Traditional Identification Research Articles

Educational Management Based on Religious Moderation: Empirical Study of Practices in Pesantren

Religious moderation has become an important concept in Islamic education, especially in Indonesia, as an effort to instill the values of balance, tolerance, and inclusiveness. Pesantren, as traditional Islamic educational institutions, have a central role in teaching moderation values to santri. This article aims to analyze the application of religious moderation in educational management in pesantren, focusing on aspects of curriculum, leadership, and character education. This research uses a qualitative method with a case study approach at Pesantren An-Nawawi Berjan Purworejo. Data were collected through observation, in-depth interviews with caregivers, santri, and teaching staff, as well as document analysis related to curriculum policies and pesantren management. The analysis was conducted using a thematic approach to identify patterns of religious moderation implementation in the daily activities of the pesantren. The results showed that the application of religious moderation in Pesantren An-Nawawi Berjan Purworejo had a positive impact on increasing tolerance among students and inclusive character building. Religious moderation is also integrated in human resource management, curriculum development, and pesantren leadership activities. In addition, the values of religious moderation help this pesantren in managing social dynamics better, increasing the operational effectiveness of the Institute. The implementation of religious moderation in Pesantren An-Nawawi shows how the pesantren can adapt to modern challenges such as intolerance and radicalism, while still maintaining its traditional identity. The integration of moderation values in the curriculum and management of pesantren strengthens the role of pesantren as institutions that educate generations that are not only academically intelligent, but also have tolerant and inclusive characters. This research confirms that religious moderation plays an important role in pesantren education management, particularly in improving tolerance and management effectiveness. The implication is that this model of implementing religious moderation can be adopted by other pesantren in Indonesia in facing the challenges of radicalism and maintaining social harmony.

Loop-mediated isothermal amplification assay coupled with lateral flow dipstick for the rapid detection of methicillin-resistant Staphylococcus pseudintermedius from dogs.

Staphylococcus pseudintermedius is a global animal pathogen. Traditional identification methods are time-consuming necessitating a more efficient approach. This study validated and enhanced the loop-mediated isothermal amplification (LAMP) technique by integration it with a lateral flow dipstick (LFD) assay for the detection of S. pseudintermedius and methicillin-resistant S. pseudintermedius (MRSP) strains. Conventional identification methods were compared with LAMP and matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). The isolates were tested for MRSP detection using oxacillin and cefoxitin disk diffusion tests alongside the LAMP assay targeting the mecA gene, a marker for methicillin resistance. Results showed that LAMP combined with LFD effectively detected S. pseudintermedius and MRSP. This study identified 53 isolates as S. pseudintermedius by conventional and LAMP methods, with MALDI-TOF MS correctly identifying 39.62% (21/53). The mecA gene, crucial for methicillin resistance, was detected in all PCR-positive isolates (n = 33) by LAMP, while the disk diffusion method identified 69.70% (23/33) of mecA-positive strains. LAMP and conventional methods exhibited superior accuracy, sensitivity, and specificity (all 100%) compared to MALDI-TOF MS, which showed lower sensitivity (39.62%) for S. pseudintermedius identification. Similarly, the LAMP assay demonstrated higher accuracy, sensitivity, and specificity (all 100%) for MRSP detection compared to the disk diffusion method (83.33%, 69.70%, and 94.87%, respectively). The LAMP assay coupled with the LFD method proved suitable for routine bacterial identification in laboratories, offering adequate sensitivity and specificity with simple steps and short reaction time.

Automated Cancer Detection Using CNNs: Innovations and Applications

Large-scale datasets and complicated cell architectures provide challenges for traditional tumor identification approaches, which often rely on human analysis or traditional machine-learning techniques. This may result in errors and inefficiencies. The inconsistent shape of tumor cells may be a challenge for these methods, making border detection and classification inconsistent. This paper suggests an automated tumor detection technique based on convolutional neural networks (CNNs) to increase tumor cell border detection and classification accuracy and efficiency. Several experiments were carried out to confirm the suggested strategy's efficacy. This entails developing and refining the CNN architecture in addition to merging deep learning and data augmentation methods. According to the findings, the model has performed well in a variety of experimental settings, especially when it comes to classifying complicated cell samples. Furthermore, the feasibility of using this model for real-time border detection was explored, indicating its suitability for use in a medical setting. Subsequent investigations will concentrate on enhancing the capabilities of models, expanding their range, and tackling problems like disparities in data. This discovery is significant because it has the potential to transform tumor identification by providing a more accurate, scalable, and efficient approach that might be used extensively in clinical settings and eventually lead to better patient outcomes.

