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In this study, a novel method for target identification in a foliage environment is presented. This method is based on the ultra wideband (UWB) wireless sensor networks (WSNs) model, and the foliage environment is specially considered. The data used to identify the targets are derived from the received signal waveform, so most existing transceivers can be exploited as detecting sensors, which leads to a potential low-cost way to identify targets during the normal communications within the WSNs under foliage environment. The selected bispectra algorithm is applied to extract the feature vector, and chaos particle swarm optimisation-based support vector machine is used as the target classifier. Experiments with real-world data samples indicate that this method has an excellent classification performance in a foliage environment. Moreover, this method shows potential for online training.

Potato early blight and late blight are devastating diseases that affect potato planting and production. Thus, precise diagnosis of the diseases is critical in treatment application and management of potato farm. However, traditional computer vision technology and pattern recognition methods have certain limitations in the detection of crop diseases. In recent years, the development of deep learning technology and convolutional neural networks has provided new solutions for the rapid and accurate detection of crop diseases. In this study, an integrated framework that combines instance segmentation model, classification model, and semantic segmentation model was devised to realize the segmentation and detection of potato foliage diseases in complex backgrounds. In the first stage, Mask R-CNN was adopted to segment potato leaves in complex backgrounds. In the second stage, VGG16, ResNet50, and InceptionV3 classification models were employed to classify potato leaves. In the third stage, UNet, PSPNet, and DeepLabV3+ semantic segmentation models were applied to divide potato leaves. Finally, the three-stage models were combined to segment and detect the potato leaf diseases. According to the experimental results, the average precision (AP) obtained by the Mask R-CNN network in the first stage was 81.87%, and the precision was 97.13%. At the same time, the accuracy of the classification model in the second stage was 95.33%. The mean intersection over union (MIoU) of the semantic segmentation model in the third stage was 89.91%, and the mean pixel accuracy (MPA) was 94.24%. In short, it not only provides a new model framework for the identification and detection of potato foliage diseases in natural environment, but also lays a theoretical basis for potato disease assessment and classification.

Human health risks caused by PM 2.5 raise awareness to the role of trees as bio-filters of urban air pollution, but not all species are equally capable of filtering the air. The objectives of this current study were: (1) to determine the foliar traits for effective PM 2.5 -capture and (2) explore species-to-species differences in foliar PM 2.5 -recapture capacity following a rain event. The study concluded that overall, the acicular needle shape made conifers more efficient with PM 2.5 accumulation and post-rainfall recapture than broadleaved species. The foliar shape and venation of broadleaved species did not appear to influence the PM 2.5 accumulation. However, the number of the grooves and trichomes of broadleaved species were positively related to foliar PM 2.5 accumulation, suggesting that they could be used as indicators for the effectiveness of tree PM 2.5 capture. Furthermore, the amount of PM 2.5 removal by rainfall was determined by the total foliar PM 2.5 . Not all PM 2.5 remained on the foliage. In some species, PM 2.5 was resuspended during the growing season, and thus reduced the net particular accumulation for that species. These findings contribute to a better understanding of tree species potential for reducing PM 2.5 in urban environments.

Due to climate change, natural hazards have increased around the world. One of these hazards is dust storms, which cause problems for people in arid and semi-arid regions. The inherent properties of dust particles can affect the atmospheric, oceanic, and continental climate systems. The leaf surface of plants constantly absorbs particulate matter, which helps to improve air quality. However, plants can also be affected by the accumulation of particulate matter. This article reviews research on how dust affects the morphological, physiological, and biochemical properties of tree leaves. The ability of vegetation to capture and retain atmospheric particulate matter depends directly on the interactions between particulate matter and plant surfaces. Atmospheric dust places additional stress on plants because they often respond to atmospheric pollution in a manner similar to drought and other environmental stresses. However, the extent to which leaf properties are affected by particulate matter is still controversial. Dust impacts on morpho-anatomical characteristics of the leaf. Young leaves with soft tissues are more vulnerable than rigid leaves. High-trichome leaf can keep more dust causing necrosis and chlorosis on the leaf. Fine particles with sizes of about 2.5 μm can penetrate inside the leaves’ tissues through the stomata leading to the degradation of chloroplast and pigments. With reduction in plant photosynthesis, a change will happen in wood cellular features by affecting the cambium. The lack of basic information on changes in plant structure, as well as the role dust plays in life cycles, is a challenge for developing management protocols and research plans on this problem.

