Leaf Skeleton Research Articles

Classical biological control of the western grape leaf skeletonizer in California, a review 1941–2022

ABSTRACT Western grape leaf skeletonizer was first reported in southern California in 1941 and soon became a serious pest of commercially produced grapes in that region. Their gregarious larvae can easily defoliate vineyards and they remained a major problem for production of grapes until this pest was reunited with its host-specific natural enemies. Efforts to import into southern California effective natural enemies during the 1950s from southern Arizona succeeded in reducing populations of this moth to economically tolerable levels. We report for the first time on releases of natural enemies into central and northern California for control of western grape leaf skeletonizer. Over 600,000 parasitoids new to this region were released during late 1970s–1980s by the state’s Biological Control Program. Foreign exploration by the California Department of Food and Agriculture (CDFA) into Mexico in 1978 resulted in additional parasitoids new to California: a new biotype of Ametadoria harrisinae and a new species of Chetogena sp. (Tachinidae). The high numbers of tachinids released throughout California is credited with aiding in the spread of a highly virulent strain of granulosis virus which in turn caused high levels of mortality to western grape leaf skeletonizer larvae. Reports of WGLS outbreaks in vineyards over the last 15 years are rare, in contrast to previous years.

Dynamic analysis and optimal control strategies of a predator–prey mathematical model for the pest eradication in oil palm plantation

Oil palm cultivation stands as a crucial industry in Indonesia, significantly contributing to the nation’s economy by generating employment opportunities and fostering social welfare for communities residing near plantations. Despite its economic importance, oil palm plantations face various challenges, with one prominent issue being the infestation of nettle caterpillar pests. These pests cause leaf skeletonization, resulting in a staggering 36% reduction in oil palm productivity over a two-year period. This paper explores diverse strategies for pest management in oil palm plantations, encompassing biological control through the stimulation of natural predators, mechanical control involving the collection and incineration of cocoons, and chemical control through pesticide application. The research introduces a predator–prey mathematical model for oil palm plantation pests, where the leaf area serves as the primary food source for caterpillars, acting as prey. Through dynamic model analysis, four equilibrium points are identified, with interconnected conditions dictating their existence and stability. These conditions are visually represented in a bifurcation plane, providing concise information. The study further includes bifurcation diagrams of equilibrium points to elucidate the influence of each parameter on pests, predators, and the leaf area of oil palm plants. Additionally, sensitivity analysis of the stable interior equilibrium point is conducted to understand the impact of individual parameters. The paper extends its investigation to optimal control strategies, evaluating six scenarios categorized into two population conditions: with predators and without predators. Within each population condition, three control strategies are considered—chemical control only, mechanical control only, and a combination of chemical and mechanical control. Simulation results from the optimal control study reveal that the presence of natural predators emerges as a pivotal strategy in effectively managing nettle caterpillars. Notably, the control of resistant pests through pupa incineration has a substantial impact on reducing the caterpillar population in subsequent life cycles.

First comprehensive higher level phylogeny of Zygaenidae (Lepidoptera) including estimated ages of the major lineages and a review of known zygaenid fossils

AbstractZygaenidae, also known as burnet, forester, smoky, or leaf skeletonizer moths, are a family of mainly diurnal moths well known for their aposematic colouration and the ability to release hydrogen cyanide as a defence mechanism. So far, few attempts have been made to understand the evolutionary history of the global zygaenid fauna. Here, we inferred the most comprehensive molecular phylogeny for Zygaenidae to date and estimated the lineage timing‐of‐divergence with a Bayesian approach. Building on earlier work, we significantly increased the taxon and gene sampling for the family, which here included data from 30 gene fragments, recovered from public databases or newly sequenced, for almost 30% of the species representing 92 genera (49%) and all five subfamilies. We recovered strong support for the monophyly of Zygaenidae, Chalcosiinae, and Zygaeninae. Procridinae were recovered as monophyletic with low support, whereas the monophyly of Callizygaeninae remains untested as we sampled only one of the two genera. In the core dataset, we recovered Procridinae as sister to Callizygaeninae + Chalcosiinae. This large clade is the sister lineage to Zygaeninae. The position of Inouelinae could not be resolved. The lineage leading to the extant Zygaenidae appears to have diverged in Late Cretaceous (ca. 86 Ma), while the divergence among the subfamilies occurred several million years before the Cretaceous–Paleogene mass extinction event (ca. 66 Ma). Additionally, we provide a review of known fossil Zygaenidae as Appendix S1. Our results form a strong basis for future studies of zygaenid biosystematics, including their ecology, evolution, and behaviour.

