Vegetation Types Research Articles

Characterizing Soil and Bedrock Water Use of Native California Vegetation

The effective characterization of landscape water balance components—evapotranspiration, runoff, recharge, and soil storage—is critical for understanding the integrated effects of the water balance on vegetation dynamics, water availability, and associated environmental responses to climate change. An improved parameterization of these components can improve assessments of landscape stress and provide useful insights for predicting and managing vegetation responses to climate change. Hydrology models typically are not able to address water availability below the mapped soil profile, but we refined a landscape hydrology model, the Basin Characterization Model, by balancing measures of actual evapotranspiration (AET) with modeled subsurface soil water holding capacity, including bedrock storage. The purpose of this study was to characterize the effective rooting depth (the depth of soil and bedrock storage required to support AET) for 35 native vegetation types in California in order to quantify soil and bedrock water use, which ranged from 0 to 3.1 m for most vegetation types, exceeding mapped soil depths. This resulted in the quantification of bedrock water use, increasing available water 67% over that calculated by mapped soils alone. We found that mid-elevation vegetation types with lower water and energy limitations have the highest evapotranspiration rates and deepest effective rooting depth. We also evaluated the resilience to drought with this more spatially realistic characterization of water and vegetation interactions.

Species Diversity and Food Potential of Orangutans in Genting Tanah Village Forest

The aim of the research is to determine the diversity of vegetation in the forests of Genting Tanah Village which has the potential to feed orangutans (Pongo pygmaeus morio). The research method is an inventory of sample plots. Data were analyzed quantitatively by calculating the Relative Density, Relative Frequency, Relative Dominance and Diversity Index values. The choice of orangutan food is determined by comparing it based on literature regarding orangutan food. The research identified 67 types of vegetation belonging to 44 families. At tree level there are 20 types of vegetation, with Shorea teysmanniana Dyer being the most important with an Importance Value Index of 78.06%. At pole level there are 32 species, with Combretocarpus rotundatus Miq. has the highest Important Value Index of 74.43%. Genting Tanah village forest also has 40 species of saplings and 41 species of undergrowth. Mallotus muticus Mull. has the highest Importance Value Index of 55.78% in the sapling group, while Hypolytrum nemorum Vahl has the highest Importance Value Index of 21.24% in the seedling and undergrowth group. The species diversity index at the seedling level of 3.24 is considered high, while the species richness index is low at the tree level and moderate at the pole level. Based on the 67 species identified, there are 34 species that have the potential to be a source of orangutan’s food.

Attica: A Hot Spot for Forest Fires in Greece

(1) Background: Forest fires are widespread in Mediterranean-climate regions and are becoming very common in urban and peri-urban areas. (2) Methods: Wildfires in Attica since 1977 are mapped and types of vegetation burned are reported. (3) Results: Fires are becoming larger. During the period of study (1977–2024), 45% of the burned area was covered with Pinus halepensis forests, 1.4% with Abies cephalonica forests, and 18.5% with shrublands. A relatively high percentage of the burned area (BA) affected more than once consisted of pine forests (65%). Ten percent of the total BA lies within the boundaries of the Natura 2000 network, Europe’s most important network of protected areas, of which 38.9% was burned. At the interannual scale, the BA in Attica is negatively correlated with relative humidity, while reduced precipitation may contribute to the expansion of wildfires. (4) Conclusions: Fires are becoming larger over time, with low humidity increasing the higher fire risk. Since the changing climate is expected to create more severe and uncontrollable conditions, mitigation and adaptation measures should be planned and be introduced immediately.