Research status and prospects of intelligent logging lithology identification

Abstract With the increasing of petroleum exploration and development, accurate lithology identification is of crucial. Machine learning (ML) plays a key role in logging lithology identification. By introducing traditional logging lithology identification methods, we review the application of ML in logging lithology identification from the perspectives of bibliometrics and ML classification in this paper. The applications of supervised learning, semi-supervised learning, unsupervised learning, ensemble learning, and deep learning algorithms in logging lithology identification are introduced in detail. Multiple ML algorithms have achieved remarkable results in different scenarios. For example, support vector machine, random forest, eXtreme gradient boosting, and convolutional neural network perform well in logging lithology identification and obtain relatively high identification accuracy. However, ML for logging lithology identification also faces challenges such as data quality, data imbalance, model generalization, and model interpretability. Future research should focus on algorithm optimization and innovation, improvements in data quality and quantity, deep multidisciplinary integration and practical application to enhance the accuracy and reliability of lithology identification. These findings provide strong support for oil and gas exploration and development.

Wooden temple as a marker of the Russian traditional identity

Wooden architecture is a special independent direction of the Russian traditional architecture. It is represented in Russia in its unique quality and diversity. This is truly a national architecture that has absorbed the mythopoetic and Christian worldview of the people. The concept of “Russian North” reflects the significant “Russianness” of the region. Here, all those perfect forms of wooden architecture were developed to continuously influence the entirety of Russian art for centuries. A wooden temple is the pinnacle of culture, its iconic heritage. The best religious buildings were created and preserved in the North.

Enhancing real-time detection of rice diseases using an optimized deep learning model

Rice is a vital staple food globally, but it is susceptible to a wide range of diseases. Early detection of leaf-related diseases is essential for ensuring its sustainability. Traditional disease identification methods often rely on manual techniques, which are time-consuming, labor-intensive, and inefficient. This study proposes a more efficient approach for detecting rice diseases using a customized VGG16 convolutional neural network (CNN), addressing these limitations. The proposed model, which includes 14 convolutional layers and a depth of 512 layers, demonstrates enhanced classification effectiveness. A dataset consisting of 3,611 custom-generated images, along with benchmark datasets, was used for evaluation. The model’s performance was assessed using five CNN-based algorithms: DenseNet121, Inception V3, ResNet50, VGG16, and a personalized VGG model. The proposed model achieved an accuracy of 96% on new data samples, outperforming current state-of-the-art models. These results highlight the proposed method’s superior effectiveness in identifying rice diseases in real-world scenarios.

Online State of Charge Estimation for Battery Based on the Improved Double Fraction‐Order Multi‐Innovation Unscented Kalman Filter With Full Parameters Identification and Self‐Rectification of Covariance Matrices

ABSTRACTThe state of charge (SOC) is an extremely important indicator of the lithium cell. Currently, traditional identification algorithms and integer‐order models are poorly adaptive and unable to respond to the dynamic characteristics of batteries in real time, whereas unscented Kalman filtering is often accompanied by the problem that the covariance matrix is not positively determined, leading to algorithmic collapse. This study combines the multi‐innovation theory with online identification and provides an improved fractional‐order multi‐innovation double unscented Kalman filter (DFOMIUKF) joint estimation algorithm. The algorithm contains a multi‐timescale framework that allows full parametric identification of fractional‐order models, which guarantees the algorithm's ability to cope with complex working conditions, and the problem of failure of the Kalman filtering algorithm is solved by modifying the covariance matrix in real time by singular value decomposition (SVD). On the basis of the fractional RC model, the FOMIUKF1 algorithm allows real‐time updating of model parameters on a macro scale, and the obtained parameters at the microscale are passed to the FOMIUKF2 algorithm for real‐time updating of the charge level of the lithium battery. Then, the DFOMIUKF algorithm is validated under different working environments, respectively. The findings indicate that the proposed algorithm predicts the SOC with the maximum values of root mean square error (RMSE) and mean absolute error (MAE) not exceeding 0.77% and 0.66%, respectively, and maintains high accuracy even at low temperatures. It is illustrated that the proposed algorithm solves the problems of the above offline algorithms with high precision and robustness.

