older resistant green leaves and young susceptible red leaves and (ii) identify the most important NBHI and thermal indices for disease detection. Using predictions made on a validation dataset, models using indices derived from thermal imagery were able to perfectly (F1 score = 1.0; accuracy = 100%) pre-visually distinguish control from infected plants one day before symptoms appeared (1 DBS) and for all stages after early symptoms appeared. Compared with control plants, plants with myrtle rust had lower and more variable normalised canopy temperature, which was associated with higher stomatal conductance and transpiration. Using NBHI derived from green leaves, excellent pre-visual classification was achieved 3 DBS, 2 DBS and 1 DBS (F1 score range = 0.89 to 0.94). The accurate characterisation of MR during pre-visual and early stages suggests that a robust detection methodology could be developed within a nursery setting.

Early Detection of Myrtle Rust on Pōhutukawa Using Indices Derived from Hyperspectral and Thermal Imagery

“…Following the method described in [27] the plants in the MR treatment were inoculated on the 4th December 2023. Inoculum used in the experiment was mass-produced on rose apple and harvested using a portable vacuum pump (Mini Cyclone Spore Collector, Tallgrass Solutions, Manhattan, KS, USA) into 00 gelatin capsules and stored for 24 h at low humidity over silica beads.…”

“…Following the method described in [27], we recorded the presence or absence of signs and symptoms for each new leaf and stem, that was distinguishable from mature growth in colour and texture. These symptoms included slight bumps or blistering on the leaf surface, red/purple spots, yellow discolouration, and developing pustules yet to break through the epidermis or fully emerged pustules with spores erupted on the surface.…”

“…A recent study [27] found that indices derived from hyperspectral data and thermal imagery were effective under controlled conditions for the pre-visual and early detection of myrtle rust on the model host rose apple (Syzygium jambos (L.) Alston). Excellent previsual classification (F1 score range = 0.89-0.94) was achieved from 3 to 1 Days Before Symptoms (DBSs) using narrow-band hyperspectral indices (NBHIs) derived from leaflevel data measured on older resistant leaves using a spectroradiometer.…”

Detecting the Short-Term Effects of Water Stress on Radiata Pine Physiology Using Thermal Imagery

“…The image capture process follows the method described in detail in Watt et al [37]. Images were captured in 16-bit tiff format.…”

Emerging advances in biosecurity to underpin human, animal, plant, and ecosystem health

“… 23 Nevertheless, there is an opportunity to build on some of these developments that initially targeted human diseases to improve detection of animal and plant diseases (e.g., high temperature related to infection), and to develop other technologies/systems that work across common biosecurity threats for human, animal, plant, and ecosystem health. 24 , 25 Visual sensor networks in combination with artificial intelligence algorithms can be used to identify insects in pheromone traps, weeds and diseased plants in cropping and natural ecosystems, as well as aquatic biofouling organisms on the hulls of ships. 26 , 27 There is also potential to combine multiple layers of autonomous surveillance technologies into integrated systems for targeted detection, localization, and management of biosecurity incursion threats.…”