Quantitative assessment of the relative impacts of climate change and human activities on net primary productivity of vegetation in the Beijing-Tianjin-Hebei region

IntroductionAs an important part of terrestrial ecosystem, vegetation plays a vital role in the terrestrial carbon cycle. As one of China’s three major urban clusters, the Beijing-Tianjin-Hebei region has experienced significant changes in vegetation due to climate change and large-scale ecological restoration efforts. Climate change and human activities are two factors that have important effects on the carbon cycles of terrestrial ecosystems. Quantitative assessment of the relative effects of climate change and human activities on vegetation dynamics is of paramount importance for regional sustainable development and ecological security.MethodsIn this study, based on ground meteorological data and remote sensing data including NDVI, an improved Carnegie-Ames-Stanford Approach model was employed to estimate the net primary productivity (NPP) of vegetation in the region from 2000 to 2020. Utilizing methods such as trend analysis, Mann-Kendall significance test, and correlation analysis, we investigated the spatiotemporal patterns of vegetation NPP and its correlations with precipitation, temperature, and solar radiation. Furthermore, the partial derivative trend residuals method was adopted to separate and quantify the impacts of climate change and human activities on vegetation NPP in different years.ResultsThe results show that: (1) from 2000 to 2020, the overall trend of vegetation NPP in the Beijing-Tianjin-Hebei region exhibited an increasing trend, with a spatial distribution pattern showing a step-like distribution from low to high to low from northwest to southeast. The multi-year average NPP was 384.90 g Cm−2, with an annual average growth rate of 3.00 g Cm−2. The NPP values of different vegetation types from large to small were: forests (578.40 g C m−2), shrubland (386.57 g C m−2), grassland (380.17 g C m−2), and cropland (324.91 g C m−2). (2) The proportion of regions with a positive correlation coefficient between average temperature and vegetation NPP in the Beijing-Tianjin-Hebei region from 2000 to 2020 was 69.85%, while the proportions of regions with positive correlations between precipitation and solar radiation with vegetation NPP were 92.1% and 89.19%, respectively. This indicates that vegetation NPP in most regions is positively correlated with precipitation, solar radiation, and temperature, with precipitation exerting a greater influence on vegetation NPP than temperature and solar radiation. (3) Vegetation improvement in the Beijing-Tianjin-Hebei region is mainly driven by both climatic factors and human activities, while vegetation degradation is primarily caused by human factors. The relative contributions of climate change and human activities to changes in vegetation NPP in the study area are 45.11% and 54.89%, respectively. The areas where they positively contribute account for 48.64% and 73.95% of the total study area, respectively, with the proportions of areas with relative contribution rates >60% being 45.86% and 28.86%, respectively.DiscussionThe findings of this study can provide scientific basis for vegetation restoration, low-carbon development, and ecosystem management decision-making in the Beijing-Tianjin-Hebei region.

Impact of vegetation type on the content and spectroscopic properties of soil organic matter in the subalpine zone of the Bieszczady Mountains (Eastern Carpathians)

Impact of vegetation type on the content and spectroscopic properties of soil organic matter in the subalpine zone of the Bieszczady Mountains (Eastern Carpathians)

A Novel Workflow for Mapping Forest Canopy Height by Synergizing ICESat-2 and Multi-Sensor Data

Precise information on forest canopy height (FCH) is critical for forest carbon stocks estimation and management, but mapping continuous FCH with satellite data at regional scale is still a challenge. By fusing ICESat-2, Sentinel-1/2 images and ancillary data, this study aimed to develop a workflow to obtain an FCH map using a machine learning algorithm over large areas. The vegetation-type map was initially produced by a phenology-based spectral feature selection method. A forest characteristic-based model was then proposed to map spatially continuous FCH after a multivariate quality control. Our results show that the overall accuracy (OA) and average F1 Score (F1) for eight main vegetation types were more than 90% and 89%, respectively, and the vegetation-type map agreed well with the census areas. The forest characteristic-based model demonstrated a greater potential in FCH prediction, with an R-value 60.47% greater than the traditional single model, suggesting that the addition of the multivariate quality control and forest structure characteristics could positively contribute to the prediction of FCH. We generated a 30 m continuous FCH map by the forest characteristic-based model and evaluated the product with about 35 km2 of airborne laser scanning (ALS) validation data (R = 0.73, RMSE = 2.99 m), which were 45.34% more precise than the China FCH, 2019. These findings demonstrate the potential of our proposed workflow for monitoring regional continuous FCH, and will greatly benefit accurate forest resources assessment.

