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The study aims to investigate the impact of tax avoidance (TA) on earnings management practices (EM). The current research also investigates the moderating role of board governance characteristics on this relationship in the Egyptian context. The sample incorporates all the non-financial companies included in the Egyptian Stock Exchange between 2017 and 2021. The final sample comprises 120 enterprises from 12 industries, with 600 observations. Statistical analysis employs fixed effects regression, pooled OLS, and GMM estimations to test the proposed hypotheses. We found a significant positive impact of TA on the level of EM. Further, board gender diversity (BGD) and board independence (BIND) were found to have a negative moderating impact as they alleviate the effect of TA on the level of EM. Finally, CEO duality (CEOD) was found to have no moderating impact. To the authors’ knowledge, this is the first study examining how board governance characteristics moderate and influence the level of EM in emerging markets. This adds new insights to the TA and EM literature, as previous research mainly focused on the direct effects of BGD, BIND, and CEOD on EM levels. The current study provides fresh evidence from an emerging market context. Full article

(This article belongs to the Special Issue Financial Reporting, Reputation, and Earnings Quality)

19 pages, 2013 KB

Open AccessArticle

Utilization of Stone Quarry Sludge in the Development of Environmentally Friendly High-Strength Concrete

by Hadi Bahmani, Hasan Mostafaei and Muhammad Ali Rostampour

J. Compos. Sci. 2025, 9(12), 648; https://doi.org/10.3390/jcs9120648 (registering DOI) - 1 Dec 2025

Abstract

This study explores a sustainable strategy for enhancing high-strength concrete (HSC) by partially replacing natural fine aggregates with stone quarry sludge (SQS), a byproduct of quarrying operations. The aim is to promote environmental conservation and waste valorization while maintaining or improving concrete performance. Concrete mixes were prepared by substituting fine sand with SQS at incremental levels of 10%, 20%, 30%, 40%, and 50%. Mechanical properties were assessed through specific weight measurements, compressive strength tests, and three-point bending evaluations. FTIR analysis was conducted to investigate microstructural changes, and a carbon footprint assessment was performed to quantify environmental benefits. The mix containing 20% SQS exhibited optimal performance, achieving a compressive strength of 61 MPa and a bending strength of 5.1 MPa. FTIR results confirmed enhanced C–S–H gel formation, indicating improved microstructural integrity. Carbon footprint analysis revealed that moderate SQS substitution significantly reduces embodied carbon. These findings support the use of quarry sludge as a viable component in eco-friendly HSC, with potential for further optimization and long-term durability studies. Full article

(This article belongs to the Section Composites Applications)

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11 pages, 464 KB

Open AccessArticle

Sex Estimation from Fragmented Thai Femora: Developing Segment-Specific Models Using Discriminant Function Analysis

by Chanasorn Poodendaen, Narawadee Choompoo, Kaemisa Srisen, Supapit Linlad, Jetniphat Chalermrerm, Worrawit Boonthai, Sitthichai Iamsaard, Nareelak Tangsrisakda, Supatcharee Arun and Suthat Duangchit

Forensic Sci. 2025, 5(4), 69; https://doi.org/10.3390/forensicsci5040069 (registering DOI) - 1 Dec 2025

Abstract

Background: Sex estimation from skeletal remains is important for forensic identification, but many methodologies focus on complete elements despite high fragmentation rates in operational contexts. The aim of this study was to develop and validate discriminant function equations for sex estimation between complete and fragmented Thai femora. Materials and Methods: A total of 560 adult femora (280 males and 280 females) were used for measurements of eight osteometric variables. Then, discriminant function analysis was applied to complete femora and anatomically isolated segments, including proximal, diaphyseal, and distal, with leave-one-out cross-validation. Results: All measurements showed significant sexual dimorphism, with percentage differences ranging from 6.56% to 42.27%. Complete femur stepwise analysis achieved 90.47% accuracy by using four optimally selected variables, performing comparably to eight-variable models. Isolated segment accuracies varied substantially: proximal segments achieved 89.64% accuracy, differing by only 0.83 percentage points from complete performance; distal segments demonstrated 86.25% accuracy from bicondylar width alone; and diaphyseal segments achieved 80.88%. Combined segment approaches demonstrated synergistic effects approaching complete femur performance. Conclusions: These population-specific equations provided validated methodologies for sex estimation from complete and fragmentary Thai femora. Anatomical region selection still maintained high classification accuracy despite skeletal incompleteness, in line with the fragmentary conditions commonly encountered in forensic and archeological contexts. Full article

