Classification Of Species Research Articles

Advanced Image Preprocessing and Integrated Modeling for UAV Plant Image Classification

The automatic identification of plant species using unmanned aerial vehicles (UAVs) is a valuable tool for ecological research. However, challenges such as reduced spatial resolution due to high-altitude operations, image degradation from camera optics and sensor limitations, and information loss caused by terrain shadows hinder the accurate classification of plant species from UAV imagery. This study addresses these issues by proposing a novel image preprocessing pipeline and evaluating its impact on model performance. Our approach improves image quality through a multi-step pipeline that includes Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) for resolution enhancement, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast improvement, and white balance adjustments for accurate color representation. These preprocessing steps ensure high-quality input data, leading to better model performance. For feature extraction and classification, we employ a pre-trained VGG-16 deep convolutional neural network, followed by machine learning classifiers, including Support Vector Machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost). This hybrid approach, combining deep learning for feature extraction with machine learning for classification, not only enhances classification accuracy but also reduces computational resource requirements compared to relying solely on deep learning models. Notably, the VGG-16 + SVM model achieved an outstanding accuracy of 97.88% on a dataset preprocessed with ESRGAN and white balance adjustments, with a precision of 97.9%, a recall of 97.8%, and an F1 score of 0.978. Through a comprehensive comparative study, we demonstrate that the proposed framework, utilizing VGG-16 for feature extraction, SVM for classification, and preprocessed images with ESRGAN and white balance adjustments, achieves superior performance in plant species identification from UAV imagery.

Mediterranean Islands as Refugia for Elasmobranch and Threatened Fishes

ABSTRACTAimThe Mediterranean Sea is one of the most anthropized seas in the world but also a marine biodiversity hotspot with many fish species under threat. The main goal of the study is to test whether on the heavily fished and anthropized Mediterranean coast, the less impacted Corsica and Balearic Islands, can be considered as refugia for threatened and elasmobranch fishes independently of protection by marine reserves.LocationThe French Mediterranean coast and three north‐western Mediterranean islands: Corsica and also Mallorca and Minorca from the Balearic archipelago.MethodsWe performed 187 fish surveys using environmental DNA metabarcoding on three islands and 109 along the continental coast. Of the 78 surveys on islands 22 correspond to no‐take marine reserves and of the 109 continental surveys 26 were carried out within reserves. After eDNA filtration, extraction, amplification, and sequencing we estimated the number of fish species but also the number commercial, threatened and elasmobranch fish species on each sample. We then performed an ANOVA by permutation to test the effect of insularity and protection on these four biodiversity metrics. We also modelled these four biodiversity metrics as a function of protection and human pressure but also environmental, habitat and sampling conditions. We also built species accumulation curves to obtain asymptotes representing the potential regional pools for each species category on both island and continental coasts.ResultsWe obtained a total of 175,982,610 reads over the 187 eDNA samples that were assigned to 153 fish species including 17 elasmobranch species among which 7 were only detected on islands. We observed a higher total fish richness on continental than island surveys regardless of protection but a higher threatened and elasmobranch fish richness on the island than on continental surveys. We obtained a significant, negative and predominant human gravity impact on the diversity of elasmobranch species. The modelled asymptote reached 148 teleostean fish species on islands and 196 on the continental coastline with a very similar rate of diversity increase with sampling effort but the shape of the species accumulation curves differed markedly for elasmobranchs with a stronger increase in diversity with sampling effort on islands.Main ConclusionsOur findings highlight that Mediterranean islands can be refugia for sharks and rays but also threatened fishes in this overexploited region. Our results also suggest that reducing or banning trawling activities may play a key role for conserving vulnerable fishes, beyond the benefits of no‐take marine reserves, which appear limited on these large home‐range species.