Greening the arid: the impact of urban vegetation on the outdoor thermal comfort in hot and dry city

This article examines the impact of urban vegetation on outdoor thermal comfort in the hot and dry city of Biskra, Algeria. The study employs the ENVI-met model to simulate various vegetation arrangement scenarios and evaluate their effectiveness in improving microclimate parameters and thermal comfort. The results show that the scenario with the densest and most extensive vegetation cover (90% of the sample area) provided the greatest reduction in air temperature, mean radiant temperature, and duration of severe thermal stress, compared to the baseline scenario with minimal vegetation. However, even the best-performing scenario did not achieve a thermally comfortable outdoor environment, likely due to the heat-absorbing properties of the asphalt ground and the reduced wind speeds caused by the dense vegetation. The study highlights the importance of considering vegetation type, cover density, and strategic placement in urban planning to mitigate the harsh climatic conditions in hot and dry cities like Biskra.

Simulating the Vegetation Gross Primary Productivity by the Biome-BGC Model in the Yellow River Basin of China

In terrestrial ecosystems, the quantification of carbon absorption is primarily represented by the gross primary productivity (GPP), which signifies the initial substances and energy acquired by the ecosystem. The GPP also serves as the foundation for the carbon cycle within the entire terrestrial ecosystem. The Biome-BGC model is a widely used biogeochemical process model for simulating the stocks and fluxes of water, carbon, and nitrogen between ecosystems and the atmosphere. However, it is the abundance of eco-physiological parameters that lead to challenges in calibrating the model. The parameter optimization method of coupling the differential evolution algorithm (DE) with the Biome-BGC model was used to calibrate and validate the eco-physiological parameters of the seven typical vegetation types in the Yellow River Basin (YRB). And then we used the calibrated parameters to simulate the GPP by way of grid-based simulation. Finally, we conducted model adaptability testing and spatiotemporal analysis of GPP variations in the YRB. The results of the validation (R2, RMSE) were: temperate grasses (0.94, 24.33 g C m−2), alpine meadows (0.94, 18.13 g C m−2), shrubs (0.94, 29.20 g C m−2), evergreen needle leaf forests (0.96, 27.88 g C m−2), deciduous broad leaf forests (0.94, 32.09 g C m−2), one crop a year (0.96, 16.19 g C m−2), and two crops a year (0.90, 38.15 g C m−2). After adaptability testing, the average R2 value between the simulated GPP values and the GPP product values in the YRB was 0.85, and the average RMSE value was as low as 50.92 g C m−2. Overall, the model exhibited strong simulation accuracy. Therefore, after calibrating the model with the DE algorithm, the Biome-BGC model could effectively adapt to the ecologically complex YRB. Moreover, it was able to accurately estimate the GPP, which establishes a foundation for analyzing the spatiotemporal trends of the GPP in the YRB. This study provides a reference for optimizing Biome-BGC model parameters and simulating diverse vegetation types on a large scale.

Assessing Carbon Sequestration and Biomass Distribution across Diverse Land Use Types in Ban Krang Subdistrict, Phitsanulok Province

This study investigates carbon dioxide (CO₂) sequestration and biomass distribution across various plant components and land use types in Ban Krang Subdistrict, Mueang District, Phitsanulok Province, with the goal of enhancing carbon management strategies. Field surveys were conducted using 14 plots of 40x40 meters to quantify biomass and estimate CO₂ sequestration across different vegetation types. The findings reveal an average CO₂ sequestration of 122.81 Ton ha⁻¹, with aboveground biomass, particularly stems, contributing the most to carbon storage. Notably, abandoned perennial crops and mixed perennial crops demonstrated the highest sequestration rates, at 657.94 Ton ha⁻¹ and 613.00 Ton ha⁻¹, respectively. In contrast, agricultural lands such as rice paddies and cassava plantations exhibited the lowest sequestration rates, though rice paddies contributed the highest total CO₂ sequestration, amounting to 61,119.71 Tons, due to their extensive area. The study highlights the critical role of diverse and dense vegetation, particularly perennial crops, in maximizing carbon sequestration. It also underscores the potential for improving carbon storage in agricultural lands through better land management practices. The results suggest that targeted strategies should prioritize high-sequestration land use types while also enhancing carbon storage in low-sequestration areas. By optimizing land use and management practices, the region can significantly increase its carbon storage capacity, contributing to climate change mitigation and promoting long-term ecological sustainability. These insights are crucial for formulating effective carbon management strategies in Ban Krang Subdistrict, as well as in other comparable regions.