Structure, Synthesis, and Bioassays of Sex Pheromone for Pyrausta machaeralis (Lepidoptera: Crambidae).

The leaf skeletonizer, Pyrausta machaeralis (Lepidoptera: Crambidae), is a serious insect pest of teak (Tectona grandis) in China. The application of insect pheromones is widely applied as an environmentally friendly technology for integrated pest management (IPM). In the present study, crude extracts of sex pheromone glands of calling P. machaeralis females were collected and then analyzed using gas chromatography/electroantennographic detection (GC/EAD) and gas chromatography-mass spectrometry (GC-MS). The combination of infrared spectroscopy (IR) and nuclear magnetic resonance (NMR) spectrometry was used for structure identification. Afterward, their electrophysiological and behavioral activity was evaluated in the laboratory and field. Herein, we eventually determined two active components, E-11-tetradecenyl acetate (E11-14:Ac) and Z-11-tetradecenyl acetate (Z11-14:Ac), at a ratio of 96:4, as the sex pheromone of P. machaeralis. The identification of sex pheromones would facilitate the development of efficient strategies for monitoring and controlling the field populations of P. machaeralis.

A scarab beetle (Coleoptera: Scarabaeidae) causes tree mortality, delayed growth, and yield reduction in cacao with genotype-specific susceptibility to herbivory.

This study explored the impact of Leucothyreus femoratus, a previously unreported folivorous pest in cacao cultivation, on cacao tree survival, development, and yield. The study was conducted in an experimental cacao plot in the Colombian plains, it featured 20 cacao genotypes in an agroforestry system, with plantain and Mexican sunflower providing temporary shade, and yopo offering permanent shade. We found an infestation rate of 2.9 ± 0.3 adult beetles per cacao tree. L. femoratus larvae were discovered in association with the roots of all plants within the agroforestry arrangement; however, yopo and plantain exhibited the highest incidence of root-feeding larvae among these associated plants. Interestingly, male and female L. femoratus displayed distinct leaf consumption patterns in the laboratory, with females consuming more foliage relative to their body weight. Moreover, field observations highlighted the detrimental impact of L. femoratus herbivory on cacao tree survival and growth, leading to leaf skeletonization, reduced plant height, and stem diameter. Trees with over 50% leaf consumption suffered more than 20% mortality. Additionally, herbivory negatively affected cacao yield, correlating higher leaf surface damage with a decrease in harvested pods. The study also identified varying antixenotic resistance in different cacao genotypes, with some consistently displaying resistance while others showed variable levels during tree establishment and production stages. This research underscores the significant role of L. femoratus as a cacao pest, emphasizing its adverse effects on cacao tree survival, development, and yield. Consequently, implementing effective control measures is vital for ensuring sustainable cacao cultivation. © 2023 Society of Chemical Industry.

Highly sensitive self-focused ultrasound transducer with a bionic back-reflector for multiscale-resolution photoacoustic microscopy.

In this study, we designed a self-focused ultrasonic transducer made of polyvinylidene fluoride (PVDF). This transducer involves a back-reflector, which is modeled after tapetum lucidum in the eyes of some nocturnal animals. The bionic structure reflects the ultrasound, which passes through the PVDF membrane, back to PVDF and provides a second chance for the PVDF to convert the ultrasound to electric signals. This design increases the amount of ultrasound absorbed by the PVDF, thereby improving the detection sensitivity. Both ultrasonic and photoacoustic (PA) experiments were conduct to characterize the performance of the transducer. The results show that the fabricated transducer has a center frequency of 13.07 MHz, and a bandwidth of 96% at -6 dB. With an acoustic numerical aperture (NA) of 0.64, the transducer provides a lateral resolution of 140µm. Importantly, the bionic design improves the detection sensitivity of the transducer about 30%. Finally, we apply the fabricated transducer to optical-resolution (OR) and acoustic-resolution photoacoustic microscopy (AR-PAM) to achieve multiscale-resolution PA imaging. Imaging of the bamboo leaf and the leaf skeleton demonstrates that the proposed transducer can provide high spatial resolution, better imaging intensity and contrast. Therefore, the proposed transducer design will be useful to enhance the performance of multiscale-resolution PAM.

