Search Results (83)

Early detection of Flavescence dorée leaf symptoms remains an open question for the research community. This work tries to fill this gap by proposing a methodology exploiting per-pixel data obtained from hyperspectral imaging to produce features suitable for machine learning training. However, since asymptomatic samples are similar to healthy samples, we propose “uncertainty-aware” models that address the probability of the samples being similar, thus producing, as output, an “unclassified” category when the uncertainty between multiple classes is too high. The original dataset of leaves hypercubes was collected in a field of Pinot Noir in northern Italy during 2023 and 2024, for a total of 201 hypercubes equally divided into three classes (“healthy”, “asymptomatic”, “diseased”). Feature predictors were 4 for each of the 10 vegetation indices (population quartiles 25-50-75 and population’s mean), for a total of 40 predictors in total per leaf. Due to the low number of samples, it was not possible to estimate the uncertainty of the input data reliably. Thus, we adopted a double Monte Carlo procedure: First, we generated 30,000 synthetic hypercubes, thus computing the per class variance of each feature predictor. Second, we used this variance (serving as uncertainty of the input data) to generate 60,000 new predictors starting from the data in the test dataset. The trained models were therefore tested on these new data, and their predictions were further examined by a Bayesian test for validation purposes. It is highlighted that the proposed method notably improves recognition of “asymptomatic” samples with respect to the original models. The best model structure is the Decision Tree, achieving a prediction accuracy for “asymptomatic” samples of $75.7\%$ against the original $49.3\%$ for the Ensemble of Bagged Decision Trees (ML4) and of $44.6\%$ against the original $13.2\%$ for the Coarse Decision Tree (ML1). Full article

We studied differences in the decomposition rate between Pinus merkusii Jungh. et de Vriese (tusam) leaves, a representative of needle leaf litter, and Diospyros celebica Bakh. (ebony) leaves, a representative of broadleaf litter, in three forest communities (Karst, Lowland, Pine) on the island of Sulawesi, Indonesia, and identified their determinants. Twenty-four 1 m × 1 m quadrats were set up in each forest community to observe the in situ decomposition process. Near each quadrat, 1 m² litter traps were set to monitor litter production. In addition, 30 litter bags containing tusam leaves and 30 litter bags containing ebony leaves were spread in the three forest communities, in both the dry and wet seasons, to observe their decomposition rate during each season. The ANOVA test showed that the one-year in situ Decomposition Rate Constant (k) was significantly highest in the Karst forest (0.0921/year), followed by the Lowland forest (0.0700/year), and the lowest in the Pine forest (0.0277/year). During the dry season, the mean k-value of tusam leaves was significantly faster than ebony leaves in Karst (0.7162/6 months for tusam, 0.3840/6 months for ebony) and Lowland forests (0.3472/6 months for tusam, 0.1017/6 months for ebony), but on the contrary, it is slower in the Pine forest (0.0498/6 months for tusam, 0.0745/6 months for ebony). During the wet season, there was no significant difference between the mean k-value of tusam leaves compared to ebony leaves in the Karst (0.5217/4 months for tusam, 0.4859/4 months for ebony) and Lowland (0.2397/4 months for tusam, 0.2098/4 months for ebony) forests, but in the Pine forest, the mean k-value of ebony leaves was significantly higher than that of tusam leaves (0.0942/4 months for tusam, 0.1650/4 months for ebony). This study explains that the decomposition process of leaf litter is complex, species-specific, and is controlled by a combination of factors. Extrinsic factors play a more critical role than intrinsic factors in determining the k-value. The low rate of decomposition of tusam leaves under its mother tree stands is not caused by intrinsic factors, but rather by extrinsic factors that inhibit the growth of decomposing agents. Full article