Neonicotinoid pesticides were first introduced in the mid-1990s, and since then, their use has grown rapidly. They are now the most widely used class of insecticides in the world, with the majority of applications coming from seed dressings. Neonicotinoids are water-soluble, and so can be taken up by a developing plant and can be found inside vascular tissues and foliage, providing protection against herbivorous insects. However, only approximately 5% of the neonicotinoid active ingredient is taken up by crop plants and most instead disperses into the wider environment. Since the mid-2000s, several studies raised concerns that neonicotinoids may be having a negative effect on non-target organisms, in particular on honeybees and bumblebees. In response to these studies, the European Food Safety Authority (EFSA) was commissioned to produce risk assessments for the use of clothianidin, imidacloprid and thiamethoxam and their impact on bees. These risk assessments concluded that the use of these compounds on certain flowering crops poses a high risk to bees. On the basis of these findings, the European Union adopted a partial ban on these substances in May 2013. The purpose of the present paper is to collate and summarise scientific evidence published since 2013 that investigates the impact of neonicotinoids on non-target organisms. Whilst much of the recent work has focused on the impact of neonicotinoids on bees, a growing body of evidence demonstrates that persistent, low levels of neonicotinoids can have negative impacts on a wide range of free-living organisms.

Anthocyanins are key pigments of plants, providing color to flowers, fruit, and foliage and helping to counter the harmful effects of environmental stresses. It is generally assumed that anthocyanin biosynthesis arose during the evolutionary transition of plants from aquatic to land environments. Liverworts, which may be the closest living relatives to the first land plants, have been reported to produce red cell wall-bound riccionidin pigments in response to stresses such as UV-B light, drought, and nutrient deprivation, and these have been proposed to correspond to the first anthocyanidins present in early land plant ancestors. Taking advantage of the liverwort model species Marchantia polymorpha, we show that the red pigments of Marchantia are formed by a phenylpropanoid biosynthetic branch distinct from that leading to anthocyanins. They constitute a previously unreported flavonoid class, for which we propose the name “auronidin,” with similar colors as anthocyanin but different chemistry, including strong fluorescence. Auronidins might contribute to the remarkable ability of liverworts to survive in extreme environments on land, and their discovery calls into question the possible pigment status of the first land plants.

Aim We examined whether the available surface area, temperature, or habitat complexity (foliage height diversity index) determine species richness of birds (and species richness of individual feeding guilds) along a complete forest elevational gradient. Further, we focused on the relationship between species richness of insectivorous birds and the availability of their food resources. Location Elevational gradient (200–3,700 m) of Mt Wilhelm (4,509 m a.s.l.), Central Range, Papua New Guinea. Taxon Birds. Methods We collected data on bird communities at eight sites (500 m elevational increment) during three surveys encompassing both dry and wet seasons over a 2‐year period. We used point counts, mist‐netting, and random walks throughout a standardized area. We tested three predictors of diversity and all of their combinations, in conjunction with sensitivity analyses for spatial effects. Habitat complexity (foliage height diversity index) and temperature were locally measured; surface area available within 200 m elevational intervals was obtained using GIS software. We further locally surveyed insect biomass and related it to species richness of insectivorous birds. Results Birds displayed a monotonic decline in species richness (from 113 to 35 bird species) with increasing elevation, and a nested pattern of species loss. The observed patterns were best explained by habitat complexity for the insectivores, frugivore‐insectivores, and total number of bird species. The available surface area was the best predictor for frugivorous birds. The mean temperature had a high correlation with species richness of all birds and gave the best fit of species richness for insectivore‐nectarivores and pure nectarivores. The biomass of insectivorous birds correlated with the biomass of arthropods. We ruled out the possibility that the elevational pattern observed in birds could be driven by a single phylogenetic radiation. Main conclusions We observed species richness patterns correlate well with habitat complexity and mean temperature, but mean temperature was not ranked as high as expected. Our results thus challenge the generally expected high importance of temperature as a regulator of water availability, production, and biochemical process that influence species richness, and underscore the importance of vegetation structure and the food resources as the driver of observed species richness.