Achieving ultrasensitivity and high breathability in biodegradable piezoresistive electronic skins

Nonbiodegradable components, such as synthetic polymers, have been widely used in the fabrication of flexible piezoresistive sensors and electronic skins (e-skins), resulting in the generation of electronic waste (e-waste) and toxic by-products. To overcome this issue, a wide range of biodegradable natural biopolymers and synthetic polymers have been employed. However, most of the current biodegradable e-skins demonstrate limitations such as low sensitivity, poor electrical conductivity, limited breathability, and low stability. To address these limitations, this study proposes a highly breathable and ultrasensitive piezoresistive e-skin based on the oxidized single-walled carbon nanotube (Ox-SWCNT)/silver nanowire (AgNW)-coated leaf skeletons. The developed disposable piezoresistive e-skin exhibits a remarkable performance including high sensitivity (19.75 ± 1.5 kPa−1, <3 kPa), sensing range of ≤42 kPa, ultralow limit of detection (∼0.27 Pa), linearity for low- and high-pressure regimes, high flexibility, high stability (3000 cycles), low weight, ultrahigh breathability, and biodegradability. In addition, the developed leaf-based e-skin demonstrates outstanding sensing performance in human-motion monitoring by detecting various motions such as subtle and vigorous flexions, airflow, and vocal-cord vibrations. The proposed piezoresistive e-skin is a promising platform for the development of various low-cost, biocompatible, fully biodegradable, and point-of-care (POC) e-skins for a wide range of subtle and vigorous pressure-monitoring applications including human–machine interfaces (HMIs), robotics, prosthesis, and flexible electronics.

Laser-textured biomimetic copper leaf with structural-wettability dual gradient for efficient fog harvesting

Water collection has gradually become research hot topic owing to its potential for alleviating water shortages in arid areas. To date, numerous artificial materials with hierarchical structure have been fabricated to improve water-collecting efficiency. Here, a multi-bioinspired fog-harvesting artificial Cu-Leaf, deriving from Leaf skeletons, Namib desert beetle, and Cactus spines, is fabricated by ultrafast laser selective ablation, chemical etching and chemical vapor deposition, which achieve efficient water-collecting by combining fog capture, droplet growing and directional transportation together. The four-level wedge-shaped tracks of Cu-Leaf skeleton and its wetting property were finely adjusted and their correlations with the fog-harvesting capability were systematically studied. Force analysis related to fog capture, droplet growing and the droplet directional transportation on the hydrophobic/hydrophilic patterned skeleton surfaces have been highlighted. The optimal biomimetic Cu-Leaf performed superior fog-harvesting property with harvesting rate of 544.03 mg/h cm2, which is 2.5 times that of the original Cu sheet. Moreover, we designed an integrated fog-harvesting device (FHD) that could realize the dynamic fog-harvesting and efficient water storage. Therefore, this work enhances the understanding of the fundamental research and provides valuable strategy for design and preparation of sustainable and efficient fog harvesting systems.

Biodegradable, breathable, flexible, and electrically modulated all-leaf capacitive electronic skin for gesture recognition and human motion monitoring

Biodegradable, flexible, non-toxic, and disposable electronic skins (e-skins) are drawing significant attention because of their biocompatibility, outstanding sensitivity, affordability, environmental friendliness, and widespread applications in wearable electronics and flexible sensing platforms. In this study, we develop a biodegradable, disposable, sensitive, breathable, and electrically modulated e-skin using all-leaf electrodes and a dielectric layer, which is fabricated by sandwiching a dried leaf between oxidized single-walled carbon nanotube (Ox-SWCNT)/silver nanowire (AgNW)-coated leaf skeletons. As an unexplored strategy, the use of Ox-SWCNTs with ultra-high thermal conductivity improves thermal stability and conductivity in AgNW-based electrodes (4–5 times). In addition, by adjusting the concentration of Ox-SWCNTs and optimizing electrical characteristics, electrical conductivity and current pathways are tuned to modulate the sheet resistance and electrical conductivity of all-leaf electrodes. The disposable e-skin exhibits a wide sensing range (0.01–97 kPa), high sensitivity (0.86 ± 0.16 kPa−1), low limit of detection (∼10 Pa), linearity for low- and high-pressure regimes, flexibility, breathability, and biodegradability. Furthermore, the developed e-skin exhibits a wide range of sensing performances in measuring human physiological signals, exhalation, vocal cord vibrations, and motion monitoring.