This work presents optimized decision tree-based ensemble machine learning models for predicting and quantifying the effects of greenhouse gas (GHG) emissions in Bangladesh. It aims to identify policy implications in response to significant environmental changes. The models analyze the emissions of CO₂, N₂O, and CH₄ from sectors including energy, industry, agriculture, and waste. We consider many parameters, including energy consumption, population, urbanization, gross domestic products, foreign direct investment, and per capita income. The data covers the period from 1971 to 2019. The model is trained using 80% of the dataset and validated using the remaining 20%. The hyperparameters, such as the number of estimators, maximum samples, maximum depth, learning rate, and minimum samples leaf, were optimized via particle swarm optimization. The models were tested, and their forecasts were extended till 2041. An examination of feature importance has identified energy consumption as a critical factor in greenhouse gas emissions, acknowledging the positive effects of clean energy in accordance with the clean development mechanism. The results demonstrate a robust model performance, with an R² score of approximately 0.90 for both the training and testing datasets. The bagging decision tree model showed the lowest mean squared error of 151.3453 and the lowest mean absolute percentage error of 0.1686. The findings of this study will help decision-makers understand the complex connections between socioeconomic conditions and the elements that contribute to greenhouse gas emissions. The discoveries will enable more precise monitoring of national greenhouse gas (GHG) inventories, allowing for focused efforts to mitigate climate change in Bangladesh. Full article

Understory vegetation, particularly dwarf bamboo, plays a crucial role in regulating forest nutrient cycles by intercepting litter and altering decomposition processes, yet its overall impacts remain understudied and insufficiently quantified. This study employs a combination of field surveys and decomposition bag experiments to investigate how understory dwarf bamboo (Fargesia decurvata) alters the spatial–temporal patterns of leaf litter production and decomposition. We found that the dwarf bamboo intercepted more than 25% of canopy litterfall, altering its spatial distribution and reducing decomposition efficiency in the bamboo crown (BC). Leaf trait-decomposition relationships differed strongly across habitats, being positive for saturated fresh weight (SFW), leaf thickness (LFT), and leaf area (LA) and dry weight (DW) in bamboo habitats but weaker in the bamboo-free habitat (NB). Potassium release was significantly higher in the BC treatment, whereas carbon release showed the opposite trend. In contrast, nitrogen and phosphorus exhibited net enrichment across all treatments, with phosphorus enrichment being slower in BC than in bamboo-covered ground surface (BG) and NB. Our results demonstrate that the understory dwarf bamboo reshapes the spatial distribution of litter and nutrient release dynamics during decomposition, resulting in element-specific nutrient release patterns. These findings provide mechanistic insights into how understory dwarf bamboo mediates nutrient cycling dynamics in forest communities. Full article

The fungicides oxathiapiprolin, benthiavalicarb, and their mixture (Zorvec Endavia) provided excellent protection for tomato fruits against Phytophthora infestans when applied directly to the fruits or to the fruit stem scar. High levels of protection were also recorded when the fungicides were applied to the root system of fruit-bearing plants grown in a greenhouse. The objective of this study was to follow the translocation of oxathiapiprolin and benthiavalicarb into the fruits of tomato. We discovered that while soil drenching conferred strong protection to leaves, it failed to provide good protection for the fruits. Similarly, a fungicidal spray applied to plants while their fruits were bagged during spraying provided full protection to the leaves but failed to protect the fruits. These results indicate differential systemic translocation of the fungicides to leaves versus fruits. LC–MS/MS analyses revealed translocation of oxathiapiprolin and benthiavalicarb to leaves but not to fruits in soil-treated plants. Thus, while fruits may be less protected, they may also pose a lower risk of pesticide residues to consumers. This is the first study to compare fruit versus leaf infection and demonstrate differential systemic translocation of systemic fungicides to leaves versus fruits. Full article

