Pathogenic root/wood rot fungal species infect multiple urban tree species in Singapore. There is a need for sustainable and environmentally friendly mitigation. We report the local Trichoderma strains as potential biocontrol agents (BCAs) for pathogenic wood rot fungal species such as Phellinus noxius, Rigidoporus microporus, and Fulvifomes siamensis. Isolated Trichoderma strains were DNA-barcoded for their molecular identities and assessed for their potential as a BCA by their rate of growth in culture and effectiveness in inhibiting the pathogenic fungi in in vitro dual culture assays. Trichoderma harzianum strain CE92 was the most effective in inhibiting the growth of the pathogenic fungi tested. Preliminary results suggested both volatile organic compound (VOC) production and direct hyphal contact contributed to inhibition. SPME GC-MS identified known fungal inhibitory volatiles. Trichoderma harzianum strain CE92 hyphae were found to coil around Phellinus noxius and Lasiodiplodia theobromae upon contact in vitro and were possibly a part of the mycoparasitism. In summary, the work provides insight into Trichoderma inhibition of pathogenic fungi and identifies local strains with good potential for broad-spectrum BCAs against root/wood rot fungi in Singapore.

Mature tropical urban trees are susceptible to root and trunk rot caused by pathogenic fungi. A metagenomic survey of such fungi was carried out on 210 soil and tissue samples collected from 134 trees of 14 common species in Singapore. Furthermore, 121 fruiting bodies were collected and barcoded. Out of the 22,067 OTUs (operational taxonomic units) identified, 10,646 OTUs had annotation information, and most were either ascomycetes (63.4%) or basidiomycetes (22.5%). Based on their detection in the diseased tissues and surrounding soils and/or the presence of fruiting bodies, fourteen basidiomycetes (nine Polyporales, four Hymenochaetales, one Boletales) and three ascomycetes (three species of Scytalidium) were strongly associated with the diseased trees. Fulvifomes siamensis affected the largest number of tree species surveyed. The association of three fungi was further supported by in vitro wood decay studies. Genetic heterogeneity was common in the diseased tissues and fruiting bodies (Ganoderma species especially). This survey identified the common pathogenic fungi of tropical urban trees and laid the foundation for early diagnosis and targeted mitigation efforts. It also illustrated the complexity of fungal ecology and pathogenicity.

Estimation of the aerodynamic load on trees is essential for urban tree management to mitigate the risk of tree failure. To assess that in a cost-effective way, scaled down tree models and numerical simulations were utilized. Scaled down tree models reduce the cost of experimental studies and allow the studies to be conducted in a controlled environment, namely in a wind or water tunnel, but the major challenge is to construct a tree model that resembles the real tree. We constructed 3D-printed scaled down fractal tree models of major urban tree species in Singapore using procedural modelling, based on species-specific growth processes and field statistical data gathered through laser scanning of real trees. The tree crowns were modelled to match the optical porosity of real trees. We developed a methodology to model the tree crowns using porous volumes filled with randomized tetrahedral elements. The wind loads acting on the tree models were then measured in the wind tunnel and the velocity profiles from selected models were captured using particle image velocimetry (PIV). The data was then used for the validation of Large Eddy Simulations (LES), in which the trees were modelled via a discretized momentum sink with 10–20 elements in width, height, and depth, respectively. It is observed that the velocity profiles and drag of the simulations and the wind tunnel tests are in reasonable agreement. We hence established a clear relationship between the measured bulk drag on the tree models in the wind tunnel, and the local drag coefficients of the discretized elements in the simulations. Analysis on the bulk drag coefficient also shows that the effect of complex crown shape could be more dominant compared to the frontal optical porosity.

A dual-polarized compact Vivaldi antenna with high gain performance is proposed for tree radar applications. The proposed design introduces an array configuration consisting of two pairs of two Vivaldi elements to optimize the operating bandwidth and gain while providing dual-polarization capability. To enhance the gain of the proposed antenna over a certain frequency range of interest, directors and edge slots are incorporated into each Vivaldi element. To further enhance the overall antenna gain, a metal back reflector is used. The measurement results of the fabricated antenna show that the proposed antenna achieves a high gain of 5.5 to 14.8 dBi over broadband from 0.5 GHz to 3 GHz. Moreover, it achieves cross-polarization discrimination larger than 20 dB, ensuring high polarization purity. The fabricated antenna is used to detect and image the defects inside tree trunks. The results show that the proposed antenna yields a better-migrated image with a clear defect region compared to that obtained by a commercial Horn antenna.

Tree defect detection is crucial for the structural health screening of trees. Existing nondestructive testing (NDT) techniques for tree defect detection require time-consuming and labor-intensive measurement campaigns. This discourages their application for the routine structural health screening of whole populations of managed urban trees. To address this issue, this study proposes a deep-learning augmented stand-off radar scheme for contactless scanning of tree trunks and rapid detection of tree defects. In this scheme, the antenna is moved along a straight trajectory at a distance from the tree trunk to obtain the trunk’s B-scan. The obtained raw B-scan is then processed by a signal-processing framework specifically developed for revealing the scattering signatures of defects in B-scan, which achieves a 30 dB and 22 dB increase in the signal-to-clutter and noise ratio of the measurement data of tree trunk samples and living trees, respectively. Finally, the processed B-scan is input into a multilevel feature fusion neural network particularly designed for extracting the signature of the defect in the processed B-scan in real time. The developed scheme’s applications to the detection of defects in real fresh-cut tree trunks show that the stand-off radar scheme can detect tree defects with 96% accuracy. This stand-off radar scheme is the first contactless NDT technique for tree defect detection while operated on a straight trajectory and potentially can be integrated into the routine tree inspection workflow which is part of urban tree management.

Ground-penetrating radar (GPR) data are often contaminated by hardware and environmental clutter, which significantly affects the accuracy and reliability of target response identification. Existing supervised deep learning techniques for removing clutter in GPR data require generating a large set of clutter-free B-scans as labels for training, which are computationally expensive in simulation and unfeasible in real-world experiments. To tackle this issue, we propose a two-stage unsupervised learning-based clutter removal scheme, called ULCR-Net, to obtain clutter-free GPR B-scans. In the first stage of the proposed scheme, a diffusion model tailored for GPR data augmentation is employed to generate a diverse set of raw B-scans from the input random noise. With the augmented dataset, the second stage of the proposed scheme uses a contrastive learning-based generative adversarial network to learn and estimate clutter patterns in the raw B-scan. The clutter-free B-scan is then obtained by subtracting the clutter pattern from the raw B-scan. The training of the two-stage network only requires a small set of raw B-scans and clutter-only B-scans that are readily available in real-world applications. Extensive experiments have been conducted to validate the effectiveness of the proposed method. Results on simulation and measurement data demonstrate that the proposed method has superior clutter removal accuracy and generalizability and outperforms existing algebraic techniques and supervised learning-based methods with limited training data by a large margin. With its high clutter suppression capability and low training data requirements, the proposed method is well-suited to remove clutter and restore target responses in real-world GPR applications.