Search Results (539)

Accurate segmentation of fruit tree leaf diseases is critical for yield protection and precision crop management, yet it is challenging due to complex field conditions, irregular leaf morphology, and diverse lesion patterns. To address these issues, Disease-Seg, a lightweight real-time segmentation framework, is proposed. It integrates CNN and Transformer with a parallel fusion architecture to capture local texture and global semantic context. The Extended Feature Module (EFM) enlarges the receptive field while retaining fine details. A Deep Multi-scale Attention mechanism (DM-Attention) allocates channel weights across scales to reduce redundancy, and a Feature-weighted Fusion Module (FWFM) optimizes integration of heterogeneous feature maps, enhancing multi-scale representation. Experiments show that Disease-Seg achieves 90.32% mIoU and 99.52% accuracy, outperforming representative CNN, Transformer, and hybrid-based methods. Compared with HRNetV2, it improves mIoU by 6.87% and FPS by 31, while using only 4.78 M parameters. It maintains 69 FPS on 512 × 512 crops and requires approximately 49 ms per image on edge devices, demonstrating strong deployment feasibility. On two grape leaf diseases from the PlantVillage dataset, it achieves 91.19% mIoU, confirming robust generalization. These results indicate that Disease-Seg provides an accurate, efficient, and practical solution for fruit leaf disease segmentation, enabling real-time monitoring and smart agriculture applications. Full article

Słowiński National Park is one of the 23 national parks in Poland and one of the two situated on the Baltic Coast in the country. It was established in 1967 to protect the most valuable ecosystems: coastal lakes, marshes, peat bogs, meadows, forests, and, above all, the dune belt of the Łebska Spit with its unique moving dunes. We aimed to 1. determine the species diversity and structure of thrips assemblages in the most important biotopes of the Park; 2. determine the geographical distribution and food preferences of thrips species; and 3. determine which environmental factors influence the diversity of insect assemblages and which thrips species distinguish these assemblages. The method used in the quantitative research was based on the use of a scoop method; it was supplemented by qualitative research (shaking branches of trees and searching for insects on their host plants). The studies were carried out in 1991 and 1999–2001 in fourteen plant associations. A total of 90 thrips species (nearly 40% of the Polish fauna) were recorded, including 71 in quantitative and 74 in qualitative samples. The study also revealed a significant correlation between the thrips assemblage composition and the following environmental factors: soil moisture, light intensity, general nutrient availability, and soil salinity. In addition, the thrips species with the most significant impact on assemblage composition were identified. The relatively high number of species found, including Taeniothrips zurstrassenii Zawirska, a species new to science, and others rarely recorded in Poland, highlights the value of the SNP habitat diversity in maintaining high Thysanoptera diversity. Full article

Accurate and efficient surface water quality monitoring is essential for ecological protection and sustainable development. However, conventional monitoring methods, such as fixed-site observations, often suffer from spatial limitations and overlook crucial auxiliary variables. This study proposes an innovative modeling framework for large-scale river water quality inversion that integrates multi-source data—including Sentinel-2 imagery, meteorological conditions, land use classification, and landscape pattern indices. To improve predictive accuracy, three tree-based machine learning models (Random Forest, XGBoost, and LightGBM) were constructed and further optimized using the Whale Optimization Algorithm (WOA), a nature-inspired metaheuristic technique. Additionally, model interpretability was enhanced using SHAP (Shapley Additive Explanations), enabling a transparent understanding of each variable’s contribution. The framework was applied to the Red River Basin (RRB) to predict six key water quality parameters: dissolved oxygen (DO), ammonia nitrogen (NH₃-N), total phosphorus (TP), total nitrogen (TN), pH, and permanganate index (COD_Mn). Results demonstrate that integrating landscape and meteorological variables significantly improves model performance compared to remote sensing alone. The best-performing models achieved R² values exceeding 0.45 for all parameters (DO: 0.70, NH₃-N: 0.46, TP: 0.59, TN: 0.71, pH: 0.83, COD_Mn: 0.57). Among them, WOA-optimized LightGBM consistently delivered superior performance. The study also confirms the feasibility of applying the models across the entire basin, offering a transferable and interpretable approach to spatiotemporal water quality prediction in other large-scale or data-scarce regions. Full article