Genetical and Morphological Identification of Prosthogonimus pellucidus (Digenea, Prosthogonimidae) in Grus japonensis

Species of the family Prosthogonimidae are considered the most pathogenic trematodes of poultry and wild birds worldwide, causing heavy economic losses in many countries. Prosthogonimosis was a common parasitic disease of Grus japonensis (Müller, 1776) which caused inflammation of the cloaca and bursa of Fabricius and even death. Morphological identifications of Prosthogonimus species are easily confusing; therefore, molecular characterization is used for discrimination. The present study was conducted to identify Prosthogonimus species at Zhalong National Nature Reserve, northeast of China. Considering the morphological variability and wide host range of individual Prosthogonimus species, a combination of both morphological and molecular analyses is indispensable for the valid identification of this parasite and the internal transcribed spacer (ITS) region was amplified for the sequence analysis and phylogenetic analysis. The results of molecular analysis together with phylogenetic reconstruction indicated that the Prosthogonimus pellucidus (von Linstow, 1873) in this study form a single cluster with P. pellucidus, revealing potentially high diversity within the genus Prosthogonimus. Classification of Prosthogonimus species seems to be unrelated to the host and may be related to geographical location. These data provide a significant resource of molecular markers for studying the taxonomy, population genetics, and systematics of Prosthogonimidae.

Unravelling the distribution of mycorrhizal plants in the Colombian páramos

AbstractThe tropicalpine páramo stands out as a unique biodiversity hotspot located in the northern Andes. Over the last decades, páramo plants and animals have been increasingly studied, yet a critical knowledge gap remains on their microbial ecology. Given the crucial importance of mycorrhiza in structural and functional communities, our study aimed to explore their geographic distribution in the Colombian páramos. Specifically, we used information about their host plants as a proxy and modelled their current distribution to unravel spatial patterns related to mycorrhizal status and mycorrhizal types. We constructed species distribution models for 440 vascular plant species representatives of the Colombian páramos using a combination of climatic and edaphic variables. Then we stacked the models following species classification into their mycorrhizal type: arbuscular, ectomycorrhizal, ericoid, orchid, and non-mycorrhizal. Our results suggest that the abundance of mycorrhizal species decreases with increasing elevation and in the west-to-east direction. The arbuscular type of mycorrhiza is chiefly dominant and spreads out widely throughout the Colombian páramos. The other types showed singular patterns, with (i) species having ericoid mycorrhiza being abundant in both the southern (Nariño–Putumayo) and the northern páramos (Sierra Nevada–Perijá); (ii) species with orchid mycorrhiza being abundant in the Western Cordillera and overall at low elevations, (iii) ectomycorrhizal species being absent, and (iv) non-mycorrhizal species being widely spread and reaching high elevations. Our study highlights the potentially high level of mycorrhization of this alpine system and contributes new spatial and environmental information towards a better understanding of mycorrhizal biogeography in tropical mountains.

Insight into the Phylogenetic Relationships of Phasmatodea and Selection Pressure Analysis of Phraortes liaoningensis Chen & He, 1991 (Phasmatodea: Lonchodidae) Using Mitogenomes

Stick and leaf insects are a group among the Insecta that are famous for their extraordinary mimicry ability. Since the establishment of the Phasmatodea, their internal classification has been constantly revised. Mitochondrial genes as molecular markers have been widely used for species classification, but the phylogenetic relationships within the Phasmatodea remain to be thoroughly discussed. In the present study, five mitogenomes of Phasmatodea ranging from 15,746 bp to 16,747 bp in length were sequenced. Bayesian inference (BI) and maximum likelihood (ML) analyses were carried out based on a 13 PCGs data matrix (nt123) and a combined matrix of 13 PCGs and two rRNA genes (nt123_rRNA). The present study supports the conclusion that Phylliidae was the basal group of Neophasmatodea and confirms the monophyly of Lonchodinae and Necrosciinae, but it shows that Lonchodidae was polyphyletic. A sister group of Bacillidae and Pseudophasmatidae was also recovered. The phylogenetic tree based on the nt_123 dataset showed higher node support values. The construction of a divergent time tree in this study supported the conclusion that extant Phasmatodea originated in the Jurassic (170 Mya) and most lineages diverged after the Cretaceous–Paleogene extinction event. To explore whether the mitochondrial genes of Phraortes liaoningensis collected from high latitudes where low temperatures occur for eight months of the year are under selection pressure, this study used the branch-site model and the branch model to analyze the selection pressure on the 13 mitochondria protein-coding genes (PCGs). We found that both ND2 and ND4L of Ph. liaoningensis exhibited positive selection sites using the branch-site model. This study shows that a low-temperature environment causes mitochondrial genes to be selected to meet the energy requirements for survival.