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16 pages, 5189 KB

Open AccessArticle

Effects of Multiple Quenching Treatments on Microstructure and Hardness of O2, D2, and D3 Tool Steels

by Emanuele Ghio, Matteo Felci and Rinaldo Garziera

J. Manuf. Mater. Process. 2025, 9(12), 395; https://doi.org/10.3390/jmmp9120395 (registering DOI) - 1 Dec 2025

Abstract

The effects of multiple austenitizing and quenching (AQ) thermal cycles on the microstructure and hardness of AISI O2 (90MnCrV8), D2 (X153CrMoV12), and D3 (X210Cr13) tool steels were systematically investigated. Up to four consecutive AQ treatments were applied to assess the influence of repeated austenitization on grain refinement, carbide dissolution, martensitic transformation, and retained austenite. The microstructure was investigated by optical and SEM observations, supported with XRD analyses. The results were correlated with Rockwell and Vickers hardness measurements. In AISI O2, the mean austenitic grain size decreased from (6.5 ± 0.8) μm to (4.3 ± 0.4) μm, accompanied by an increase in hardness from ~800 HV1 to ~950 HV1 (63 HRC), mainly due to the progressive carbide dissolution and a reduction in retained austenite. In AISI D2 and D3, repeated AQ cycles led to a marked reduction in carbide size and volume fraction (up to 25%), with D2 showing partial coarsening beyond the third cycle and D3 exhibiting continuous dissolution owing to higher carbide stability. A linear correlation between the carbide volume fraction and Rockwell hardness was established. Compared with conventional single-step treatments, the multi-cycle AQ approach also promote spheroidization of small carbides. Full article

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12 pages, 1784 KB

Open AccessReview

Research on Wavefront Sensing Applications Based on Photonic Lanterns

by Zhengkang Zhao, Hangyu Zheng, Lianghua Xie, Jie Zhang, Zhuoyun Feng, Kaige Liu, Bin Zhu, Deen Wang, Ju Wang, Wei Liu and Qiang Yuan

Sensors 2025, 25(23), 7300; https://doi.org/10.3390/s25237300 (registering DOI) - 1 Dec 2025

Abstract

The Photonic Lantern (PL) is a novel fiber optic device emerging in wavefront sensing, which converts multimode fiber light fields into single-mode fields. By decomposing complex multimode fields into simple fundamental modes, the PL maps wavefront aberrations to light intensity. The Photonic Lantern Wavefront Sensor (PLWFS) functions as an ideal focal-plane sensor. It aligns the focal and imaging planes to coincide completely. This configuration mitigates Non-Common Path Aberrations (NCPAs), which traditional sensors struggle to resolve. This paper reviews the research history of the PLWFS. It first introduces the fabrication methods for PL, then focuses on illustrating the theoretical and experimental developments of the PLWFS. PLWFS research began with the initial realization of sensing simple tip/tilt aberrations, moved to establishing linear response models for small aberrations, and subsequently introduced methods such as neural network algorithms and broadband polychromatic light sources to achieve large aberration sensing and correction. This paper highlights significant research achievements from each stage, summarizes the current limitations in the research, and finally discusses the future potential of the PLWFS as an excellent focal-plane wavefront sensor. Full article

(This article belongs to the Special Issue Feature Review Papers in Optical Sensors)

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30 pages, 3695 KB

Open AccessArticle

Microbial Diversity of the Baikal Rift Zone Freshwater Alkaline Hot Springs and the Ecology of Polyextremophilic Dissimilatory Iron-Reducing Bacteria

by Anastasia I. Maltseva, Alexander G. Elcheninov, Alexandra A. Klyukina, Alexandra V. Gololobova, Elena V. Lavrentyeva, Tuyana G. Banzaraktsaeva, Vyacheslav B. Dambaev, Darima D. Barkhutova, Daria G. Zavarzina and Evgenii N. Frolov

Biology 2025, 14(12), 1716; https://doi.org/10.3390/biology14121716 (registering DOI) - 1 Dec 2025