Assessing Species Richness with Camera Trap Surveys During Five Years of Large-Scale Mining Disruptions

In the Northern Cape Province of South Africa, an investigation was launched into the impact of large-scale open-pit mining on wildlife ecology and populations, more specifically on the animal species richness and detection rates across different vegetation types. Using camera traps, we monitored a 43,000-hectare area, which included active mining areas and adjacent lands, over a period of five years (2020–2024). Data on 44 animal species ranging from small mammals to large megaherbivores were collected, with a large variation in species richness across the study site being observed. The detection of species that are of conservation concern, such as the vulnerable Temminck’s ground pangolin (Smutsia temminckii) and endangered mountain reedbuck (Redunca fulvorufula), highlighted additional potential risks that mining activities pose to biodiversity in the area, emphasizing the importance of monitoring biodiversity in areas that are impacted by large-scale anthropogenic and mining activities. Furthermore, the results suggest that some areas may require a more targeted approach to conservation in order to mitigate the disruptive effect of mining. Benchmarking the species present and proving the presence of endangered and vulnerable species prove the successful first steps into understanding habitat disruption caused by mining activities and will guide future conservation and management efforts.

Charcoal morphotypes as indicators of fire fuel types and fire events along eight centuries in east‐central Mexico

ABSTRACTThe study of fire and charcoal production in tropical mesophytic ecosystems provides valuable insights into the ecological and climatic history of these systems. The diverse vegetation produces charcoal in various forms and sizes, which influences its dispersion and provides key information about fire dynamics. In this study, we used an approximately 850‐year sedimentary charcoal record extracted from Lake Atezca in east‐central Mexico to reconstruct the history of fire activity, including frequency and intensity. We present innovative information on charcoal morphotypes to interpret fire regimes, identify the types of vegetation burned and analyze human impact. The results reveal a shift in fire regimes during the Spanish Conquest, transitioning from local‐scale burning associated with intensive wood burning for indigenous agriculture to regional‐scale burning characterized by the burning of herbs and grasses linked to the expansion of extensive livestock grazing. This transition is reflected in the decrease of wood charcoal and the increase of smaller particles, as well as in the rise in titanium in the sediments, indicating greater landscape alteration. The evolution of fire regimes in Lake Atezca was driven primarily by human activities since pre‐Hispanic times, although climatic factors, such as severe droughts, may have also influenced fire activity.

Analysis of drought response thresholds and drought-causing factors of central Asian vegetation

Understanding the threshold responses of vegetation to drought is essential for predicting and mitigating drought risk. Across Central Asia, extreme weather is expected to increase by the end of the 21st century. However, the threshold responses of different Central Asian vegetation types are not well understood. Here, we calculated drought indices for Central Asia based on meteorological data and remotely sensed vegetation indices from 2001 to 2020, used trend analysis to assess changes in drought and vegetation in Central Asia over the past 20 years, evaluated the threshold responses of five vegetation types to drought, and explored the drought causative factors of different vegetation types using coexistence analysis. All five Central Asian vegetation types responded to increasing drought in a nonlinear manner, and thus exhibited drought-responsive thresholds. Forests were the first responders to increasing drought, and wetlands were the least responsive overall. Grasslands, cultivated lands, shrublands, and forests crossed the drought threshold in different years between 2001 and 2020, with shrublands exhibiting the largest percentage of area crossing the threshold. Finally, the drought causative factor for grasslands, shrublands, and wetlands was soil moisture; for cultivated lands was precipitation; and for forests was saturated water vapor pressure difference. This study uncovers the characteristic responses of different Central Asian vegetation types to drought, and lays a theoretical foundation for drought assessment and ecological restoration in Central Asia.

Assessing foodborne health risks from dietary exposure to antibiotic resistance genes and opportunistic pathogens in three types of vegetables: An in vitro simulation of gastrointestinal digestion

Assessing foodborne health risks from dietary exposure to antibiotic resistance genes and opportunistic pathogens in three types of vegetables: An in vitro simulation of gastrointestinal digestion

Co-Occurrence Patterns of Aquatic Macroinvertebrates in Laurentian Great Lakes Coastal Wetlands.

