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Drought is a major abiotic stress that adversely affects the growth and productivity of plants. Malondialdehyde (MDA), a substance produced by membrane lipids in response to reactive oxygen species (ROS), can be used as a drought indicator to evaluate the degree of plasma membrane damage and the ability of plants to drought stress tolerance. Still measuring MDA is usually a labor- and time-consuming task. In this study, near-infrared (NIR) spectroscopy combined with partial least squares (PLS) was used to obtain rapid and high-throughput measurements of MDA, and the application of this technique to plant drought stress experiments was also investigated. Two exotic conifer tree species, namely, slash pine ( Pinus elliottii ) and loblolly pine ( Pinus taeda ), were used as plant material exposed to drought stress; different types of spectral preprocessing methods and important feature-selection algorithms were applied to the PLS model to calibrate it and obtain the best MDA-predicting model. The results show that the best PLS model is established via the combined treatment of detrended variable–significant multivariate correlation algorithm (DET-sMC), where latent variables (LVs) were 6. This model has a respectable predictive capability, with a correlation coefficient ( R 2 ) of 0.66, a root mean square error (RMSE) of 2.28%, and a residual prediction deviation (RPD) of 1.51, and it was successfully implemented in drought stress experiments as a reliable and non-destructive method to detect the MDA content in real time.

Common mistletoe is increasingly mentioned as contributing not only to the decline of deciduous trees at roadside and in city parks, but to conifers in stands. The presence of Viscum in fir stands has been known for many years, but since 2015 has also been the cause of damage to pine. In 2019, mistletoe was observed on 77.5 thousand hectares of Scots pine stands in southern and central Poland. Drought resulting from global climate change is implicated as an important factor conducive to weakening trees and making them more susceptible to the spread of mistletoe and other pests. This paper presents an overview of the latest information on the development of this semi-parasitic plant in Poland, its impact on tree breeding traits and raw material losses, as well as current options for its prevention and eradication.

Knowledge of the reduction in tree stem diameter with increasing height is considered significant for reliable tree taper prediction. Tree taper modeling offers a comprehensive framework that connects tree form to growth processes, enabling precise estimates of volume and biomass. In this context, machine learning modeling approaches offer strong potential for predicting difficult-to-measure field biometric variables, such as tree stem diameters. Two promising machine learning approaches, temporal convolutional networks (TCNs) and extreme gradient boosting (XGBoost), were evaluated for their ability to accurately predict trees’ stem profiles, suggesting a powerful and safe strategy for predicting tree stem sectional volume with minimal ground-truth measurements. The comparative analysis of TCN- and XGBoost-constructed models showed their strong ability to capture the taper trend of the trees. XGBoost proved particularly well adapted to the stem profile of black pine (Pinus nigra) trees in the Karya forest of Mount Olympus, Greece, by summarizing its spatial structure, substantially improving the accuracy of total stem volume up to RMSE% equal to 3.71% and 7.94% of all ranges of the observed stem volume for the fitting and test data sets. The same trend was followed for the 1 m sectional mean stem-volume predictions. The tested machine learning methodologies provide a stable basis for robust tree stem volume predictions, utilizing easily obtained field measurements.

The adsorption capacity of pine tree leaves for removal of methylene blue (MB) from aqueous solution was investigated in a batch system. The effects of the process variables, such as solution pH, contact time, initial dye concentration, amount of adsorbent, agitation speed, salt concentration, and system temperature on the adsorption process were studied. The extent of methylene blue dye adsorption increased with increase in initial dye concentration, contact time, agitation speed, temperature, and solution pH but decreased with increased in amount of adsorbent and salt concentration. Equilibrium data were best described by both Langmuir isotherm and Freundlich adsorption isotherm. The maximum monolayer adsorption capacity of pine tree leaves biomass was 126.58 mg/g at 30 °C. The value of separation factor, R L , from Langmuir equation and Freundlich constant, n , both give an indication of favorable adsorption. The intrapartical diffusion model, liquid film diffusion model, double exponential model, pseudo-first and second order model were used to describe the kinetic and mechanism of adsorption process. A single stage bath adsorber design for the MB adsorption onto pine tree leaves has been presented based on the Langmuir isotherm model equation. Thermodynamic parameters such as standard Gibbs free energy (Δ G 0 ), standard enthalpy (Δ H 0 ), and standard entropy (Δ S 0 ) were calculated.