Analyzing the Distribution Patterns of Endemic Quercus vulcanica (Boiss. et Heldr. ex) Kotschy in Türkiye Under Climate Change Using Ensemble Modeling

Quercus vulcanica (Boiss. et Heldr. ex) Kotschy (Kasnak oak), one of the 18 Quercus species naturally distributed in Anatolia, is an endemic species with a restricted distribution range. In accordance with the International Union for the Conservation of Nature (IUCN) Red List of Endangered Species classification, Quercus vulcanica is designated as a species of low risk (LC: Least Concern). However, it is predicted that the habitat of Quercus vulcanica will narrow and that the species will become endangered as a result of potential climate change scenarios in the future. The aim of this study was to estimate the temporal and spatial distribution of Quercus vulcanica in Anatolia during the LGM, as well as to examine the impact of present and future climate changes on the species. In this context, principal component analysis was applied to 19 bioclimatic variables of the Community Climate System Model Version 4 (CCSM4) climate model, with nine variables identified for use in modeling. Habitat suitability was estimated using the Biodiversity Modeling (BIOMOD) ensemble modeling method, which combines the results of nine different algorithms through the R package ‘biomod2’, applying both committee averaging and weighted average approaches. To evaluate the performance of the models, the Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC), True Skill Statistics (TSS), KAPPA and Boyce Index were calculated. The contributions of the environmental variables were determined on a per-algorithm-model basis. The results of the analyses show that the bioclimatic variables that contribute the most to the distribution of the species are Bio8. The modeling results show that Quercus vulcanica is capable of occupying suitable habitat areas across the majority of Anatolia during the Last Glacial Maximum (LGM). It is anticipated that future projections will indicate a notable reduction in the extent of suitable habitat for the species, with the remaining areas confined to the vicinity of the Ilgaz Mountains, Köroğlu Mountains and Bolkar Mountains. Given the increasing destruction that Quercus vulcanica, an endemic plant, will be adversely affected by as a result of human impacts and climate change, it is of the highest importance to develop adaptation strategies with a view to protecting the species’ habitat and the sustainability of the species.

Automated Bird Species Identification Through Machine Learning Techniques

The taxonomy of bird species is fundamental to ecological research, conservation efforts, and biodiversity monitoring. Traditional identification methods, which rely on field notes and visual assessments by trained ornithologists, are often labor-intensive, time-consuming, and prone to error. In recent years, machine learning algorithms and pre-trained models such as ResNet, Histogram of Oriented Gradients (HOG), and Scale-Invariant Feature Transform (SIFT) have shown significant promise in automating bird species classification. This study explores the application of these advanced models in identifying bird species from visual data, discussing key challenges, methodologies, and the potential to achieve high classification accuracy with reliable confidence scores. By leveraging deep learning techniques, we aim to enhance the precision and scalability of bird taxonomy, supporting more efficient ecological studies and conservation practices.

CLIP-FSSC: A Transferable Visual Model for Fish and Shrimp Species Classification Based on Natural Language Supervision