Balsam fir is an important specialty horticultural crop in eastern North America and commonly harvested for use as Christmas trees. Postharvest quality is a major challenge for producers, who are particularly concerned about postharvest needle retention. It was hypothesized that pyroligneous acid (PA) would help increase postharvest needle retention in balsam fir when supplied via xylem or foliage. This project first identified foliar spraying as the best application method, then designed a multivariate experiment with two factors. The first factor was foliar treatment (control, water, 1% PA, 2% PA, and 4% PA). The second factor was time, where branches were evaluated for needle abscission at 0, 2, 4, 6, and 8 weeks after harvest. The experiment was replicated 5 times and needle abscission, water uptake, chlorophyll, carotenoids, flavonoids, total phenolics, membrane injury, proline, and H₂O₂ production were all measured in response. Postharvest abscission reached 100% over the 8-week experiment and water uptake decreased by over 80%. Chlorophyll, proline, membrane injury, and H₂O₂ production all increased over time. Although PA did not improve needle retention compared to the control under the tested conditions, 4% PA spray increased proline concentration by 40% while decreasing membrane injury by 26%. Ultimately, PA did not consistently improve needle retention but did induce proline accumulation and membrane protection. Full article

Hermit beetles (Osmoderma spp.) are protected and endangered across Europe, experiencing ongoing decline throughout most of their range. Because nearly all populations are small and isolated, Osmoderma genus is highly susceptible to extinction and requires active conservation measures. The primary cause of decline in the genus is habitat loss, particularly the removal of hollow trees that provide essential larval habitat. The nutritional wood mold within these hollows, on which larvae depend for 3–4 years of development, is directly linked to population survival. The aim of this study was to develop methodical ex situ breeding guidelines for reintroduction designed to eliminate environmental constraints and ecological requirement gaps. We first synthesize literature-based evidence on habitat conditions, applied methods, study durations, and key ecological insights relevant to Osmoderma conservation. Based on these results, we then create an ex situ breeding guideline for reintroduction, combining published data with practical breeding objectives in cases where empirical data are limited. Full article

Hilly and mountainous areas are important agricultural production regions globally. Their dramatic topography, dense fruit tree planting, and steep slopes severely restrict the application of traditional plant protection machinery. Pest and disease control has long relied on manual spraying, resulting in high labor intensity, low efficiency, and pesticide utilization rates of less than 30%. Plant protection UAVs, with their advantages of flexibility, high efficiency, and precise application, provide a feasible technical approach for plant protection operations in hilly and mountainous areas. However, steep slopes and dense orchard environments place higher demands on key technologies such as drone positioning and navigation, attitude control, trajectory planning, and terrain following. Achieving accurate identification and adaptive following of the undulating fruit tree canopy while maintaining a constant spraying distance to ensure uniform pesticide coverage has become a core technological bottleneck. This paper systematically reviews the key technologies and research progress of plant protection UAVs in hilly and mountainous operations, focusing on the principles, advantages, and limitations of core methods such as multi-sensor fusion positioning, intelligent SLAM navigation, nonlinear attitude control and intelligent control, three-dimensional trajectory planning, and multimodal terrain following. It also discusses the challenges currently faced by these technologies in practical applications. Finally, this paper discusses and envisions the future of plant protection UAVs in achieving intelligent, collaborative, and precise operations on steep slopes and in dense orchards, providing theoretical reference and technical support for promoting the mechanization and intelligentization of mountain agriculture. Full article