Stone Pine (Pinus pinea L.) is currently the pine species with the highest commercial value with edible seeds. In this respect, this study introduces a new methodology for extracting Stone Pine trees from Digital Surface Models (DSMs) generated through an Unmanned Aerial Vehicle (UAV) mission. We developed a novel enhanced probability map of local maxima that facilitates the computation of the orientation symmetry by means of new probabilistic local minima information. Four test sites are used to evaluate our automated framework within one of the most important Stone Pine forest areas in Antalya, Turkey. A Hand-held Mobile Laser Scanner (HMLS) was utilized to collect the reference point cloud dataset. Our findings confirm that the proposed methodology, which uses a single DSM as an input, secures overall pixel-based and object-based F 1 -scores of 88.3% and 97.7%, respectively. The overall median Euclidean distance revealed between the automatically extracted stem locations and the manually extracted ones is computed to be 36 cm (less than 4 pixels), demonstrating the effectiveness and robustness of the proposed methodology. Finally, the comparison with the state-of-the-art reveals that the outcomes of the proposed methodology outperform the results of six previous studies in this context.

This study was conducted on naturally growing pine trees in Dohuk Governorate, northern Iraq, to estimate the leaf area index using remote sensing techniques and also identify changes in forest tree cover and monitor them by foresters to make decisions towards sustainability. I took ground field data during the year 2022m from (30) Sample, measurements were taken of the height and diameter at breast height for each sample, as well as the height of the center of the crown and the number of trees. A function tree was chosen to represent the trees of the sample, along with calculating the number of branches for this tree. The wet weight of the leaves was also taken to calculate the average branch length for each of the three layers of the tree. The samples were dried until the weight was stable, and then the surface area of (150) sample leaves was estimated, and from there, the leaf area index was estimated. Vegetation cover indicators were also calculated by using a satellite data source derived from the Sentinel-2 satellite and calculating the equations for these indicators using (ArcGIS) program, so the paper area guide was prepared based on these indicators by preparing the following equations: (1) Estimating the area index as a function of the Natural Differences Vegetation Index (NDVI) : LAI = 2.40159-34.6505*NDVI + 159.204*NDVI 2 . (2) Leaf area index as a function of the percentage vegetation index (RVI) : LAI = -0.598472 + 1.13841*RVI. (3) Leaf area index as a function of the Green Natural Differences Index (GNDVI) : LAI = -0.99298 + 10.1345*GNDVI.

PurposeToday, the decrease in water resources in the worldwide has become an undeniable reality. In addition to climate change, wild agricultural irrigation also increases the rapid consumption of this vital source. The main purpose of this study is to evaluate the effectiveness of olive tree-OTB and pine tree biochar-PTB used for enhancing the water retention capacity of hydrogel against water scarcity stress on the arugula plant.MethodThe PVA/SA were used for hydrogel synthesis and 0.1%, 0.25%, 0.5%, and 1% OTB and PTB were used as additives in hydrogel. Characterization of hydrogels were carried out with SEM and FTIR analyzes. The swelling properties of hydrogels were determined gravimetrically. A 1% and 1.5% hydrogel/turf ratio was added to the pots to determine the effectiveness of hydrogels on growth parameters of arugula under the water scarcity.Results and ConclusionThe results showed that biochar-doped hydrogels had higher swelling capacity than pure hydrogels. When the hydrogels achieved the equilibrium swelling capacity, the best efficiency was obtained from 1% PTB-doped hydrogel as 141.05% and 0.1% OTB-doped hydrogel as 103.60%. However, OTB-doped hydrogels faster swelled than PTB-doped hydrogels. The effects of hydrogels on growth parameters of arugula under different water scarcity were also determined. The results showed that 0.25% OTB- and 1% PTB-doped hydrogels had positive effects on plant under water scarcity. Besides that, the 1% hydrogel/turf ratio was enough for PTB-doped hydrogels for healthy plant. In summary, it was determined that it was appropriate to use pine tree biochar by doping in the hydrogels that will be applied in the fight against drought.