Fish and shrimp species classification is a practical need in the field of aquaculture. Traditional classification method includes extracting modal features of the single image and training them on downstream datasets. However, this method has the disadvantage of requiring manual annotated image data and significant training time. To address these issues, this paper introduces a method named CLIP-FSSC (Contrastive Language–Image Pre-training for Fish and Shrimp Species Classification) for zero-shot prediction using a pre-trained model. The proposed method aims to classify fish and shrimp species in the field of aquaculture using a multimodal pre-trained model that utilizes semantic text description as an image supervision signal for transfer learning. In the downstream fish dataset, we use natural language labels for three types of fish - grass carp, common carp, and silver carp. We extract text category features using a transformer and compare the results obtained from three different CLIP-based backbones for the image modality - vision transformer, Resnet50, and Resnet101. We compare the performance of these models with previous methods that performed well. After performing zero-shot predictions on samples of the three types of fish, we achieve similar or even better classification accuracy than models trained on downstream fish datasets. Our experiment results show an accuracy of 98.77%, and no new training process is required. This proves that using the semantic text modality as the label for the image modality can effectively classify fish species. To demonstrate the effectiveness of this method on other species in the field of aquaculture, we collected two sets of shrimp data - prawn and cambarus. Through zero-shot prediction, we achieve the highest classification accuracy of 92.00% for these two types of shrimp datasets. Overall, our results demonstrate that using a multimodal pre-trained model with semantic text description as an image supervision signal for transfer learning can effectively classify fish and shrimp species with high accuracy, while reducing the need for manual annotation and training time.

Tree Species Classification by Multi-Season Collected UAV Imagery in a Mixed Cool-Temperate Mountain Forest

Effective forest management necessitates spatially explicit information about tree species composition. This information supports the safeguarding of native species, sustainable timber harvesting practices, precise mapping of wildlife habitats, and identification of invasive species. Tree species identification and geo-location by machine learning classification of UAV aerial imagery offer an alternative to tedious ground surveys. However, the timing (season) of the aerial surveys, input variables considered for classification, and the model type affect the classification accuracy. This work evaluates how the seasons and input variables considered in the species classification model affect the accuracy of species classification in a temperate broadleaf and mixed forest. Among the considered models, a Random Forest (RF) classifier demonstrated the highest performance, attaining an overall accuracy of 83.98% and a kappa coefficient of 0.80. Simultaneously using input data from summer, winter, autumn, and spring seasons improved tree species classification accuracy by 14–18% from classifications made using only single-season input data. Models that included vegetation indices, image texture, and elevation data obtained the highest accuracy. These results strengthen the case for using multi-seasonal data for species classification in temperate broadleaf and mixed forests since seasonal differences in the characteristics of species (e.g., leaf color, canopy structure) improve the ability to discern species.

Pangenome analysis of Paenibacillus polymyxa strains reveals the existence of multiple and functionally distinct Paenibacillus species.

The development of sequencing technologies has led to an exponential increase in microbial sequencing data. Accurately identifying bacterial species remains a challenge because of extensive intra-species variability, the need for multiple identification methods, and the rapid rate of taxonomic changes. A substantial contribution to elucidating the relationships among related bacterial strains comes from comparing their genomic sequences. This comparison also allows for the identification of the "pangenome," which is the set of genes shared by all individuals of a species, as well as the set of genes that are unique to subpopulations. Here, we applied this approach to Paenibacillus polymyxa, a species studied for its potential as a biofertilizer and biocontrol agent and known as an antibiotic producer. Our work highlights the need for a more efficient classification of this bacterial species and provides a better delineation of strains with different properties.

MosquitoSong+: A noise-robust deep learning model for mosquito classification from wingbeat sounds.

In order to assess risk of mosquito-vector borne disease and to effectively target and monitor vector control efforts, accurate information about mosquito vector population densities is needed. The traditional and still most common approach to this involves the use of traps along with manual counting and classification of mosquito species, but the costly and labor-intensive nature of this approach limits its widespread use. Numerous previous studies have sought to address this problem by developing machine learning models to automatically identify species and sex of mosquitoes based on their wingbeat sounds. Yet little work has addressed the issue of robust classification in the presence of environmental background noise, which is essential to making the approach practical. In this paper, we propose a new deep learning model, MosquitoSong+, to identify the species and sex of mosquitoes from raw wingbeat sounds so that it is robust to the environmental noise and the relative volume of the mosquito's flight tone. The proposed model extends the existing 1D-CNN model by adjusting its architecture and introducing two data augmentation techniques during model training: noise augmentation and wingbeat volume variation. Experiments show that the new model has very good generalizability, with species classification accuracy above 80% on several wingbeat datasets with various background noise. It also has an accuracy of 93.3% for species and sex classification on wingbeat sounds overlaid with various background noises. These results suggest that the proposed approach may be a practical means to develop classification models that can perform well in the field.

