Search Results (172)

In forests dominated by deciduous tree species, coniferous species are often disproportionately important because of their contrasting functional traits. Eastern white pine (Pinus strobus L.), once a widespread emergent canopy species, co-occurs with deciduous hardwoods in the northern Lake States, but is often uncommon in contemporary hardwood stands. To gain insights into the potential utility of hardwood management strategies for simultaneously regenerating white pine, we leveraged a northern hardwood silvicultural experiment with scattered overstory pine. Seven growing seasons post-harvest, we conducted a complete census of white pine regeneration (height ≥ 30 cm) and mapped their locations and the locations of potential seed trees. Pine regeneration was sparse and strongly spatially aggregated, with most clusters falling within potential seed shadows of overstory pines. New recruits were found to have the highest density in a scarified portion of the study area leeward of potential seed trees. Low regeneration densities within treatment units, strong spatial aggregation, and the spatial arrangement of potential seed trees precluded generalizable inferences regarding the utility of specific treatment combinations. Nevertheless, our results underscore the critical importance of residual overstory pines as seed sources and highlight the challenges associated with realizing their potential in managed northern hardwoods. Full article

Fall detection is a critical task in healthcare monitoring systems, especially for elderly populations, for whom timely intervention can significantly reduce morbidity and mortality. This study proposes a privacy-preserving and scalable fall-detection framework that integrates federated learning (FL) with transfer learning (TL) to train deep learning models across decentralized data sources without compromising user privacy. The pipeline begins with data acquisition, in which annotated video-based fall-detection datasets formatted in YOLO are used to extract image crops of human subjects. These images are then preprocessed, resized, normalized, and relabeled into binary classes (fall vs. non-fall). A stratified 80/10/10 split ensures balanced training, validation, and testing. To simulate real-world federated environments, the training data is partitioned across multiple clients, each performing local training using pretrained CNN models including MobileNetV2, VGG16, EfficientNetB0, and ResNet50. Two FL topologies are implemented: a centralized server-coordinated scheme and a ring-based decentralized topology. During each round, only model weights are shared, and federated averaging (FedAvg) is applied for global aggregation. The models were trained using three random seeds to ensure result robustness and stability across varying data partitions. Among all configurations, decentralized MobileNetV2 achieved the best results, with a mean test accuracy of 0.9927, F1-score of 0.9917, and average training time of 111.17 s per round. These findings highlight the model’s strong generalization, low computational burden, and suitability for edge deployment. Future work will extend evaluation to external datasets and address issues such as client drift and adversarial robustness in federated environments. Full article

In an era marked by the rapid growth of the Internet of Things (IoT), network security has become increasingly critical. Traditional Intrusion Detection Systems, particularly signature-based methods, struggle to identify evolving cyber threats such as Advanced Persistent Threats (APTs)and zero-day attacks. Such threats or attacks go undetected with supervised machine-learning methods. In this paper, we apply K-means clustering, an unsupervised clustering technique, to a newly created modern network attack dataset, UWF-ZeekDataFall22. Since this dataset contains labeled Zeek logs, the dataset was de-labeled before using this data for K-means clustering. The labeled data, however, was used in the evaluation phase, to determine the attack clusters post-clustering. In order to identify APTs as well as zero-day attack clusters, three different labeling heuristics were evaluated to determine the attack clusters. To address the challenges faced by Big Data, the Big Data framework, that is, Apache Spark and PySpark, were used for our development environment. In addition, the uniqueness of this work is also in using connection-based features. Using connection-based features, an in-depth study is done to determine the effect of the number of clusters, seeds, as well as features, for each of the different labeling heuristics. If the objective is to detect every single attack, the results indicate that 325 clusters with a seed of 200, using an optimal set of features, would be able to correctly place 99% of attacks. Full article