In niche-based community assembly theory, it is presumed that communities in habitats with high natural disturbance regimes are less likely to be structured by competitive mechanisms. Laurentian Great Lakes (hereafter Great Lakes) coastal wetlands can experience drastic diel fluctuations in dissolved oxygen levels, severe wave action, ice scour, and near complete freezing during the winter such that conditions are inhospitable for most organisms. The high natural disturbance levels are thought to cause high interannual turnover for aquatic macroinvertebrate communities and support the hypothesis that these communities are less likely to experience less competitive interactions and negative co-occurrence structure. We hypothesize that non-random co-occurrence patterns will be rare in Great Lake coastal wetlands and non-competitive processes (e.g., through shared or differential microhabitat affinities, pollution tolerances, or biotic homogenization) will be more common than competitively driven negative co-occurrence patterns. Null model analysis was performed on 134 macroinvertebrate communities sampled from across the Great Lakes basin from 2000 to 2013. To disentangle the effects of alternative structuring mechanisms (i.e., shared/differential habitat affinities, shared/differential pollution tolerance, and biological homogenization/competitive exclusion), communities were parsed based on the year sampled, the vegetation type from which community samples were collected, and lastly species' functional feeding group assignment or taxonomic group. As expected, very few communities were non-randomly structured; however, all of those that were non-random exhibited showed more negative co-occurrences than by chance. Upon further investigation, these communities consisted of species that are known to overwinter in wetlands, and therefore, avoid having to recolonize after each spring thaw. With expected changes in habitat conditions due to climate change, we propose that null model analyses can be used as an early warning system for community change.

A comparison of country-scale subsoil predictions between a numeric and a taxonomic soil classification system

Traditional soil classification systems are designed to communicate information; however, surveyor biases and tacit knowledge can lead to subjective soil class designations. Consequently, different soil scientists may classify the same soil differently. This becomes a critical issue when mapping soil classes, as there could be multiple interpretations for the same observation. To address this problem, numerical soil classification systems have been developed. However, little is known about how well they compare to taxonomic systems when spatially predicted on a national scale. This study aimed to compare a previously developed, unsupervised numeric classification system and South Africa's taxonomic soil classification system in terms of their spatial predictions across the country. The taxonomic system of South Africa has 19 defined subsoil horizons, which were aggregated into eight horizons and compared to a nine horizon numeric classification as well as South Africa's profile (soil form) classification comprising 73 different soil groupings, which was used as a control. The comparison was conducted from predictions through gradient tree boosting in Google Earth Engine at a 30 m resolution. The numerical system (kappa = 0.30, accuracy = 0.57) exhibited poor spatial predictions, with a kappa 22 % lower and accuracy 2 % lower than the control (kappa = 0.52, accuracy = 59 %). On the other hand, the taxonomic system performed well, with a kappa of 0.57 and an accuracy of 67 %, exhibiting a 5 % increase in kappa and an 8 % increase in accuracy compared to the control. It was hypothesized that the overpredictions of the predominant horizon contributed to the numeric system's poor performance. Nevertheless, both systems showed the highest maximum entropy in arid regions of the Karoo and savannah biomes, albeit in spatially distinct ecoregions. It was thought that the divergence in the two systems' maximum entropy was due to their association with precipitation differences (amount and seasonality) as well as vegetation type and cover (woodlands vs. shrublands). To map the country in more detail, further soil sampling should be conducted in arid regions and optimisation of the predictive algorithm for each soil category should be performed.