Disease Detection in Tropical Tomato Leaves via Machine Learning Models

This study addresses the significant threat of tomato diseases to production in Ghana, which has led to substantial yield and quality losses, adversely affecting the livelihoods of local farmers and the availability of this essential dietary staple. Traditional disease identification methods are time-consuming and rely on subjective visual inspections, hindering early detection and control. This study develops a machine learning model capable of accurately identifying tomato plant diseases through image processing. The methodology involves processing a dataset of tomato plant images displaying healthy and diseased symptoms. The proposed model employs the YOLOv5 architecture and is deployed on a mobile platform for accessible disease identification. The model achieved a validation mAP@.5 of 0.715, demonstrating strong performance during live, on-site testing. This system provides a swift, accurate, and automated solution for detecting tomato diseases, supporting the sustainability of tomato production in Ghana.

Performance of the VITEK® MS system for the identification of filamentous fungi in a microbiological laboratory in Chile

Filamentous fungi are an emergent cause of severe infections in immunocompromised patients. Timely and accurate identification is crucial to initiate appropriate therapy. Traditional identification methods are time-consuming, labor-intensive, and operator-dependent. Matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) mass spectrometry is a rapid and easy-to-perform identification method. The effectiveness of a commercial MALDI-TOF MS platform to identify filamentous fungi in a clinical laboratory was evaluated. The study included 67 fungal isolates from 35 species/species complexes, which were identified and confirmed in mycology reference laboratories; 32 derived from clinical samples, 34 from strain collections and one was an ATCC strain. The study used the VITEK® MS system (v3.2.0 database), after sample extraction by VITEK® MS Mould Kit. Results were classified as “correct species”, “correct species complex”, “correct genus”and “no identification”. VITEK® MS correctly identified 91.0% of isolates (58.2% to species, 29.9% to species complex, and 1.5% to genus level only). In 82%, the result matched the species/species complex identified by reference methods. No misidentifications were observed. The kit was rapid and easy to use. In conclusion, the VITEK® MS system showed a high capability to accurately identify filamentous fungi in a clinical laboratory.

Hegel’s Theory of Time

Abstract While sharing some features in common with presentist accounts of time, Hegel’s theory of time fundamentally offers an alternative to standard A-Theories, B-Theories, and C-Theories of time. While compatible with Kantian ideality of time on the one hand and spacetime in the theory of general relativity on the other, Hegel’s theory of time reaches beyond both a transcendental form of sensibility on the one hand and a paradigm for material motion in physics, on the other. Further, while Hegel’s theory of time provides reason to reject such theories of time as the Moving Spotlight, Growing and Shrinking Block, and a range of others, my interest here is in making comprehensible Hegel’s theory of time itself. I will argue that according to Hegel’s theory, the past and the future exist as constitutive of the now, and the now is not separable from space, but rather is itself spatial becoming. Stemming from his underlying logic of actuality and becoming, Hegel espouses the view that history (the past) and future possibility are present and constitutive of the “now.” Understanding his theory of time will also help shed some light on a systematic feature found across his philosophy of nature and mind. Additionally, while his theory presents an elegant notion of time within his philosophy, it also offers an intuitive and productive solution for a traditional identity problem of concrete particulars across time and intervenes in contemporary philosophy of time.

Cultural Strategy of Jaranan Arts in the Midst of Modernization: Portrait of Jaranan Okta Putri Kencono in Surabaya City

Jaranan as the traditional heritage of the people of East Java, Surabaya faces great challenges due to urbanization and globalization. The Jaranan Okta Putri Kencono group, although new, managed to survive in the midst of social changes and competition with other art groups. This study examines the cultural strategies applied by this group to adapt to modernization. This research reveals how technological and social adaptations are applied in maintaining the existence of traditional arts amid declining interest in modern entertainment. This qualitative research examines Okta Putri Kencono cultural strategy in adapting to modernization in Surabaya. The study was conducted in Lakarsantri and Wiyung, Surabaya which were chosen because of their relevance to the group's origins and appearance. We triangulated between observation, interview, and documentation as our data collection methods. Interview was conducted with various members of the Okta Putri Kencono group, consisting of consists of the founder, leader, and bopo; bopo senior; and sound system administrator.The study show that three phases faced by Jaranan Okta Putri Kencono group are mythical phase, ontological phase, and functionalist phase. In the mystical phase, Jaranan still maintains spiritual rituals such as offerings as a tribute to ancestors. The ontological phase is characterized by the reinterpretation of traditional values to answer the challenges of modern society, including adaptation to performance elements without leaving traditional identities. Meanwhile, the functionalist phase adapts to modernization, Okta Putri Kencono integrates modern elements, such as audiovisual technology, updated story themes, and collaborations with young artists and creative communities. In essence, the Jaranan Okta Putri Kencono group has successfully navigated the challenges of modernity while preserving its rich cultural heritage. By adapting to changing times, the group ensures the continued vitality of the Jaranan tradition for future generations.