Abstract

Polyextremophilic microbial communities of Baikal Rift Zone hot springs have been studied fragmentarily, and these studies have typically focused on either phototrophic microbial mats or on the whole microbial community from one or a few sites. In our work, we conducted the first large-scale screening of microbial communities from seven hot spring groups in the Baikal Rift Zone, using metabarcoding of the V3-V4 regions of the 16S rRNA gene. Analysis of alpha and beta diversity, as well as co-occurrence network analysis, revealed that the microbial diversity of the studied springs is highly dependent on temperature values. This approach allowed classifying microbial communities into four distinct groups, characterized by significantly different taxa representing the key functional roles of primary producers, heterotrophic consumers, and terminal destructors of organic matter. Sulfate-reducing bacteria constituted a major metabolic group driving the final stage of organic matter mineralization. Moreover, the presence of alkalithermophilic dissimilatory iron reducers, whose existence was debatable, was proved in the studied samples by cultural methods. The phylotypes that gained an advantage on selective media with synthesized ferrihydrite and hydrogen or acetate added as an electron donor belonged to the genus Parvivirga of the order Anaerosomatales and several unknown representatives of the phylum Bacillota. Full article

(This article belongs to the Special Issue Microbial Life at the Extremes: From Environmental Diversity to Functional Insights)

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19 pages, 5883 KB

Open AccessArticle

Pulse-Controlled Electrodeposition of Ni/ZrO₂ with Coumarin Additive: A Parametric Study

by Maria Myrto Dardavila and Constantina Kollia

Coatings 2025, 15(12), 1400; https://doi.org/10.3390/coatings15121400 (registering DOI) - 1 Dec 2025

Abstract

Ni/ZrO₂ composite coatings are increasingly employed, yet the influence of organic additives under a pulse current regime on their electrodeposition remains insufficiently addressed. This study investigates the combined effect of pulse frequency (0.01–100 Hz) and coumarin concentration (0–2 mmol L⁻¹) on the co-deposition behavior, microstructure, and properties of Ni/ZrO₂ coatings electrodeposited from a Watts-type bath. The structural, morphological, and compositional features were analyzed through SEM/EDS, FE-SEM, and XRD, while microhardness and surface roughness were determined to establish processing–structure–property correlations. The results revealed that coumarin acts as an effective levelling agent, promoting smoother and finer-grained coatings while modifying ZrO₂ incorporation and Ni crystallographic orientation. Increasing coumarin concentration led to a notable refinement of nickel crystallites and a rise in hardness, reaching values close to 650 HV under optimal PC conditions. Pulse frequency was found to strongly influence the microstructural characteristics and particle co-deposition rates, particularly at low frequencies, where a balance between additive adsorption and current modulation favored particle incorporation and enhanced the microhardness. It was demonstrated that the synergistic control of pulse parameters and coumarin concentration enables the design of Ni/ZrO₂ composite coatings with tailored microstructure, low roughness, and superior hardness for demanding applications. Full article

(This article belongs to the Section Surface Characterization, Deposition and Modification)

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23 pages, 3223 KB

Open AccessArticle

What Potential Does the Metaverse Hold for Overcoming Supply Chain Geopolitical Disruptions Through Scenario-Based Planning and Risk Management?

by Kamdem Poupi Arnold Brice, Aratrika De, Wiysenyuy Louis Nyuydzeran, Kamese Jordan Junior and Tagne Poupi Theodore Armand

Virtual Worlds 2025, 4(4), 55; https://doi.org/10.3390/virtualworlds4040055 (registering DOI) - 1 Dec 2025

Abstract

Geopolitical disruptions such as trade wars, sanctions, and political instability threaten global supply chain (SC) resilience. As a result, multinational corporations face financial losses, operational delays, and strategic uncertainties, creating an urgent demand for innovative risk management and scenario-planning strategies. Traditional risk management methods struggle to keep pace with the complexity of these events. This study explores the metaverse, combining VR, AR, digital twins, AI, and blockchain, as a tool for enhancing SC risk management. By enabling immersive scenario planning, real-time risk visualization, and collaborative decision-making, the metaverse supports agile and resilient supply chains. This research proposes a conceptual framework integrating key fourth industrial revolution (4IR) technologies to address geopolitical SC disruptions systematically. This model fosters digital preparedness, simulation-based learning, and adaptive coordination. While technological, organizational, and regulatory challenges persist, the study demonstrates that metaverse-enabled systems can support future-ready SC resilience strategies. Full article

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10 pages, 1593 KB

Open AccessArticle

Upcycling Medical Tablet Blister Waste into High-Performance Triboelectric Nanogenerators for Sustainable Energy Harvesting

by Vikram Lakshmi Suneetha, Velpula Mahesh, Khanapuram Uday Kumar and Rajaboina Rakesh Kumar