Cover Picture and Issue Information

Journal of Applied EcologyVolume 60, Issue 5 p. 751-753 COVER PICTURE AND ISSUE INFORMATIONFree Access Cover Picture and Issue Information First published: 02 May 2023 https://doi.org/10.1111/1365-2664.14210AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Graphical Abstract Cover image: The leaf skeletonizer Leucoplema dohertyi feeding on a coff ee leaf in the native range of Arabica coff ee in Southwestern Ethiopia. (pp. 911-922). Photo: © Ayco Tack. Volume60, Issue5May 2023Pages 751-753 RelatedInformation

Management intensity affects insect pests and natural pest control on Arabica coffee in its native range

Abstract Agroforestry systems provide opportunities to reduce the trade‐off between agricultural production and biodiversity, for example by enhancing a diverse community of species potentially acting as natural pest control agents. While management of agroforestry systems is intensifying across the globe, we lack insights into the impact of management intensity on pest levels and natural pest control, especially along broad management gradients and as compared with natural forests. We assessed the impact of management intensity on major insect pests (the coffee blotch miner, the serpentine leaf miner, the coffee leaf skeletonizer and damage by other free‐feeding herbivores) and natural pest control by parasitoid wasps across sixty sites in the centre of origin of Arabica coffee in southwestern Ethiopia. Within this region, coffee is growing along a broad management gradient ranging from little or no management in the natural forest to intensively managed commercial plantations. In the wet season, pest levels were largely similar in the natural forest, semi‐forest and semi‐plantation systems, whereas pests reached outbreak densities in the most intensively managed plantation system. In contrast, management intensity did not significantly affect pest levels in the dry season. The insect pests differed in their seasonal dynamics, consistently declined with elevation and were largely unaffected by shade levels. Parasitism rate of the coffee blotch miner was lower, and the parasitoid community was distinct, in the most intensively managed plantation system. Synthesis and applications : Our findings support the hypothesis that the weaker top‐down control by parasitoids in the intensively managed plantation sites leads to higher pest levels, and that—at least for some pest species—there is a threshold in the effect of management intensity on pest levels and natural pest control. It is important to consider such non‐linear relationships to maintain or enhance the sustainability of agroforestry systems during management intensification. Overall, our findings highlight that ecological knowledge of natural pest control can be used to intensify production to improve the livelihood of smallholder farmers without jeopardizing natural pest control but only up to a certain point where it starts to deteriorate.

Hyperelastic Graphene Aerogels Reinforced by In-suit Welding Polyimide Nano Fiber with Leaf Skeleton Structure and Adjustable Thermal Conductivity for Morphology and Temperature Sensing

Hyperelastic Graphene Aerogels Reinforced by In-suit Welding Polyimide Nano Fiber with Leaf Skeleton Structure and Adjustable Thermal Conductivity for Morphology and Temperature Sensing

Leaf-Counting in Monocot Plants Using Deep Regression Models.

Leaf numbers are vital in estimating the yield of crops. Traditional manual leaf-counting is tedious, costly, and an enormous job. Recent convolutional neural network-based approaches achieve promising results for rosette plants. However, there is a lack of effective solutions to tackle leaf counting for monocot plants, such as sorghum and maize. The existing approaches often require substantial training datasets and annotations, thus incurring significant overheads for labeling. Moreover, these approaches can easily fail when leaf structures are occluded in images. To address these issues, we present a new deep neural network-based method that does not require any effort to label leaf structures explicitly and achieves superior performance even with severe leaf occlusions in images. Our method extracts leaf skeletons to gain more topological information and applies augmentation to enhance structural variety in the original images. Then, we feed the combination of original images, derived skeletons, and augmentations into a regression model, transferred from Inception-Resnet-V2, for leaf-counting. We find that leaf tips are important in our regression model through an input modification method and a Grad-CAM method. The superiority of the proposed method is validated via comparison with the existing approaches conducted on a similar dataset. The results show that our method does not only improve the accuracy of leaf-counting, with overlaps and occlusions, but also lower the training cost, with fewer annotations compared to the previous state-of-the-art approaches.The robustness of the proposed method against the noise effect is also verified by removing the environmental noises during the image preprocessing and reducing the effect of the noises introduced by skeletonization, with satisfactory outcomes.