Characterization of gut microbiota dynamics in an Alzheimer’s disease mouse model through clade-specific marker-based analysis of shotgun metagenomic data

Alzheimer’s disease (AD) is a complex neurodegenerative disorder significantly impairing cognitive faculties, memory, and physical abilities. To characterize the modulation of the gut microbiota in an in vivo AD model, we performed shotgun metagenomics sequencing on 3xTgAD mice at key time points (i.e., 2, 6, and 12 months) of AD progression. Fecal samples from both 3xTgAD and wild-type mice were collected, DNA extracted, and sequenced. Quantitative taxon abundance assessment using MetaPhlAn 4 ensured precise microbial community representation. The analysis focused on species-level genome bins (SGBs) including both known and unknown SGBs (kSGBs and uSGBs, respectively) and also comprised higher taxonomic categories such as family-level genome bins (FGBs), class-level genome bins (CGBs), and order-level genome bins (OGBs). Our bioinformatic results pinpointed the presence of extensive gut microbial diversity in AD mice and showed that the largest proportion of AD- and aging-associated microbiome changes in 3xTgAD mice concern SGBs that belong to the Bacteroidota and Firmicutes phyla, along with a large set of uncharacterized SGBs. Our findings emphasize the need for further advanced bioinformatic studies for accurate classification and functional analysis of these elusive microbial species in relation to their potential bridging role in the gut-brain axis and AD pathogenesis.

Enhanced fish species classification using dynamic multilayer perceptron and transformer encoders with extra distribution data

Enhanced fish species classification using dynamic multilayer perceptron and transformer encoders with extra distribution data

Multimodal In-Sensor Computing System Using Integrated Silicon Photonic Convolutional Processor.

Photonic integrated circuits offer miniaturized solutions for multimodal spectroscopic sensory systems by leveraging the simultaneous interaction of light with temperature, chemicals, and biomolecules, among others. The multimodal spectroscopic sensory data is complex and has huge data volume with high redundancy, thus requiring high communication bandwidth associated with high communication power consumption to transfer the sensory data. To circumvent this high communication cost, the photonic sensor and processor are brought into intimacy and propose a photonic multimodal in-sensor computing system using an integrated silicon photonic convolutional processor. A microring resonator crossbar array is used as the photonic processor to implement convolutional operation with 5-bit accuracy, validated through image edge detection tasks. Further integrating the processor with a photonic spectroscopic sensor, the in situ processing of multimodal spectroscopic sensory data is demonstrated, achieving the classification of protein species of different types and concentrations at various temperatures. A classification accuracy of 97.58% across 45 different classes is achieved. The multimodal in-sensor computing system demonstrates the feasibility of integrating photonic processors and photonic sensors to enhance the data processing capability of photonic devices at the edge.

Spatial Connectivity Through Mountains and Deserts Drove South American Scorpions Dispersal