Nanoenergy Adv. 2025, 5(4), 19; https://doi.org/10.3390/nanoenergyadv5040019 (registering DOI) - 1 Dec 2025

Abstract

The increasing accumulation of medical waste, especially discarded pharmaceutical blister packs, poses both environmental risks and missed opportunities for resource recovery. In this work, we demonstrate, for the first time, the direct upcycling of tablet blister waste into a potential frictional layer in triboelectric nanogenerators (TENGs). The polymer structure of blister packs, combined with Silicone rubber as a counter frictional layer, enabled the fabrication of durable TENG devices (TS-TENGs). Systematic electrical testing revealed that the TS-TENG achieved an open-circuit voltage of approximately 300 V, a short-circuit current of about 40 μA, and a peak power density of 3.54 W/m² at an optimal load resistance of 4 MΩ. The devices maintained excellent stability over 10,000 mechanical cycles, confirming their durability. Practical demonstrations included powering 240 LEDs, four LED lamps, and portable electronic devices, such as calculators and hygrometers, through capacitor charging. This study shows that not only can tablet blister waste be used as a triboelectric material but it also presents a sustainable method to reduce pharmaceutical waste while advancing self-powered systems. The approach offers a scalable and low-cost means to integrate medical waste management with renewable energy technologies. Full article

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14 pages, 1118 KB

Open AccessArticle

Using Machine Learning to Identify Predictors of Heterogeneous Intervention Effects in Childhood Obesity Prevention

by Elizabeth Mannion, Kristine Bihrmann, Nanna Julie Olsen, Berit Lilienthal Heitmann and Christian Ritz

Data 2025, 10(12), 196; https://doi.org/10.3390/data10120196 (registering DOI) - 1 Dec 2025

Abstract

Obesity prevention interventions in children often produce small or null effects. However, ignoring heterogeneous responses may widen pre-existing inequalities. This secondary analysis explored baseline predictors of differential effects on BMI z-score, Fat mass (%), stress, and sleep outcomes in obesity-susceptible, healthy-weight children (n = 543). A modified LASSO regression was applied to baseline characteristics, including physical activity and socio-demographics. Few predictors were retained. For BMI z-score, weekly chores and parental divorce were the strongest predictors: children who did chores had a slightly larger increase in BMI z-score in the intervention group compared with controls (MD = 0.15, 95% CI: −0.03, 0.33), while children with divorced parents showed a smaller increase (MD = −0.19, 95% CI: −0.69, 0.31). These results align with evidence that low-intensity activity has limited impact on obesity outcomes and that children with compounded vulnerability may respond differently to tailored interventions. Even when overall effects are small, machine learning approaches can identify potential predictors of heterogeneous intervention effects, supporting the design of future targeted interventions aimed at reducing inequalities. Full article

(This article belongs to the Section Computational Biology, Bioinformatics, and Biomedical Data Science)

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28 pages, 3999 KB

Open AccessArticle

Microstructure Evolution and Phase Formation in WC-TiC-TaC-HfC(-ZrC) High-Entropy Carbide Systems During Mechanical Activation and Spark Plasma Sintering

by Igor Yu Buravlev, Aleksey O. Lembikov, Anton A. Belov, Saveliy M. Pisarev, Ekaterina A. Ponomareva, Erkhan S. Kolodeznikov, Nikita S. Ogorodnikov, Anastasia A. Buravleva, Aleksandr N. Fedorets, Oleg O. Shichalin and Eugeniy K. Papynov