Rapeseed planter application in simultaneous fertilization faces extensive mode and poor precision challenges. In this study, we attempt to mitigate these problems by combining high-precision radar speed sensors and fertilization parameters with the relation between the rotation speed and the fertilizer amount of the fertilizer discharge apparatus. We designed a fertilization control system based on a high-precision radar sensor. The mathematical model of the permanent magnetic direct-current motor was constructed, and the transfer function of the control link was determined. Using the regulation toolbox in MATLAB 2020-PD, the proportional-derivative (PD) control parameters were determined. Finally, the tests were performed to validate the performance of the designed fertilization control system. The relation between the actual fertilizer discharge amount and the target value was used for evaluation. The relative errors of 2.82, 2.67, and 3.43% were obtained between the target fertilizer application rates and the actual rates in constant-speed fertilization, pave variable-speed, and field tests, respectively. They fall within the acceptable range, proving that the developed system satisfied the fertilization quality requirements and showed high control precision. The present research results can provide a theoretical reference for simultaneous rapeseed seeding and variable fertilization. Full article

Long-term no-till systems have led to shifts in weed communities and reduced the effectiveness of herbicide-based control. Occasional tillage is proposed as an alternative strategy to disrupt weed emergence patterns by redistributing seeds within the soil profile. This study aimed to evaluate the impact of occasional tillage on weed seedbank composition and vertical distribution of viable weed seeds and propagules within the soil profile, after more than 20 years of continuous no-till. A paired-plot experiment was conducted in Carlos Casares, Buenos Aires, Argentina, with three replications. Treatments included continuous no-till and occasional tillage (two disk harrow passes in August 2022 and April 2023) combined with three soil depths (0–5, 5–10, and 10–15 cm). Soil samples were collected in spring 2022 and fall 2023, and weed emergence was recorded under semi-controlled conditions. Overall species richness did not differ significantly between tillage treatments but was consistently greater in the upper 0–5 cm soil layer. Weed abundance also declined with depth. Five species, Chenopodium album, Stellaria media, Eleusine indica, Oxybasis macrosperma, and Heliotropium curassavicum, were frequent across treatments. Some species were exclusive to either no-till or tilled plots, for example, Datura ferox, Poa annua, and Veronica peregrina were found only in tilled plots, while Portulaca oleracea, Medicago lupulina, and Trifolium repens were exclusive to no-till plots. These results indicate that occasional tillage alters species composition and vertical seed distribution in the seedbank without significantly reducing total richness or abundance, offering an additional, but not always effective, tool to influence weed community structure in no-till systems. Full article

In the first part of this study, Y₂O₃-doped copper thiocyanate (CuSCN) with different x wt% (named CuSCN-xY, x = 0, 1, 2, and 3) films were synthesized onto ITO substrates using the spin coating method. UV-vis, SEM, AFM, EDS, and cyclic voltammetry were used to investigate the material properties of the proposed films. The conductivity and carrier mobility of the films increased with additional yttrium doping. It was found that the films with 2% Y₂O₃ (CuSCN-2Y) have the smallest valence band edges (5.28 eV). Meanwhile, CuSCN-2Y films demonstrated the densest surface morphology and the smallest surface roughness (22.8 nm), along with the highest conductivity value of 764 S cm⁻¹. Then, P-I-N self-powered UV photodetectors (PDs) were fabricated using the ITO substrate/ZnO seed layer/ZnO nanorod/CsPbBr₃/CuSCN-xY/Ag structure, and the characteristics of the devices were measured. In terms of response time, the rise time and fall time were reduced from 26 ms/22 ms to 9 ms/5 ms; the responsivity was increased from 243 mA/W to 534 mA/W, and the on/off ratio was increased to 2.47 × 10⁶. The results showed that Y₂O₃ doping also helped improve the P-I-N photodetector’s device performance, and the mechanisms were investigated. Compared with other published P-I-N self-powered photodetectors, our proposed devices show a fairly high on/off ratio, quick response times, and high responsivity and detectivity. Full article

Cotton is a vital economic crop in global agriculture and the textile industry, contributing significantly to food security, industrial competitiveness, and sustainable development. Traditional technologies such as spectral imaging and machine learning improved cotton cultivation and processing, yet their performance often falls short in complex agricultural environments. Deep learning (DL), with its superior capabilities in data analysis, pattern recognition, and autonomous decision-making, offers transformative potential across the cotton value chain. This review highlights DL applications in seed quality assessment, pest and disease detection, intelligent irrigation, autonomous harvesting, and fiber classification et al. DL enhances accuracy, efficiency, and adaptability, promoting the modernization of cotton production and precision agriculture. However, challenges remain, including limited model generalization, high computational demands, environmental adaptability issues, and costly data annotation. Future research should prioritize lightweight, robust models, standardized multi-source datasets, and real-time performance optimization. Integrating multi-modal data—such as remote sensing, weather, and soil information—can further boost decision-making. Addressing these challenges will enable DL to play a central role in driving intelligent, automated, and sustainable transformation in the cotton industry. Full article