Breathable, Flexible, Transparent, Hydrophobic, and Biotic Sustainable Electrodes for Heating and Biopotential Signal Measurement Applications

Pressure to reduce the global amount of e‐waste has increased in recent years. The optimal use of natural resources is a demanding area especially due to the overabundance of the use of resources and challenges with after‐life disposal. Herein, an easy method is developed to fabricate an improved version of leaf skeleton‐based biodegradable, transparent, flexible, and hydrophobic electrodes. A fractal‐like rubber leaf skeleton is used as the substrate, physical vapor deposited Au interlayer to promote adhesion, and uniform deposition of overlayer silver nanowires. The fabricated surfaces present a high level of electrical stability, optical transparency, hydrophobicity, and robust mechanical properties. The prepared electrodes demonstrate a comparable level of optical transmittance to the virgin leaf skeleton. The mechanical sturdiness of the electrodes is verified by 1k bending cycles. To demonstrate the functionality of these hybrid biotic conductive network (HBCN) electrodes, their performance is evaluated as flexible transparent heating elements and as biosignal measurement electrodes. The heater can reach a temperature of 140 °C with only 2.5 V in ≈5 s and Ag nanowire loading of ≈160 μg cm−2. Likewise, electrocardiogram (ECG) and electromyogram (EMG) signals are successfully obtained from the electrodes without using any electrode gel or other electrolytes.

A deep learning generative model approach for image synthesis of plant leaves.

A well-known drawback to the implementation of Convolutional Neural Networks (CNNs) for image-recognition is the intensive annotation effort for large enough training dataset, that can become prohibitive in several applications. In this study we focus on applications in the agricultural domain and we implement Deep Learning (DL) techniques for the automatic generation of meaningful synthetic images of plant leaves, which can be used as a virtually unlimited dataset to train or validate specialized CNN models or other image-recognition algorithms. Following an approach based on DL generative models, we introduce a Leaf-to-Leaf Translation (L2L) algorithm, able to produce collections of novel synthetic images in two steps: first, a residual variational autoencoder architecture is used to generate novel synthetic leaf skeletons geometry, starting from binarized skeletons obtained from real leaf images. Second, a translation via Pix2pix framework based on conditional generator adversarial networks (cGANs) reproduces the color distribution of the leaf surface, by preserving the underneath venation pattern and leaf shape. The L2L algorithm generates synthetic images of leaves with meaningful and realistic appearance, indicating that it can significantly contribute to expand a small dataset of real images. The performance was assessed qualitatively and quantitatively, by employing a DL anomaly detection strategy which quantifies the anomaly degree of synthetic leaves with respect to real samples. Finally, as an illustrative example, the proposed L2L algorithm was used for generating a set of synthetic images of healthy end diseased cucumber leaves aimed at training a CNN model for automatic detection of disease symptoms. Generative DL approaches have the potential to be a new paradigm to provide low-cost meaningful synthetic samples. Our focus was to dispose of synthetic leaves images for smart agriculture applications but, more in general, they can serve for all computer-aided applications which require the representation of vegetation. The present L2L approach represents a step towards this goal, being able to generate synthetic samples with a relevant qualitative and quantitative resemblance to real leaves.

Fractal-like Hierarchical CuO Nano/Microstructures for Large-Surface-to-Volume-Ratio Dip Catalysts

Dip catalysts are attracting interest in both academia and industry for catalyzing important chemical reactions. These provide excellent stability, better recoverability, recyclability, and easy scale-up. Using the unique microstructures of leaf skeletons, we present a fractal-like hierarchical surface that can be used as a versatile and efficient dip catalyst. Copper oxide microcactuses with nanoscalar features were fabricated onto the Bauhinia racemosa leaf skeletons via a combination of physical vapor deposition, electroplating, and chemical oxidation methods. The coated leaf skeletons have a very high surface area, and the three-dimensional (3D) morphology allows the reactants to encounter the catalytic sites efficiently and move around the reaction mixture swiftly. The fabricated bioinspired leaf skeleton-based dip catalyst was characterized and demonstrated to be very efficient for alcohol dehydrogenation reaction, examined under different experimental conditions. A ceramic 3D-printed catalyst holder was designed to hold the catalysts to avoid any damage caused by the magnetic bars during the reactions. The performance is determined using the reaction yields, and the efficiencies are correlated with microcactus-like structures composed of CuO and the 3D fractal-like shape provided by the leaf skeleton. This strategy can be applied to fabricate other dip catalysts using different materials and designs, suitable for catalyzing numerous other chemical reactions.