J. Compos. Sci. 2025, 9(12), 647; https://doi.org/10.3390/jcs9120647 (registering DOI) - 1 Dec 2025

Abstract

In this study, medium- and high-entropy carbide systems with compositions WC-TiC-TaC-HfC and WC-TiC-TaC-HfC-ZrC were successfully synthesized via a combination of mechanical activation (using high-energy ball milling, HEBM) and spark plasma sintering (SPS) at 1900 °C. Investigation of the SPS consolidation kinetics revealed that both systems undergo single-stage active densification via a solid-state sintering mechanism within the temperature range of 1316–1825 °C. The introduction of ZrC into the five-component system led to a 22% decrease in the maximum shrinkage rate (from 0.9 to 0.7 mm·min⁻¹), which is attributed to the manifestation of a sluggish diffusion effect, characteristic of high-entropy systems. X-ray diffraction analysis of the consolidated samples confirmed the formation of predominantly single-phase high-entropy solid solutions (W-Ti-Ta-Hf)C and (W-Ti-Ta-Hf-Zr)C with a NaCl-type cubic structure (space group Fm-3m) and lattice parameters of 4.4101 Å and 4.4604 Å, respectively. Energy-dispersive X-ray spectroscopy revealed a near-equimolar distribution of metallic components with deviations not exceeding ±1.9 at. %. The addition of ZrC increased the average crystallite size by 84.3% (from 83.6 to 153.1 nm). Both systems achieved comparable relative densities of ~91.75%; however, they exhibited differences in hardness distribution: the four-component system is characterized by a higher average microhardness (1860 HV), while the five-component system exhibits a higher macrohardness HV30 (2008.1). The established correlations between composition, phase formation, microstructure, and properties provide a fundamental basis for the targeted design of high-entropy carbide ceramics with tailored characteristics for high-temperature applications. Full article

(This article belongs to the Section Composites Manufacturing and Processing)

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29 pages, 3769 KB

Open AccessSystematic Review

Illuminating Industry Evolution: Reframing Artificial Intelligence Through Transparent Machine Reasoning

by Albérico Travassos Rosário and Joana Carmo Dias

Information 2025, 16(12), 1044; https://doi.org/10.3390/info16121044 (registering DOI) - 1 Dec 2025

Abstract

As intelligent systems become increasingly embedded in industrial ecosystems, the demand for transparency, reliability, and interpretability has intensified. This study investigates how explainable artificial intelligence (XAI) contributes to enhancing accountability, trust, and human–machine collaboration across industrial contexts transitioning from Industry 4.0 to Industry 5.0. To achieve this objective, a systematic bibliometric literature review (LRSB) was conducted following the PRISMA framework, analysing 98 peer-reviewed publications indexed in Scopus. This methodological approach enabled the identification of major research trends, theoretical foundations, and technical strategies that shape the development and implementation of XAI within industrial settings. The findings reveal that explainability is evolving from a purely technical requirement to a multidimensional construct integrating ethical, social, and regulatory dimensions. Techniques such as counterfactual reasoning, causal modelling, and hybrid neuro-symbolic frameworks are shown to improve interpretability and trust while aligning AI systems with human-centric and legal principles, notably those outlined in the EU AI Act. The bibliometric analysis further highlights the increasing maturity of XAI research, with strong scholarly convergence around transparency, fairness, and collaborative intelligence. By reframing artificial intelligence through the lens of transparent machine reasoning, this study contributes to both theory and practice. It advances a conceptual model linking explainability with measurable indicators of trustworthiness and accountability, and it offers a roadmap for developing responsible, human-aligned AI systems in the era of Industry 5.0. Ultimately, the study underscores that fostering explainability not only enhances functional integrity but also strengthens the ethical and societal legitimacy of AI in industrial transformation. Full article

(This article belongs to the Special Issue Advances in Information Studies)

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17 pages, 7634 KB

Open AccessArticle

CLSM-Guided Imaging to Visualize the Depth of Effective Disinfection in Endodontics

by Rebecca Mattern, Sarah Böcher, Gerhard Müller-Newen, Georg Conrads, Johannes-Simon Wenzler and Andreas Braun

Antibiotics 2025, 14(12), 1201; https://doi.org/10.3390/antibiotics14121201 (registering DOI) - 1 Dec 2025

Abstract

Background/Objectives: Important goals of endodontic treatment procedures are to effectively eliminate microorganisms from the root canal system and prevent reinfection. Despite advances in techniques, these goals continue to be difficult to achieve due to the complex anatomy of the root canal system and bacterial invasion into the dentinal tubules of the surrounding root dentin. This pilot study aimed to refine a confocal laser scanning microscopy (CLSM) model with LIVE/DEAD staining to quantitatively assess the depth of effective disinfection by endodontic disinfection measures. Methods: Thirty caries-free human teeth underwent standardized chemo-mechanical root canal preparation and were inoculated with Enterococcus faecalis. Following treatment, CLSM-guided imaging with LIVE/DEAD staining allowed for differentiation between vital and dead bacteria and quantification of the depth of effective disinfection. Results: An average depth of bacterial eradication of 450 µm for conventional and 520 µm for sonically activated irrigation (EDDY) could be observed with significant differences (p < 0.05) in the coronal and medial positions. Conclusions: The results indicated that sonically activated irrigation (EDDY) provided a more homogeneous (omnidirectional) irrigation pattern compared to conventional irrigation. The study highlights the importance of effective disinfection strategies in endodontics, emphasizing the need for further research on the depth of effective disinfection of endodontic disinfection measures and the optimization of disinfection protocols. Full article