Seed propagation is the primary means of reproducing many native and endemic species, including garberia [Garberia heterophylla (W. Bartram) Merrill & F. Harper]. This attractive pollinator plant, native to Florida, is scarcely found in nursery production and largely unknown to the gardening community. To better understand the seed biology of garberia, a series of experiments were conducted to evaluate the effects of population on seed viability and germination response to four seasonal temperatures, as well as the effects of time on seed storability. Initial seed viability was 49% and 60% for Central and North Florida populations, respectively. Seeds germinated readily, indicating non-dormancy, with significant effects of population and temperature observed. Overall, on day 28, a greater germination proportion was observed from seeds collected from North Florida than Central Florida across temperatures, except for winter (11/22 °C), where responses were similar. The greatest germination proportion for seeds collected from North Florida was observed at 15/27 °C (fall) and 19/29 °C (spring), whereas the greatest germination from Central Florida was observed at 11/22 °C (winter), with the steepest decline observed at summer temperatures (24/33 °C). Further, it was observed that garberia seeds are intolerant of long-term storage, losing viability as early as 3 months under conventional cold or room temperature storage and decreasing substantially more after 6 months. These findings contribute to the overall understanding of the seed biology of underutilized species such as garberia, key to the development of efficient and reliable propagation systems for our nursery industry. Full article

The main objective of energy balance analysis is to guide farmers in making informed decisions that promote the efficient management of natural resources, optimise the use of agricultural inputs, and improve the overall economic performance of their farms. In addition, it supports the adoption of sustainable agricultural practices, such as crop diversification, the use of renewable energy sources, and the recycling of agricultural by-products and residues into natural energy sources or fertilisers. This paper analyses the variation in energy efficiency between 2019 and 2022 of the main crops in Angola: maize, soybean, and rice, and the forest production of eucalyptus biomass in agroforestry farms. The research was based on the responses to interviews conducted with the managers of the farms regarding the machinery used, fuels and lubricants, labour, seeds, phytopharmaceuticals, and fertilisers. The quantities are gathered by converting data into Megajoules (MJ). The results show variations in efficiency and energy balance. In corn, efficiency fluctuated between 1.32 MJ in 2019 and 1.41 MJ in 2020, falling to 0.94 MJ in 2021 due to the COVID-19 pandemic before rising to 1.31 MJ in 2022. For soybeans, the energy balance went from a deficit of −8223.48 MJ in 2019 to a positive 11,974.62 MJ in 2022, indicating better use of resources. Rice stood out for its high efficiency, reaching 81,541.33 MJ in 2021, while wood production showed negative balances, evidencing the need for more effective strategies. This research concludes that understanding the energy balance of agricultural operations in Angola is essential not only to achieve greater sustainability and profitability but also to strengthen the resilience of agricultural systems against external factors such as climate change, fluctuations in input prices, and economic crises. A comprehensive understanding of the energy balance allows farmers to assess the true cost-effectiveness of their operations, identify energy inefficiencies, and implement more effective strategies to maximise productivity while minimising environmental impacts. Full article

This study was carried out with the aim of understanding how farmers perceive and deal with fall armyworm (FAW) infestations in maize crops. Data based on a questionnaire were collected across nine districts of the Punjab province in Pakistan. Findings revealed that about 38% of farmers had medium-sized landholdings and had been cultivating maize crop for the last 11–20 years. Hybrid maize seed was used by 55% of the farmers, and 60% of the maize grown was used as fodder, as well as a cash crop. Surprisingly, only 39% of farmers were able to correctly identify FAW; however, the majority (72%) recognized the larvae as the most damaging stage. Although most of the farmers (71%) grew maize crops during both seasons, only 34% of the farmers recognized autumn as the peak infestation period of FAW. However, despite limited awareness, a high percentage (86%) of farmers managed FAW effectively using various chemical treatments. This study also highlighted the influence of some factors on farmers’ perception of FAW such as: age, farming experience, and maize cultivation practices. Overall, the findings emphasize the need for increased awareness of the basic biology and targeted management strategies for FAW to safeguard maize crops in the region. Full article