1. Terrestrial laser scanning (TLS) captures the three-dimensional structure of habitats. Compared to traditional methods of forest mensuration, it allows quantification of structure at increased resolution, and the derivation of novel metrics with which to inform ecological studies and habitat management. 2. Lowland woodlands in the UK have altered in structure over the last century due to increased abundance of deer and a decline in management. We compared whole-canopy profiles between woodlands with high (>10 deer km⁻²) and low deer density (c. 1 deer km⁻²), and in stands with and without records of management interventions in the last 20 years, providing a test case for the application of TLS in habitat assessment for conservation and management. 3. Forty closed-canopy lowland woodlands (height range 16·5-29·4 m) were surveyed using TLS in two regions of the UK, divided into areas of high- and low-deer abundance, and between plots which had been recently managed or were unmanaged. Three-dimensional reconstructions of the woodlands were created to document the density of foliage and stem material across the entire vertical span of the canopy. 4. There was a 68% lower density of understorey foliage (0·5-2 m above-ground) in high-deer woodlands, consistent in both regions. Despite this, total amounts of foliage detected across the full canopy did not differ between deer density levels. High-deer sites were 5 m taller overall and differed in the distribution of foliage across their vertical profile. Managed woodlands, in contrast, exhibited relatively minor differences from controls, including a lower quantity of stem material at heights from 2 to 5 m, but no difference in foliage density. All main effects were replicated equally in both regions despite notable differences in stand structures between them. 5. Synthesis and applications. Terrestrial laser scanning allows ecologists to move beyond two-dimensional measures of vegetation structure and quantify patterns across complex, heterogeneous, three-dimensional habitats. Our findings suggest that reduction of deer populations is likely to have a strong impact on woodland structures and aid in restoring the complex understorey habitats required by many birds, whereas management interventions as currently practiced have limited and inconsistent effects.

A number of eye-tracking studies have shown that viewing natural environments is associated with reduced eye movement activity compared with viewing built environments. This has been linked to the cognitive benefits of viewing nature and explained in terms of Kaplan and Kaplan’s Attention Restoration Theory. However, the theory has recently been criticized for the lack of empirical evidence supporting its framework. The first aim was to replicate the results of previous eye movement studies using different visual stimuli. In addition, we investigated whether reduced eye movements when viewing natural versus urban images could be explained by greater perceptual fluency and fractal complexity of the images. The participants ( N  = 66) viewed images of forests with and without foliage and images of urban apartment buildings while their eye movements were recorded. The self-reported perceptual fluency and fractal complexity of the presented images were measured. Analysis of eye movements revealed significantly less eye movement activity (a reduced number of fixations that are longer) when viewing natural images than urban ones, consistent with previous findings. There was no significant difference between images containing foliage and those without for any of the measured variables. However, mediation analysis did not show significant effects of perceptual fluency on eye fixations. Moreover, while previous research suggests that fractal structure may be one of the mechanisms underlying perceptual fluency, mediation analysis did not reveal any significant effects of fractal complexity on eye fixations. This raises the question of what causes the differences in eye movement patterns and the restorative effects of nature versus urban scenes. Further research should address the specific spatio-cognitive dimensions of natural images and the individual differences that may affect how people move their eyes when processing different types of scenes.

Tropical forest canopies are comprised of tree crowns of multiple species varying in shape and height, and ground inventories do not usually reliably describe their structure. Airborne laser scanning data can be used to characterize these individual crowns, but analytical tools developed for boreal or temperate forests may require to be adjusted before they can be applied to tropical environments. Therefore, we compared results from six different segmentation methods applied to six plots (39 ha) from a study site in French Guiana. We measured the overlap of automatically segmented crowns projection with selected crowns manually delineated on high-resolution photography. We also evaluated the goodness of fit following automatic matching with field inventory data using a model linking tree diameter to tree crown width. The different methods tested in this benchmark segmented highly different numbers of crowns having different characteristics. Segmentation methods based on the point cloud (AMS3D and Graph-Cut) globally outperformed methods based on the Canopy Height Models, especially for small crowns; the AMS3D method outperformed the other methods tested for the overlap analysis, and AMS3D and Graph-Cut performed the best for the automatic matching validation. Nevertheless, other methods based on the Canopy Height Model performed better for very large emergent crowns. The dense foliage of tropical moist forests prevents sufficient point densities in the understory to segment subcanopy trees accurately, regardless of the segmentation method.