High-speed optical resolution photoacoustic microscopy with MEMS scanner using a novel and simple distortion correction method

Optical resolution photoacoustic microscopy (OR-PAM) is a remarkable biomedical imaging technique that can selectively visualize microtissues with optical-dependent high resolution. However, traditional OR-PAM using mechanical stages provides slow imaging speed, making it difficult to biologically interpret in vivo tissue. In this study, we developed a high-speed OR-PAM using a recently commercialized MEMS mirror. This system (MEMS-OR-PAM) consists of a 1-axis MEMS mirror and a mechanical stage. Furthermore, this study proposes a novel calibration method that quickly removes the spatial distortion caused by fast MEMS scanning. The proposed calibration method can easily correct distortions caused by both the scan geometry of the MEMS mirror and its nonlinear motion by running an image sequence only once using a ruler target. The combination of MEMS-OR-PAM and distortion correction method was verified using three experiments: (1) leaf skeleton phantom imaging to test the distortion correction efficacy; (2) spatial resolution and depth of field (DOF) measurement for system performance; (3) in-vivo finger capillary imaging to verify their biomedical use. The results showed that the combination could achieve a high-speed (32 s in 2 × 4 mm) and high lateral resolution (~ 6 µm) imaging capability and precisely visualize the circulating structure of the finger capillaries.

Multi-angle data acquisition to compensate transducer finite size in photoacoustic tomography

In photoacoustic tomography (PAT) systems, the tangential resolution decreases due to the finite size of the transducer as the off-center distance increases. To address this problem, we propose a multi-angle detection approach in which the transducer used for data acquisition rotates around its center (with specific angles) as well as around the scanning center. The angles are calculated based on the central frequency and diameter of the transducer and the radius of the region-of-interest (ROI). Simulations with point-like absorbers (for point-spread-function evaluation) and a vasculature phantom (for quality assessment), and experiments with ten 0.5 mm-diameter pencil leads and a leaf skeleton phantom are used for evaluation of the proposed approach. The results show that a location-independent tangential resolution is achieved with 150 spatial sampling and central rotations with angles of ±8°/±16°. With further developments, the proposed detection strategy can replace the conventional detection (rotating a transducer around ROI) in PAT.

Three-Dimensional Wheat Modelling Based on Leaf Morphological Features and Mesh Deformation

The three-dimensional (3D) morphological structure of wheat directly reflects the interrelationship among genetics, environments, and cropping systems. However, the morphological complexity of wheat limits its rapid and accurate 3D modelling. We have developed a 3D wheat modelling method that is based on the progression from skeletons to mesh models. Firstly, we identified five morphological parameters that describe the 3D leaf features of wheat from amounts of 3D leaf digitizing data at the grain filling stage. The template samples were selected based on the similarity between the input leaf skeleton and leaf templates in the constructed wheat leaf database. The leaf modelling was then performed using the as-rigid-as-possible (ARAP) mesh deformation method. We found that 3D wheat modelling at the individual leaf level, leaf group, and individual plant scales can be achieved. Compared with directly acquiring 3D digitizing data for 3D modelling, it saves 79.9% of the time. The minimum correlation R2 of the extracted morphological leaf parameters between using the measured data and 3D model by this method was 0.91 and the maximum RMSE was 0.03, implying that this method preserves the morphological leaf features. The proposed method provides a strong foundation for further morphological phenotype extraction, functional–structural analysis, and virtual reality applications in wheat plants. Overall, we provide a new 3D modelling method for complex plants.

Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm.

By considering the line pattern of acoustic-resolution photoacoustic microscopy (AR-PAM) vessel images, we develop modified algorithms for synthetic aperture focusing technique (SAFT) and deconvolution based on a directional approach to enhance images. The modified algorithms consist of Fourier accumulation SAFT (FA-SAFT) and directional model-based (D-MB) deconvolution. To evaluate the performance of our algorithms, we conduct a series of imaging experiments and apply our algorithms, and existing SAFT and deconvolution algorithms are also applied for side-by-side comparison. By imaging tungsten wire phantom, our algorithms enable full width at half maximum of 26 - 31 µm over depth of focus of 1.8 mm and minimum resolvable distance of 46 - 49 µm, besting existing SAFT and deconvolution algorithms. Imaging of leaf skeleton phantom and in vivo imaging of mouse blood vessels also prove that our algorithm is capable of providing high-resolution, high-signal-to-noise ratio, and good-fidelity results for complex structures and for in vivo applications, especially for the images with the line pattern. The proposed directional approach can not only be used in AR-PAM but also in other imaging modalities to deal with the line pattern, such as FA-SAFT for ultrasound imaging and D-MB deconvolution for optical coherence tomography angiography.