ABSTRACTAimThe aim of this study is to infer the geographic dispersal paths and the environmental conditions that shaped the historical biogeography of Brachistosternus scorpions in South America. We evaluated the role that altitude and aridity had on the geographic distance that each species dispersed from the location of the genus common ancestor. Based on the previous studies, we evaluated the hypothesis postulating that species geographic expansion was promoted by arid conditions in high altitudes.LocationSouth America.TaxonBrachistosternus genus.MethodsWe integrated two methodological approaches in this study, the phylogenetic Geographical model and the Conductance model, considering the uncertainty associated with the phylogenetic relationship and the species classification. The Geo model infers the locations of ancestral species in a phylogenetic tree, assuming a spherical space and using samples of georeferenced locations for every species as input data. It allows us to estimate the species dispersal routes and distances from the location of the genus common ancestor. The Conductance model is based on the circuit theory and infers the geographic route and distance of least resistance between an origin and destination point. We defined the origin as the location of the genus common ancestor obtained from the Geo model and a destination point as the current geographic location of each species. This model infers the geographic routes with the least cost of resistance for dispersal in a landscape of varying altitude and aridity. Finally, we evaluated the correlation between the two dispersal distances each species have moved from the location of the common ancestor, that is, the distance inferred from the Geo model and from the Conductance model.ResultsThe Geo model shows that Brachistosternus's geographical origin was most likely along the coast of south Peru, and central Chile. From this location, extant species dispersed thorough routes ranging from 873 to 2800 km in average. The Conductance model that considers the routes with least resistance to elevation and aridity simulated dispersal distances that are highly correlated with the species dispersal distances obtained from the Geo model.Main ConclusionsWe revealed the geographic dispersal routes, with the least resistance to the pressures imposed by changes in altitude and aridity, that 55 species of scorpions have probably followed in the last 30 million years in South America. These geographic routes that went along the Andean Mountains and the arid zones of South America shaped the current spatial distribution of the genus Brachistosternus.

Microtopography-Guided precision restoration of sandy lands through UAV: A case study in Hunshandake Sandy Land, China

Increasing desertification rates have adversely affected biodiversity and ecosystem functioning in sandy lands. Ecological restoration is an effective way to combat desertification. Specific microtopographic characteristics may facilitate vegetation growth, enhancing the success of restoration efforts. However, limited research to date has explored how microtopography may guide the “precision restoration” of sandy lands by supporting spatially continuous patterns of vegetation growth. Here, high-resolution unmanned aerial vehicle (UAV) multispectral data were used to identify individual species and extract microtopographic variables, and the relationships between vegetation growth and microtopography were characterized for the Hunshandake Sandy Land in China. The distribution of three dominant shrub species and grasses was investigated by comparing the performance of five popular machine-learning methods. An auto-marking watershed algorithm was then developed to discriminate individual semi-shrubs (Artemisia desertorum). Finally, a new vegetation growth index (VGI), calculated from the UAV-derived crown area, normalized difference vegetation index (NDVI), and canopy height, was used to characterize the relationships between vegetation growth and several microtopographic variables (aspect, slope, and a topographic wetness index [TWI]). With the highest species classification accuracy (94.36%) and individual discrimination rate (81%), areas with high humidity (TWI), gentle and shady slopes were found to most strongly support vegetation growth; for grasses, VGI was lower in artificially-restored regions than naturally-developed regions with similar microtopographic characteristics. These findings provide valuable guidance on the use of UAV to support diverse ecological restoration solutions in sandy lands with a “precision restoration” strategy, thereby improving the survival of key vegetation for sand restoration, especially in remote areas.

Comparison Between Gans and Diffusion Models in the Generation of Synthetic Images for Enhancing Tree Species Recognition

Objective: This study investigates the application of Generative Adversarial Networks (GANs) and Diffusion Models in data augmentation to improve the classification of tree species images, which is essential for sustainable forest management. Theoretical Framework: Fundamental concepts of machine learning, generative and classifier networks, as well as data augmentation techniques through synthetic image generation, are presented, establishing a solid foundation for the research. Method: The research utilized 2,178 images of cross-sections of wood from 18 species, applying Deep Convolutional GAN (DCGAN) and U-Net with diffusion on a rare species, evaluated using FID and IS metrics. The generated images were used to train and validate classification models, assessed by F1-score. Results and Discussion: The results revealed that Diffusion Models generated more realistic images and performed better in the classification of the rare species. The discussion contextualizes these results in light of the theoretical framework, exploring their implications for environmental management. Limitations, such as the impact of indiscriminate addition of synthetic data, are also addressed. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of forest management and environmental management. These implications may cover areas such as sustainable innovation and biodiversity conservation. Originality/Value: This study contributes to the literature by demonstrating the potential of Diffusion Models to outperform GANs in generating synthetic images for sustainable forest management purposes. The relevance and value of this research are evidenced by its practical application in promoting innovative and sustainable practices in environmental management.

A pictorial key to the adult and larval nasal mites (Halarachnidae) of marine mammals.