Kentucky bluegrass (KBG) has poor seed establishment in the fall when used as a perennial groundcover in corn production. This study was conducted to investigate the effect of various seed treatments and soil amendments on the establishment of KBG under drought and non-drought conditions, simulated in a growth chamber. The effect of seed treatments, soil amendments, and irrigation frequency on KBG germination and shoot dry weight were measured over 21 days in a controlled environment at 21 °C, 50% RH, and exposure to a constant red light. The treatments were the Hydroloc seed treatment, a lime soil amendment, the Pivot Bio seed treatment, an ammonium nitrate soil amendment, a gibberellic acid seed treatment, osmotic seed priming, and an untreated control. The layout was a randomized complete block design, with two irrigation frequencies (restricted and full irrigation) and four replications (blocks). The irrigation treatments were applied to whole plots and the seed treatments were applied to subplots. The entire experiment was repeated four times. Irrigation affected the germination of all the seed treatments, but the size of the effect depended on the seed treatment applied. The control and Hydroloc treatments did not have significantly different dry shoot weights, while all the other treatments had significantly different dry shoot weights when comparing the irrigation regimes. The Hydroloc treatment significantly outperformed all the other treatments in regard to the restricted and full irrigation regime. These results indicate that the Hydroloc seed treatment improves KBG germination and shoot dry weight in drought and non-drought conditions, promoting KBG establishment in a wide range of soil moisture conditions. Full article

Certain entomopathogenic fungi, such as Beauveria bassiana, are highly pathogenic to arthropod pests and are able to colonize plant tissues, thereby enhancing both plant growth and disease resistance. This study assessed three B. bassiana strains (CBM1, CBM2, and CBM3) for their pathogenicity toward insect larvae and colonization potential in wheat. The insecticidal activity of the fungi against the larvae of the major lepidopteran pests Helicoverpa armigera, Spodoptera frugiperda, Mythimna separata, and Plutella xylostella was determined. The fungi were then applied to wheat plants using seed immersion and soil drench methods; their colonization rates were compared, and the impacts of fungal colonization on wheat growth and survival were evaluated. The results demonstrated that all three strains were effective in reducing insect damage, with B. bassiana CBM1 exhibiting the highest pathogenicity followed by CBM3 and CBM2. B. bassiana CBM1 was particularly effective, with a significantly higher colonization rate achieved through soil drenching compared to seed immersion. The soil inoculation of B. bassiana resulted in increased plant height at 30 days after sowing (DAS) and root length at 15 DAS compared to the control group. B. bassiana CBM1-colonized wheat increased the mortality of fall armyworm. This research has enriched the biological control microbial resource pool and highlights the potential of B. bassiana in integrated pest management strategies. Full article

The seeds of Cyperus esculentus L. exhibit an uneven surface and irregular shape, which adversely affect precision seeding. Pre-sowing seed soaking treatment not only improves seeding performance, but also enhances the germination capability of C. esculentus seeds. However, the intrinsic parameters of the seeds undergo significant changes after soaking in terms of their physical properties, such as volume, weight, and density. These changes directly influence the fluidity and positioning accuracy of the seeds during the seeding process. Additionally, contact parameters, such as the coefficient of friction and the contact area between the seeds and the seeding apparatus, are altered by soaking. These parameters are crucial for designing efficient seeding devices. Therefore, it is necessary to measure the intrinsic parameters of soaked C. esculentus seeds and their contact parameters with the seeding apparatus to provide parameter support for the precision seeding analysis of pre-soaked C. esculentus. This study focuses on the calibration and experimental investigation of discrete element parameters for soaked C. esculentus seeds. Free-fall collision tests, static friction tests, and rolling friction tests were conducted to calibrate the contact parameters between soaked C. esculentus seeds and between the seeds and steel materials. Using Design-Expert, Plackett–Burman tests, steepest ascent tests, and Box–Behnken response surface tests were designed to obtain the optimal parameter combination for the C. esculentus contact model. The optimal parameters were validated through angle of repose simulation tests and physical experiments. The results indicate that the rolling friction coefficient (F) between seeds, the static friction coefficient (E) between seeds, and the rolling friction coefficient (J) between seeds and steel plates significantly affect the angle of repose. The optimal combination of discrete element parameters is as follows: the static friction coefficient (E) between seeds is 0.675, the rolling friction coefficient (F) between seeds is 0.421, and the rolling friction coefficient (J) between seeds and steel plates is 0.506. Using the calibrated parameters for simulation, the average angle of repose was 32.31°, with a relative error of 1.1% compared to the physical experiments. Full article