Comparative analysis of the complete chloroplast genome of Pueraria provides insights for species identification, phylogenetic relationships, and taxonomy

Background Pueraria is an edible and medicinal raw material, which is of great value to the pharmaceutical and food industries. Nonetheless, due to morphological diversity and complex domestication history, the classification of Pueraria plants is ambiguous. As the varieties on the market are mixed, the species are difficult to distinguish, and their morphological characteristics are similar to the physical and chemical properties. It is difficult to accurately identify them by traditional identification methods. Chloroplast (cp) genomes are widely used in species identification and phylogenetic studies to achieve accurate identification of medicinal plants, and can also provide more reference information for phylogenetic studies. Based on interspecific and intraspecific sampling, the cp genomes of eight species or varieties of Pueraria plants were examined in this study.ResultsThe study unveiled that the cp genome size varied from 151,555 to 153,668 base pairs (bp), with the total GC content ranging from 35.4 to 37.0%. Moreover, it was discerned that the cp genome contained between 128 and 135 genes. Comparative analysis indicated that the highest number of Simple Sequence Repeats (SSRs) was identified in P. montana and P. alopecuroides, with a preponderance of these SSRs being rich in Adenine (A) and Thymine (T) nucleotides. Complete comparison and sliding window analysis of the cp genome established that the non-coding region exhibited greater sequence differences than the coding region, and that the large single copy (LSC) region demonstrated higher nucleotide polymorphism levels. Fourteen highly variable loci such as rpoB,ycf1,rbcL,trnF-GAA-trnL,psbC-psbD, and ycf4-cemA were detected as potential molecular markers for Pueraria species identification. Moreover, the phylogenetic tree demonstrated that other Pueraria species had the most distant relationship with Haymondia wallichii and Toxicopueraria peduncularis, thereby offering fresh perspectives into the species classification of Pueraria. The molecular clock analysis results indicate that the divergence time of Pueraria may occur at ∼6.46 Ma. It is speculated that the cold climate may be the cause of Pueraria species diversity and promote the radiation of the genus.ConclusionThis research provides theoretical backing and serves as a reference point for the identification and taxonomical classification of Pueraria species. The findings will prove beneficial in future studies on the preservation of medicinal resources, phylogenetic relationships, and genetic engineering of Pueraria plants.

Reliability of wing morphometrics for species identification of human-biting black flies (Diptera: Simuliidae) in Thailand

BackgroundFast and reliable species identification of black flies is essential for research proposes and effective vector control. Besides traditional identification based on morphology, which is usually supplemented with molecular methods, geometric morphometrics (GM) has emerged as a promising tool for identification. Despite its potential, no specific GM techniques have been established for the identification of black fly species.MethodsAdult female black flies collected using human bait, as well as those reared from pupae, were used in this study. Here, landmark-based GM analysis of wings was assessed for the first time to identify human-biting black fly species in Thailand, comparing this approach with the standard morphological identification method and DNA barcoding based on the mitochondrial cytochrome c oxidase subunit I (COI) gene. To explore genetic relationships between species, maximum likelihood (ML) and neighbor-joining (NJ) phylogenetic trees were built. Additionally, three different methods of species delimitation, i.e., assemble species by automatic partitioning (ASAP), generalized mixed yule coalescent (GMYC), and single Poisson tree processes (PTP), were utilized to identify the morphologically defined species. The effectiveness of a COI barcode in identifying black fly species was further examined through the best match (BM) and best close match (BCM) methods.ResultsSeven black fly species, namely Simulium tenebrosum Takaoka, Srisuka & Saeung, 2018 (complex), S. doipuiense Takaoka & Choochote, 2005 (complex), S. nigrogilvum Summers, 1911, S. nodosum Puri, 1933, S. asakoae Takaoka & Davies, 1995, S. chamlongi Takaoka & Suzuki, 1984, and S. umphangense Takaoka, Srisuka & Saeung, 2017 were morphologically identified. Compared with the standard method, the GM analysis based on wing shape showed high success in separating species, achieving an overall accuracy rate of 88.54%. On the other hand, DNA barcoding surpassed wing GM for species identification with a correct identification rate of 98.57%. Species delimitation analyses confirmed the validity of most nominal species, with an exception for S. tenebrosum complex and S. doipuiense complex, being delimited as a single species. Moreover, the analyses unveiled hidden diversity within S. asakoae, indicating the possible existence of up to four putative species.ConclusionsThis study highlights the potential of wing GM as a promising and reliable complementary tool for species identification of human-biting black flies in Thailand.Graphical