Mites in the family Halarachnidae are common endoparasites infesting the nasal tissues of a variety of marine mammals. These mites are easily transmissible and compromise the health of their hosts, especially in captive environments. While these mites are noted by marine mammal caretakers, they may easily be misidentified due to repeated revisions to halarachnid mite taxonomy and reclassification of misidentified specimens. Species identification currently requires multiple taxonomic keys, knowledge of revisions to species classifications through time, and training in acarology, which is impractical for marine mammal clinicians. Therefore, to summarize the known taxonomy and aid in future identification of halarachnid mites, we present a pictorial key composed of illustrations based on existing literature and images obtained by scanning electron microscopy (SEM) and high-resolution light microscopy (LM). Illustrations are organized into flow charts for the identification of both adult and larval stages. Dorsal shield silhouettes are also provided to facilitate the identification of adults. We hope that this key be used to simplify future taxonomic research, provide a standard for species identification, and aid in the diagnosis of halarachnid infestations in captive and rehabilitated marine mammal populations.

Neutrosophic set and optimized deep learning for classification of chicken Eimeria species: a practical solution for poultry industry

AbstractTo optimize infection control and bolster productivity within the poultry industry, it is imperative to accurately classify Chicken Eimeria species. There are several methods for determining Eimeria disease in chickens. Traditional methods involve watching for clinical symptoms, and macroscopic lesions, and studying the parasite’s biology and oocyst morphology. These methods are frequently time-consuming and labor-intensive, necessitating the manual collection and analysis of samples, which can be especially difficult in large chicken farms. Deep learning algorithms, on the other hand, provide automated, accurate, and non-invasive methods for the detection of Eimeria. This paper proposed a classification model for the automatic classification of chicken Eimeria species. The proposed model is mainly based on integrating neutrosophic set theory and InceptionV3 deep-learning architecture. Three primary phases make up the proposed chicken Eimeria species classification model: the data preprocessing phase, the neutrosophic image conversion phase, and the image classification phase. To address the issue of class imbalance in the adopted dataset and enhance the model’s generalizability, the random oversampling method, and data augmentation techniques are employed during the data preprocessing phase. The preprocessed data is considered to feed the neutrosophic set-based segmentation algorithm, where true, false, and intermediate subsets are extracted. Finally, the true subset is utilized to feed the optimized InceptionV3. To determine the optimal hyperparameter values for InceptionV3, a modified version of the Brown Bear optimization algorithm is proposed in this paper. To evaluate the effectiveness of the proposed model, a real benchmark dataset comprising images of different Eimeria species is adopted. The experimental results revealed that the proposed model offers a more efficient and accurate alternative to traditional methods and state-of-the-art models, enabling faster and more effective diagnosis and treatment of Eimeria infections. It achieved an overall accuracy, specificity, sensitivity, and F1-score of nearly 100%. Additionally, the results showed that the high performance of the proposed model can reduce labor costs and boost throughput, thereby enhancing economic viability even more.

A Review of Semantic Segmentation and Instance Segmentation Techniques in Forestry Using LiDAR and Imagery Data

The objective of this review is to conduct a critical analysis of the current literature pertaining to segmentation techniques and provide a methodical summary of their impact on forestry-related activities, emphasizing their applications using LiDAR and imagery data. This review covers the challenges, progress, and application of these strategies in ecological monitoring, forest inventory, and tree species classification. Through the process of synthesizing pivotal discoveries from multiple studies, this comprehensive analysis provides valuable perspectives on the present status of research and highlights prospective areas for further exploration. The primary topics addressed encompass the approach employed for executing the examination, the fundamental discoveries associated with semantic segmentation and instance segmentation in the domain of forestry, and the ramifications of these discoveries for the discipline. This review highlights the effectiveness of semantic and instance segmentation techniques in forestry applications, such as precise tree species identification and individual tree monitoring. However, challenges such as occlusions, overlapping branches, and varying data quality remain. Future research should focus on overcoming these obstacles to enhance the precision and applicability of these segmentation methodologies.