Hazard impacts are increasing in complexity as the world population grows. No universal strategies are available to minimize or eliminate the impacts of all scenarios. In this paper, a priority scenario-based strategy methodology is proposed using a Decision Tree (DT) machine learning tool. This approach identifies the parameters and combinations that contribute to high impact and loss from a hazard event conditioned on a priority scenario. The method is applied to a local water distribution network under seismic hazards. The priority scenarios in this study are vulnerability (VPS), damage (DPS), and cost (CPS). Each priority scenario identifies different affected areas. Some areas were repeatedly affected in different priority scenarios, showing an overlap of effects and making them a high crucial priority. Based on the analysis, a priority-based map was generated, highlighting areas that should be given priority for restoration or protection. The DTs were compared with other ML tools and Tree-based models to ascertain the best tool that determines the affected parameters. Competition tests compared the results from the ML tools and showed acceptable predictions; however, the DT was demonstrated to be the most ideal tool for this proposed method, showing an r² of 0.6745, 0.9259, and 0.7343 for VPS, DPS, and CPS, respectively. Full article

The Internet of Medical Things (IoMT) interconnects medical devices, software applications, and healthcare services through the internet to enable the transmission and analysis of health data. IoMT facilitates seamless patient care and supports real-time clinical decision-making. The IoMT faces substantial security threats due to limited device resources, high device interconnectivity, and a lack of standardization. In this paper, we present an Intrusion Detection System (IDS) called An Empirical Distribution Ranking-Based Fuzzy J48 Classifier for Multiclass Intrusion Detection in IoMT Networks (EDR-FJ48) to distinguish between regular traffic and multiple types of security threats. The proposed IDS is built upon the J48 decision tree algorithm and is designed to detect a wide range of attacks. To ensure the protection of medical devices and patient data, the system incorporates a fuzzy IF-THEN rule inference module. In our approach, fuzzy rules are formulated based on the fuzzified values of selected features, which capture the statistical behavior of the input observations. These rules enable interpretable and transparent decision-making and are applied before the final classification step. We thoroughly evaluated our methodology through extensive simulations using three publicly available datasets, such as WUSTL-EHMS-2020, CICIoMT2024, and ECU-IoHT. The results exhibit exceptional accuracy rates of 99.68%, 98.71%, and 99.43%, respectively. A comparative analysis against state-of-the-art models in the existing literature, based on metrics including accuracy, precision, recall, F1-score, and time complexity, reveals that our proposed method achieves superior results. This evidence suggests that our method constitutes a robust solution for mitigating security threats in IoMT networks. Full article

Lupin (Lupinus spp.) is increasingly incorporated into processed foods as a gluten-free ingredient and alternative protein source, but it is also a regulated allergen in the European Union due to cross-reactivity with other legumes, especially peanut. Reliable methods for detecting undeclared lupin traces in complex food matrices are therefore essential for consumer protection. In this study, a loop-mediated isothermal amplification (LAMP) assay was developed for rapid and sensitive detection of lupin DNA. Several nuclear and chloroplast regions were evaluated for primer design, and gene encoding the Lup a 1 allergen was selected as the optimal target. Amplification was monitored by real-time fluorescence, agarose gel electrophoresis, and visual colorimetry. The selected primer set achieved a detection limit of 25 pg of lupin DNA and consistently detected lupin in binary mixtures down to 10 mg/kg, with no cross-reactivity against closely related legumes or tree nuts. Application to processed foods confirmed detection in products declaring lupin and revealed potential undeclared presence in some commercial samples. Colorimetric detection provided reliable results comparable to real-time monitoring, enabling simple readouts without specialized equipment. Overall, the developed LAMP assay represents a rapid, specific, and sensitive alternative to PCR-based methods for allergen monitoring and food safety management. Full article