Prediction of environmental suitability for Haematoxylum campechianum: a proposal to promote reforestation in Mexico

Models of potential distribution and ecological niche allow to estimate priority areas for the management of species of interest, as well as to identify variables related to environmental suitability and predict the behavior of species in the face of anthropogenic disturbances. Logwood (Haematoxylum campechianum) is a species that we consider a natural heritage in southeastern Mexico because of its ecological role and the current and historical use of its wood. This species is present in different vegetation types and even dominates in some ecosystems called "tintales", which have been exploited for dye extraction in past centuries. This, combined with changes in land use for livestock, agriculture, and other anthropogenic activities, has affected the Logwood´s distribution and connectivity of its populations. We modeled the potential distribution of H. campechianum and evaluated the feasibility of reforestation with this species in southeastern Mexico. For this purpose, we included 638 occurrence points and used the MaxEnt algorithm for modeling, analyzing different parameters. We used the potential distribution model, accessibility to the areas and water availability to calculate a feasibility index for reforestation. The environmental suitability for the species is more present in southeastern Mexico, conditioned by the aridity index, the maximum temperature of the coldest month and the warmest month. The greatest percentage of areas with high feasibility indexes for reforestation are found in Campeche, apparently a key state for the conservation and management of the species. This study can be the starting point for implementing reforestation programs with H. campechianum and can be used as an example to apply to other species with patrimonial value.

Diversity of primary vegetation species of lake shore impacts largely carbon emissions in thermokarst lakes on the Qinghai-Tibet plateau.

Terrestrial organic matter from surrounding primary vegetation is critical for carbon cycling in thermokarst lakes. However, the characteristics and contribution of this vegetation in shaping microbial community and affecting carbon emissions in thermokarst lakes remain poorly understood. This study quantifies the influence of lakeshore primary vegetation characteristics on microbial community and carbon emissions across lakes with different vegetation types on the Qinghai-Tibet Plateau (QTP). Our findings indicate that methane (CH4) diffusion and ebullition emissions increase significantly from alpine desert (AD), alpine steppe (AS), alpine meadow (AM) to swamp meadow (SM). Specifically, diffusion fluxes in lakes averaged 1.7 mmol m‌‌-2 d-1 (ranging from 0.04 to 5.7 mmol m-2 d-1), and ebullition fluxes in lakes averaged 14.5 mmol m-2 d-1 (ranging from 0.18 to 99.1 mmol m-2 d-1). Carbon dioxide (CO2) diffusive emissions in SM, AM, AS were significantly higher than AD, with fluxes ranging from -14.12 mmol m-2 d-1 to 51.33 mmol m-2 d-1. The primary vegetation diversity also showed a significant increase from AD (3.17 ± 1.33), AS (5.33 ± 1.63), AM (4.5 ± 1.05) to SM (9.2 ± 1.40). Lake shore primary vegetation diversity indirectly enhances CH4 and CO2 emissions by altering water pH. Additionally, it influences methane-cycling microorganisms through pH regulation, further promoting CH4 emissions. Greater vegetation diversity also increases CH4 emissions by affecting sediment microbial carbon. These results underscore the importance of integrating vegetation diversity and microbial community patterns into carbon cycling models to improve the predictions of thermokarst lake carbon emissions in a warming and greening future, emphasizing the urgent requirement to protect the pristine environments of permafrost regions.

Assessing the Co-Occurrence of European Pine Marten (Martes martes) With Humans and Domestic Cats on a Mediterranean Island.

Anthropogenic activities often lead to changes in the distribution and behavior of wild species. The mere presence of humans and free-roaming domestic cats (Felis catus) can affect wildlife communities; however, responses to these disturbances might not be ubiquitous and may vary with local conditions. We investigated European pine marten's (Martes martes) distribution on Elba Island, Italy, where the species is the only wild carnivore. In this system, pine martens act as the top predator, and human presence is mostly driven by seasonal tourism. We evaluated (1) pine marten's occurrence in relation to vegetation type and elevation and the potential effects of proximity to settlements, (2) whether pine marten's distribution was associated with the co-occurrence of humans and domestic cats, and, if so, (3) whether these co-occurrence patterns were associated with proximity to anthropogenic infrastructures. Additionally, we explored similarities in activity patterns between pine marten and the other two species. We collected camera-trap data at 77 locations throughout Elba Island in February-July 2020. Using single-season multistate occupancy models, we found evidence that pine martens' occupancy was generally high across all vegetation types and elevation, and proximity to settlements was only weakly associated with the species occurrence. Contrary to expectations, we found no evidence of an association between pine martens' distribution and the presence of either humans or free-roaming domestic cats on Elba Island. Opposing activity patterns might have facilitated pine martens' co-existence with humans, with pine martens being active at ground level almost exclusively during nighttime. On the contrary, cats and pine martens showed similar activity patterns, and further studies are needed to define the co-existence mechanisms. These findings have important management implications and suggest that response to direct and indirect anthropogenic pressures can be highly context-dependent and mediated by the availability of resources and competition mechanisms.