(This article belongs to the Special Issue Antibacterial Treatment in Periodontal and Endodontic Therapy, 3rd Edition)

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32 pages, 3739 KB

Open AccessArticle

Operational Flexibility Assessment of Distributed Reserve Resources Considering Meteorological Uncertainty: Based on an End-to-End Integrated Learning Approach

by Chao Gao, Bin Wei, Yabin Chen, Fan Kuang, Pei Yong and Zixu Chen

Processes 2025, 13(12), 3870; https://doi.org/10.3390/pr13123870 (registering DOI) - 1 Dec 2025

Abstract

In the context of the rapid development of renewable energy and frequent extreme weather, accurate evaluation of the backup operation flexibility of multiple distributed resources is a prerequisite for improving the resilience of power systems. However, it is difficult to consider the detailed model of each distributed resource and evaluate its regulation ability in the operation of power systems because of the small number of distributed resources. Therefore, this paper first quantifies the capacity boundaries of distributed reserve resources on the power generation, load, and energy storage sides under different meteorological conditions through economic self-dispatching optimization and Minkowski aggregation methods. Subsequently, the maximum correlation–minimum redundancy (mRMR) principle and Granger causality test are combined to reduce the dimensionality of high-dimensional meteorological features. Finally, the stacking ensemble learning method is introduced to build an end-to-end modelling framework from multi-source weather input to reserve capability prediction. The results show that (1) the reserve capacity of multivariate distributed resources has significant intra-day and intra-day periodicity and seasonal differences; (2) the mRMR algorithm considering the Granger causality test can capture the correlation and causality between high-dimensional meteorological features and reserve capabilities, and the obtained features are more explanatory; (3) the average R² of the stacking model in both upper-reserve and lower-reserve predictions reaches 0.994. In terms of computational efficiency, the training time of the proposed model is 130.85 s for upper-reserve prediction and 133.71 s for lower-reserve prediction, which is significantly lower than that of conventional hybrid models while maintaining stable performance under extreme meteorological conditions such as high temperatures and strong winds; (4) compared with integration methods such as simple averaging and error weighting, the stacking integration strategy proposed in this paper remains stable in the mean and variance of prediction results, verifying its comprehensive advantages in structural design and performance integration. Full article

(This article belongs to the Special Issue Modeling, Optimization, and Control of Distributed Energy Systems)

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44 pages, 10191 KB

Open AccessArticle

Hyperspectral Imaging and Machine Learning for Automated Pest Identification in Cereal Crops

by Rimma M. Ualiyeva, Mariya M. Kaverina, Anastasiya V. Osipova, Alina A. Faurat, Sayan B. Zhangazin and Nurgul N. Iksat

Biology 2025, 14(12), 1715; https://doi.org/10.3390/biology14121715 (registering DOI) - 1 Dec 2025

Abstract

The spectral characteristics of harmful insect pests in wheat fields were characterised using hyperspectral imaging for the first time. The analysis of spectral profiles revealed that reflectance is determined by the structure of the insect’s chitin and the colouration of its body surface. Insects with lighter or more vivid colours (white, yellow, or green) showed higher reflectance values compared to those with predominantly dark pigmentation. Reflectance was also influenced by the presence of wings, surface roughness, and the age of the insect. Each species exhibited distinct spectral patterns that allowed for differentiation not only from other insect species but also from the plant background. A classification model using PLS-DA was developed and demonstrated high accuracy in identifying 12 pest species, confirming the strong potential of hyperspectral imaging for species-level classification. The results validate the PLS-DA method for differentiating insects based on spectral characteristics and underscore the reliability of this approach for automated monitoring systems to detect phytophagous pests in crop fields. This technology could reduce insecticide use by 30–40% through targeted application. The research has both scientific and economic significance, laying the groundwork for integrating machine learning and computer vision into agricultural monitoring. It supports the advancement of precision farming and contributes to improved global food security. Full article

(This article belongs to the Section Bioinformatics)

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