In the context of the rapid development of Consumer Internet of Things (CIoT), the manipulation and unauthorized distribution of multimedia content have raised serious concerns regarding copyright protection and data authenticity. Ensuring secure traceability and authenticity in complex network environments remains a major challenge. Traditional blockchain mechanisms often suffer from high latency during large-scale data queries, making them unsuitable for real-time CIoT applications. To address this, this paper proposes a high-efficiency blockchain-based multimedia data security traceability method. First, blockchain is integrated with the PROV model to guarantee operation transparency and data credibility. Second, a joint index structure comprising a fast index (using traceability positioning tables for cross-block jumps) and a multi-bucket index (using self-balancing binary trees) is designed. Experimental results demonstrate that compared to traditional blockchain and SEBDB methods, the proposed mechanism remains stable as data volume exceeds 2000 records. Specifically, the query time growth rate is significantly lower than linear scanning methods (12–18% vs. 45–62% for traditional methods), and the number of traversed records is reduced by 60–75% by avoiding full-chain traversal, verifying the method’s superiority in handling high-frequency CIoT multimedia queries while providing protection against tampering and unauthorized distribution. Full article

In the context of privacy-preserving face recognition systems, entropy plays a crucial role in determining the efficiency and security of computational processes. However, existing schemes often encounter challenges such as inefficiency and high entropy in their computational models. To address these issues, we propose a privacy-preserving face recognition method based on the Face Feature Coding Method (FFCM) and symmetric homomorphic encryption, which reduces computational entropy while enhancing system efficiency and ensuring facial privacy protection. Specifically, to accelerate the matching speed during the authentication phase, we construct an N-ary feature tree using a neural network-based FFCM, significantly improving ciphertext search efficiency. Additionally, during authentication, the server computes the cosine similarity of the matched facial features in ciphertext form using lightweight symmetric homomorphic encryption, minimizing entropy in the computation process and reducing overall system complexity. Security analysis indicates that critical template information remains secure and resilient against both passive and active attacks. Experimental results demonstrate that the facial authentication efficiency with FFCM classification is 4% to 6% higher than recent state-of-the-art solutions. This method provides an efficient, secure, and entropy-aware approach for privacy-preserving face recognition, offering substantial improvements in large-scale applications. Full article

Steep olive orchards in northwest Syria are experiencing severe land degradation as a result of unsustainable uphill–downhill tillage, which accelerates erosion and reduces productivity. To address this problem, three tillage systems, no-till natural vegetation strips (NVSs), contour tillage, and uphill–downhill tillage, were evaluated at two research sites, Yakhour and Tel-Hadya, NW Syria. The adoption of no-till NVSs significantly increased soil organic matter (SOM) at both sites, outperforming uphill–downhill tillage. While contour tillage resulted in lower SOM levels than NVSs, it still performed better than the conventional uphill–downhill practice. Contour soil flux (CSF) was lower in Yakhour, where mule-drawn tillage on steep slopes (31–35%) was practiced, compared to higher CSF values in Tel-Hadya, where tractor tillage was applied on gentler slopes (11–13%), which highlights the influence of slope steepness on soil fluxes. Over four years, net soil flux (NSF) indicated greater soil loss under tractor tillage, confirming that mule-drawn tillage is less disruptive. Olive trees with no-till NVSs benefited from protected root systems, improved soil structure through SOM accumulation, reduced erosion risk, and improved surface runoff buffering, which resulted in increased water infiltration and soil water retention. This study was carried out using a participatory technology development (PTD) framework, which guided the entire research process, from diagnosing problems to co-designing, field testing, and refining soil conservation practices. In Yakhour, farmers actively identified the challenges of degradation. They collaboratively chose no-till natural vegetation strips (NVSs) and contour tillage as key interventions, valuing NVSs for their ability to conserve moisture, suppress weeds and pests, and increase olive productivity. The farmer–scientist co-learning network positioned PTD not only as an outreach tool but also as a core research method, enabling locally relevant and scalable strategies to restore soil functions and combat land degradation in northwest Syria’s hilly olive orchards. Full article