This study aimed to evaluate the effect of partial rye flour (RF) replacement with white bean malt (WBM) and red bean malt (RBM) on the baking and the nutritional quality of bread. The addition of white and red bean malts to the rye flour reduced the falling number and the maximum viscosity of the paste. Significant differences in the color of the crust and crumb of baked bread were shown. The addition of malt from bean seeds did not cause significant changes in the consumer assessment of bread. In some cases, a 30% increase in the polyphenols content was observed and an improvement in the antioxidant properties of bread with WBM and RBM was noted. Also, the overall protein and essential amino acids content in the bread was significantly increased. Due to WBM and RBM addition, the quantity of volatile compounds was higher than it was in the control sample, and in specific instances, it had doubled compared to the control sample. Full article

Precise aboveground biomass (AGB) estimation of forests is crucial for sustainable carbon management and ecological monitoring. Traditional methods, such as destructive sampling, field measurements of Diameter at Breast Height with height (DBH and H), and optical remote sensing imagery, often fall short in capturing detailed spatial heterogeneity in AGB estimation and are labor-intensive. Recent advancements in remote sensing technologies, predominantly Light Detection and Ranging (LiDAR), offer potential improvements in accurate AGB estimation and ecological monitoring. Nonetheless, there is limited research on the combined use of UAS (Uncrewed Aerial System) and Backpack-LiDAR technologies for detailed forest biomass. Thus, our study aimed to estimate AGB at the plot level for Picea crassifolia forests in eastern Qinghai, China, by integrating UAS-LiDAR and Backpack-LiDAR data. The Comparative Shortest Path (CSP) algorithm was employed to segment the point clouds from the Backpack-LiDAR, detect seed points and calculate the DBH of individual trees. After that, using these initial seed point files, we segmented the individual trees from the UAS-LiDAR data by employing the Point Cloud Segmentation (PCS) method and measured individual tree heights, which enabled the calculation of the observed/measured AGB across three specific areas. Furthermore, advanced regression models, such as Random Forest (RF), Multiple Linear Regression (MLR), and Support Vector Regression (SVR), are used to estimate AGB using integrated data from both sources (UAS and Backpack-LiDAR). Our results show that: (1) Backpack-LiDAR extracted DBH compared to field extracted DBH shows about (R² = 0.88, RMSE = 0.04 m) whereas UAS-LiDAR extracted height achieved the accuracy (R² = 0.91, RMSE = 1.68 m), which verifies the reliability of the abstracted DBH and height obtained from the LiDAR data. (2) Individual Tree Segmentation (ITS) using a seed file of X and Y coordinates from Backpack to UAS-LiDAR, attaining a total accuracy F-score of 0.96. (3) Using the allometric equation, we obtained AGB ranges from 9.95–409 (Mg/ha). (4) The RF model demonstrated superior accuracy with a coefficient of determination (R²) of 89%, a relative Root Mean Square Error (rRMSE) of 29.34%, and a Root Mean Square Error (RMSE) of 33.92 Mg/ha compared to the MLR and SVR models in AGB prediction. (5) The combination of Backpack-LiDAR and UAS-LiDAR enhanced the ITS accuracy for the AGB estimation of forests. This work highlights the potential of integrating LiDAR technologies to advance ecological monitoring, which can be very important for climate change mitigation and sustainable environmental management in forest monitoring practices. Full article