EA-CNN: Enhanced Attention-CNN with Explainable AI for Fruit and Vegetable Classification

The quality of vegetables and fruits are judged by their visual features. Misclassification of fruits and vegetables lead to a financial loss. To prevent the loss, superstores need to classify fruits and vegetables in terms of size, color and shape. To improve the accuracy of models for classifying fruits and vegetables, researchers have introduced various CNN architectures like VGG16, AlexNet, DenseNet-121 and different attention-based variants by compromising models’ complexity leading to high computational cost and overlooking feature’s contribution for interpretable predictions. Additionally, the major drawback in most of the existing works is the utilization of the limited dataset and number of classes for fruit and vegetable classification task instead of the benchmark dataset like Fruit-360 which comprises of 141 distinct fruit and vegetable classes. In this study, an explainable artificial intelligence (XAI) driven enhanced attention-CNN (EA-CNN) is proposed for accomplishing the fruit and vegetable classification task accurately and efficiently. The proposed EA-CNN model (a) detects the class of fruits and vegetable accurately through visual features by manipulating undiscover customized pooling technique and enhanced attention feature extraction mechanism, (b) classifies the fruits and vegetables efficiently through a simplified architecture, (c) provides interpretable predictions through XAI approach. By utilizing entire fruit-360 database, this research noteworthily enhances the model’s capability to classify wide range of fruits and vegetables, thus providing an effective and reliable solution for practical applications. The proposed study outclasses baseline models with regard to accuracy and computational cost on fruit-360 benchmark dataset. EA-CNN achieves better accuracy of 98.1% in smaller number of iterations as compared to benchmark models. Moreover, to further prove the efficiency, generalization ability, robustness, scalability and adaptability of this research, the architecture of proposed EA-CNN model is validated on another real-world dataset named as ‘Fruit Recognition’. The experimental outcomes display that the proposed EA-CNN model produces substantial performance on Fruit-Recognition dataset as well by attaining a generalized accuracy of 96%.

Biogenic Amines: Catalysis, Quality, and Safety Aspects of Food Items Consumed in Saudi Arabia

Introduction: In this study, the identification and quantification of biogenic amines in 45 commonly consumed food samples in Saudi Arabia were carried out. The enzymes responsible for producing these biogenic amines include spermidine (SPD), putrescine (PUT), tryptamine (TRP), tyramine (TYR), and histamine (HIS), which are synthesized through organo-catalytic pathways. Method: The diverse range of samples analyzed encompassed various types of beef, pickle varieties, canned fish, vegetables, chicken varieties, spices, fruits, and salad ingredients. Sample preparation involved the use of dansyl chloride after aqueous extraction, followed by isolation and analysis using reversed-phase HPLC with a UV detector. In five beef samples, mean concentrations of SPD, PUT, TRP, HIS, and TYR were identified as 9.41, 8.98, 155.8, 100.8, and 304.2 mg kg-1, respectively. Canned fish samples exhibited mean concentrations of TRP, PUT, HIS, TYR, and SPD at 71.6, 3.88, 29.2, 2.56, and 2.02 mg kg-1, respectively. Result: Among five pickle samples, mean concentrations of TRP, PUT, HIS, TYR, and SPD were reported as 118.8, 39.12, 35.2, 27.2, and 2.56 mg kg-1, respectively. Chicken samples primarily contained TRP, HIS, and SPD as the identified biogenic amines, with mean concentrations of 87.2, 105.6, and 5.22 mg kg-1, respectively. Fruit samples generally exhibited low levels of all enzymes except for TRP. Conclusion: It was found that vegetables, seasonings, and salad ingredients either had undetectable or low quantities of biogenic amines.