Sirex noctilio is an invasive pest that contributes to pine tree decline, leading to visual symptoms such as needle discoloration, crown thinning, and eventual tree death. Detecting these visible phenotypic signs from drone imagery is challenging due to elongated or irregular crown shapes, weak color differences, and occlusion within dense forests. This study introduces YOLO-PTHD, a lightweight deep learning model designed for detecting visible signs of pine decline in UAV images. The model integrates three customized components: Strip-based convolution to capture elongated tree structures, Channel-Aware Attention to enhance weak visual cues, and a scale-sensitive dynamic loss function to improve detection of minority classes and small targets. A UAV-based dataset, the Sirex Woodwasp dataset, was constructed with annotated images of weakened, and dead pine trees. YOLO-PTHD achieved an mAP of 0.923 and an F1-score of 0.866 on this dataset. To evaluate the model’s generalization capability, it was further tested on the Real Pine Wilt Disease dataset from South Korea. Despite differences in tree symptoms and imaging conditions, the model maintained strong performance, demonstrating its robustness across different forest health scenarios. Field investigations targeting Sirex woodwasp in outbreak areas confirmed that the model could reliably detect damaged trees in real-world forest environments. This work demonstrates the potential of UAV-based visual analysis for large-scale phenotypic surveillance of pine health in forest management.

The pine-tree lappet moth, D. pini, is a harmful defoliator of pine forests in Europe and Asia and a potentially invasive species in North America. The lures for trapping D. pini males based on two known components of its sex pheromone appeared weakly attractive to male moths. Identification of all components of the sex pheromone might allow for the development of more effective lures. The pheromone was sampled from virgin females using SPME and analyzed using gas chromatography coupled with mass spectrometry. Four new likely components ((Z5)-dodecenal, (Z5)-dodecen-1-ol, (Z5)-decen-1-yl acetate, (Z5)-tetradecen-1-yl acetate) and two known components ((Z5,E7)-dodecadienal, (Z5,E7)-dodecadien-1-ol) were identified based on comparison against authentic standards, Kováts indices and spectra libraries. The samples also contained several sesquiterpenes. Wind tunnel and field experiments showed that some blends of synthetic pheromone components alone or enriched with Scots pine essential oil (SPEO) were attractive to D. pini males. One component—(Z5)-decen-1-yl acetate—had a repelling effect. The presented knowledge of D. pini sex pheromone provides a basis for developing optimal lures for monitoring or controlling insect populations.

The aim of this study is to investigate the potential applications of pine cones as plant-based waste material in the construction industry. In order to achieve this target, the pine cone particles (PCP) are mixed with cement to create new lightweight concretes. Furthermore, pine tree resin (PTR), acting as a natural bio-polymer binder, is incorporated into selected samples to ascertain its potential as a binder. The pine cones are cut into particles of 2–4 cm, 0–2 cm, and ground into a powder. A series of critical tests is conducted on the novel produced samples, including thermal conductivity, specific heat, density, compressive strength, water absorption rate, and drying rate. The experiments show that thermal conductivity, specific heat capacity, and thermal expansion coefficient decrease as the weight ratio and size of PCP increase. The presence of PTR increases porosity, further decreasing thermal conductivity, specific heat, and thermal expansion coefficients for the majority of samples. The compressive strength values decrease with the presence of PTR and PCP. Regarding durability, the water absorption ratios remain below the critical 30% threshold, making the material suitable for internal applications or external facades protected by coating/plaster or as external coverings.