Loop-Mediated Isothermal Amplification Combined With Lateral-Flow Dipstick for Detection of Centrocestus formosanus in Ornamental Fish.

The minute intestinal trematode Centrocestus formosanus is a major problem that can be found in the gills of various fish species. This parasite can destroy the gill structure of fish, leading to increased fish morbidity and mortality rates. In this study, loop-mediated isothermal amplification (LAMP) based on the internal transcript spacer 1 region combined with a lateral-flow dipstick (LAMP-LFD) assay was used to detect C. formosanus in goldfish, Carassius auratus. The results showed that LAMP-LFD was specific to C. formosanus and had no cross-amplification with other co-infecting parasite species, close related parasite species or their hosts (both intermediate host and definitive host). The limit of detection is as low as one metacercaria per gill. In testing 40 goldfish gill samples, the LAMP-LFD method showed 100% accuracy when compared to traditional morphological identification. This method can be used as a rapid diagnostic tool for C. formosanus detection to obtain epidemiological information for monitoring, controlling, and preventing outbreaks of this parasite.

A Novel Tool for Soybean Pods and Branches Recognition by Deep Learning Based Approach

ABSTRACTPhenotypic trait identification is crucial in cultivating new soybean varieties with high yield and quality. The traditional soybean phenotypic trait identification relies on manual pod counting and plant height measuring with a ruler. The heavy workload causes the data collected by human resources to be extremely prone to error. Therefore, developing an efficient and high‐quality method to obtain phenotypic data of soybean pods and branches is urgently needed. Three network models including ResNet‐101, Swin‐S and ConvNeXt‐S are compared in this study, and the ConvNeXt‐S model is identified as optimal, with a mAP@0.5 of 0.95, which could reach 74.2%. A deep learning–based approach soybean plant phenotype detection and data storage system is developed, including information on plants, pods and branches. The R2 between the number of pods detected by the system and the true value reached 0.995. These results indicate that the system is more accurate and stable than the manual phenotype identification. Our study paves the way for reducing economic and time costs as well as improving phenotype identification efficiency and accuracy in detecting soybean phenotypes.

Advancing Grapevine Variety Identification: A Systematic Review of Deep Learning and Machine Learning Approaches

The Eurasian grapevine (Vitis vinifera L.) is one of the most extensively cultivated horticultural crop worldwide, with significant economic relevance, particularly in wine production. Accurate grapevine variety identification is essential for ensuring product authenticity, quality control, and regulatory compliance. Traditional identification methods have inherent limitations limitations; ampelography is subjective and dependent on skilled experts, while molecular analysis is costly and time-consuming. To address these challenges, recent research has focused on applying deep learning (DL) and machine learning (ML) techniques for grapevine variety identification. This study systematically analyses 37 recent studies that employed DL and ML models for this purpose. The objective is to provide a detailed analysis of classification pipelines, highlighting the strengths and limitations of each approach. Most studies use DL models trained on leaf images captured in controlled environments at distances of up to 1.2 m. However, these studies often fail to address practical challenges, such as the inclusion of a broader range of grapevine varieties, using data directly acquired in the vineyards, and the evaluation of models under adverse conditions. This review also suggests potential directions for advancing research in this field.

A Time-Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen.

Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process. This study developed a time-frequency-based data-driven approach aiming to improve the effectiveness of traditional data-driven structural damage identification approaches for large complex structures. Firstly, the structural acceleration signals in the time domain were converted into two-dimensional images via the Gram angle difference field (GADF). Subsequently, the characteristic feature in the image data was studied by convolutional neural networks (CNNs) to predict the structural damage conditions. An experimental study on a scale model of a cable-stayed bridge was conducted to identify the damage of stay cables under the moving vehicle load on the main girders. The CNN was employed to extract the characteristic features from the time-varying monitoring data of vehicle-bridge interactions. The CNN parameters were optimized to conduct the structural damage classification task. The performance of the proposed method was evaluated by comparing it with various traditional pre-trained networks. The effect of environmental noise on the prediction accuracy was also investigated. The numerical results show that the ResNet model has the best performance in terms of damage identification accuracy and convergence speed, achieving higher accuracy and faster convergence compared to the other four traditional networks. The method can accurately identify damage on bridges using insufficient sensors on the bridge deck, which has valuable potential for application to real-world bridges with monitoring data. As the Signal-to-Noise Ratio (SNR) decreases from 20 dB to 2.5 dB, the prediction accuracy of ResNet decreases from 86.63% to 62.5%, which demonstrates the robustness and reliability in identifying structural damage.

Automated open-stope design optimization through machine learning methods

ABSTRACT This research innovates in open-stope mine layout optimisation, transitioning from traditional manual identification of ore veins to an automated approach based on DBSCAN and Principal Component Analysis. Paired with an optimisation model for stopes determination, this method streamlines the layout design and ensures efficient and precise configurations under specific constraints, such as stope and pillar sizes. Validation across five diverse, realistic instances demonstrated noteworthy accuracy and efficiency, revealing an average deviation of −0.6% and −0.1% for strike and dip angles, respectively, and processing 10 million blocks in under a minute.

The Multifunctional Catalytic Hemoglobin from Amphitrite ornata: Protocols on Isolation, Taxonomic Identification, Protein Extraction, Purification, and Characterization.

The multifunctional catalytic hemoglobin from the terebellid polychaete Amphitrite ornata, also named dehaloperoxidase (AoDHP), utilizes the typical oxygen transport function in addition to four observed activities involved in substrate oxidation. The multifunctional ability of AoDHP is presently a rare observation, and there exists a limitation for how novel dehaloperoxidases can be identified from macrobenthic infauna. In order to discover more infaunal DHP-bearing candidates, we have devised a facilitated method for an accurate taxonomic identification that places visual and molecular taxonomic approaches in parallel. Traditional visual taxonomic species identification by the non-specialist, at least for A. ornata or even for other marine worms, is a very difficult and time-consuming task since a large diversity is present and the method is restricted to adult worm specimens. The work herein aimed to describe a method that simplifies the taxonomic identification of A. ornata in particular through the assessment of its mitochondrial cytochrome c oxidase subunit I gene by employing the DNA barcoding technique. Furthermore, whole-worm specimens of A. ornata were used to extract and purify AoDHP followed by an H2O2-dependent peroxidase activity assay evaluation against substrate 2,4,6-trichlorophenol. AoDHP isoenzyme A was also overexpressed as the recombinant protein in Escherichia coli, and its peroxidase activity parameters were compared to AoDHP from the natural source. The activity assay assessment indicated a tight correlation for all Michaelis-Menten parameters evaluated. We conclude that the method described herein exhibits a streamlined approach to identify the polychaete A. ornata, which can be adopted by the non-specialist, and the full procedure is predicted to facilitate the discovery of novel dehaloperoxidases from other marine invertebrates.

Advances in insect biomonitoring for agriculture and forestry: A synthesis on a multifaceted special issue of Agricultural and Forest Entomology

Abstract Advancing biomonitoring of insects is vital if we are to safeguard insect biodiversity whilst optimizing the ecosystem service provision necessary for production systems such as agriculture and forestry. Over recent years, many key technological, methodological and conceptual advances have been made in and adjacent to biomonitoring. These advances are set to form the biomonitoring toolbox of the 21st century. The Agricultural and Forest Entomology special issue “Advances in insect biomonitoring for agriculture and forestry” exemplifies several of these advances across ten articles. From artificial intelligence‐based image analysis and identification, through molecular methods for highly resolved monitoring, to innovative use of existing methods, the articles highlight the diverse future of biomonitoring and the multi‐modal nature of its data. Through the advances represented in this special issue, progress towards highly resolved and scalable biomonitoring is certain, with fully automated biomonitoring seeming increasingly possible. These examples do, however, largely still depend on traditional monitoring and identification of insects for validation or reference data collection. To advance biomonitoring effectively, traditional approaches therefore must remain a key component of monitoring schemes and should be integrated with emerging technologies for robust and accurate biomonitoring.