To address the high computational and communication overheads and the limited edge security found in many existing batch verification methods for Mobile Edge Computing (MEC), this paper presents a blockchain-based batch authentication and symmetric group key agreement protocol. A core feature of this protocol is the establishment of a shared symmetric key among all authenticated participants. This symmetry in key distribution is fundamental for enabling secure and efficient broadcast or multicast communication within the MEC group. The protocol introduces a chameleon hash function built on elliptic curves, allowing smart mobile devices (SMDs) to generate lightweight signatures. The edge server (ES) then performs efficient large-scale batch authentication using an aggregate signature technique. Considering the need for secure and independent communication between SMDs and ES, the protocol further establishes a one-to-one session key agreement mechanism and uses a Merkle tree to verify session key correctness. Formal verification with ProVerif2.05 tool confirms the protocol’s security and multiple protection properties. Experimental results show that, compared with the CPPBA, ECCAS, and LBVP schemes, the protocol improves computational efficiency of batch authentication by 0.94%, 67.20%, and 49.53%, respectively. For group key agreement, the protocol achieves a 35.26% improvement in computational efficiency over existing schemes. Full article

Cybersecurity is a field in which integration of artificial intelligence (AI) represents a significant direction towards protection against cyber threats. This scoping review explores the current impact and future prospects of AI in four key areas of cybersecurity: threat detection, endpoint security, phishing and fraud detection, and network security. The main goal was to answer the research question, ‘Is AI an effective method to enhance current infrastructures’ cybersecurity?’ Method: Through the PRISMA-ScR protocol, 2548 records were identified from the Google Scholar database from January 2020 to April 2025. The following search terms were used to identify available literature: “Artificial Intelligence Cybersecurity”, “Machine Learning Cybersecurity”, “Cybersecurity Innovation AI”, “AI Future Perspective Cybersecurity”, “Machine Learning Innovation Cybersecurity”. The search only included articles in English. No grey literature has been included. Articles with a focus on performance optimization, cost analysis and business models without a focus on privacy and security have been discarded. Results: The impact and performance of AI algorithms have been highlighted through a selection of 20 articles. Both Machine Learning and Neural Network methods have been employed in the literature, with Decision Trees and Random Forest being the most common approaches. Discussion: The main common limitations of the analyzed articles have been discussed, highlighting possible future directions of research to tackle them. Conclusions: Despite the evidenced limitations, AI showed promising results in improving cybersecurity, especially concerning cyber attack detection and classification, with methods able to grant very high accuracy and trustworthiness. Full article

In recent years, Polish producers have been increasingly interested in organic fruit production. In this cultivation system, it is very important to choose cultivars that are less susceptible to diseases and pests. In research conducted in the years 2009–2019 in central Poland, the suitability of four sour cherry cultivars (‘Kelleris 16’, ‘Oblacinska’, ‘Debreceni Bötermö’, ‘Pandy 103’) for organic cultivation was assessed. The trees grew in two separate experimental quarters: conventional and organic, about 1 km apart. It was proved that organic sour cherry cultivation is possible, but there are many challenges. In the organic cultivation system, trees were more sensitive to low temperatures and grew and yielded less than those grown using conventional methods. The weaker growth and lower yields were mainly due to the ineffective protection against cherry leaf spot and brown rot. The fruit quality was closely dependent on the weather conditions. The fruit harvested in the organic orchard had a lower weight but tended to be firmer than that harvested in the conventional one. The smallest, but most abundant in soluble solids, were the fruits of the ‘Oblacinska’ cultivar. Unfortunately, they were infested by larvae of cherry fruit flies. Occasionally, the pest larvae lived in organic sour cherries of the ‘Pandy 103’ and ‘Debreceni Bötermö’ cultivars. In years with high rainfall, 20 to 35% of the fruit in the organic quarter was affected by fungal diseases. Full article