Aster spp., a key grass species for the ecological restoration of alpine degraded grasslands on the Qinghai–Tibet Plateau, often suffers from pest damage during its flowering and seed maturation stages, severely limiting the effectiveness of ecological restoration and the sustainable utilization of germplasm resources. This study focused on nine widely distributed species of Aster in the Three River Source Region of Qinghai Province, systematically investigated the structure of arthropod communities and the spatiotemporal dynamics of pests, and developed an integrated pest management (IPM) strategy. Through systematic surveys at multiple sites, a total of 109 arthropod species were identified (57 families of insects, 96 species; 7 families of spiders, 13 species). The Diptera (Tephritidae) and Hemiptera (Miridae) were identified as dominant groups. Tephritis angustipennis was determined to be the key pest, with its population density reaching a peak in mid-to-late August (p < 0.05). Based on the occurrence patterns of the pest, an IPM strategy integrating physical, chemical, and biological control methods was proposed: flower head bagging as a physical barrier significantly reduced plant damage but required balancing the risk of seed sterility. A combination lure (broad-spectrum fruit fly lure + a mixture of sugar and vinegar) showed a significant effect in attracting and killing adult flies. In chemical control, spraying a combination of insecticides (DB: 10% β-Cypermethrin aqueous emulsion (9 mL/acre) + 5% avermectin (20 mL/acre)) during the leaf expansion stage to early flowering stage achieved approximately 80% pest mortality within 24 h; additionally, supplementary spraying of 5% broflanilide (30 mL/acre) during the full flowering stage prolonged the efficacy and delayed the development of insecticide resistance. In terms of natural enemy utilization, Lycosidae and Thomisidae demonstrated significant potential for naturally regulating pest populations. Physiological mechanism studies showed that the difference in responses between plant catalase (CAT) activity and insect glutathione S-transferase (GST) activity was a key factor driving control efficacy (the cumulative explanation rate reached 94%). This IPM strategy, by integrating physical barriers, dynamic trapping, targeted spraying, and natural enemy control, significantly enhances control efficiency and ecological compatibility, providing a theoretical basis and technical paradigm for the ecological restoration of degraded alpine grasslands and the sustainable management of medicinal plants in cold regions. Full article

This study proposes CEL-DL-Bagging (Cross-Entropy Loss-optimized Deep Learning Bagging), a multi-model fusion framework that integrates cross-entropy loss-weighted voting with Bootstrap Aggregating (Bagging). First, we develop a lightweight recognition architecture by embedding a salient position attention (SPA) mechanism into four base networks (YOLOv5s-cls, EfficientNet-B0, MobileNetV3, and ShuffleNetV2), significantly enhancing discriminative feature extraction for disease patterns. Our experiments show that these SPA-enhanced models achieve consistent accuracy gains of 0.8–1.7 percentage points, peaking at 97.86%. Building on this, we introduce DB-CEWSV—an ensemble framework combining Deep Bootstrap Aggregating (DB) with adaptive Cross-Entropy Weighted Soft Voting (CEWSV). The system dynamically optimizes model weights based on their cross-entropy performance, using SPA-augmented networks as base learners. The final integrated model attains 98.33% accuracy, outperforming the strongest individual base learner by 0.48 percentage points. Compared with single models, the ensemble learning algorithm proposed in this study led to better generalization and robustness of the ensemble learning model and better identification of rice diseases in the natural background. It provides a technical reference for applying rice disease identification in practical engineering. Full article

This study aimed to evaluate the fluoride content and other key physicochemical properties in commercially available black tea infusions, with a focus on tea form and geographic origin, in order to assess their contribution to total dietary fluoride intake. Methods: A total of 121 black tea samples were analyzed, including 66 loose-leaf, 42 bags, and 13 pyramid-bag teas. Infusions were prepared using standardized brewing protocols. Fluoride concentrations were determined with an ion-selective electrode, while the pH, buffer capacity, titratable acidity, calcium, and inorganic phosphorus were also measured. Statistical analysis included ANOVA, Tukey post hoc tests, and Pearson correlation analysis. Results: Fluoride content varied significantly by tea form and origin. Infusion of tea bags exhibited the highest fluoride, calcium, and acidity levels, while loose-leaf teas had the lowest. Teas from Africa contained approximately twice as much fluoride as those from Central or East Asia. Significant correlations between fluoride, calcium, and phosphorus were observed, particularly in tea-bag infusions, suggesting processing influences mineral release. Conclusions: Black tea, particularly in bag form and sourced from African regions, may significantly contribute to daily fluoride intake. Given the potential to exceed recommended fluoride thresholds, especially in individuals consuming multiple cups daily or living in fluoridated areas, these findings underscore the importance of consumer awareness and possible product labeling to guide safe consumption. Full article

This study aimed to determine the caffeine and L-theanine contents in tea infusions prepared under varying fermentation degrees, brewing conditions (temperature and time), and serving methods (tea bag/loose-leaf). Infusions of six tea types (green, white, yellow, oolong, black, and Pu-erh) and various serving forms of green, white, and black tea were brewed at 80 °C and 100 °C for 2, 5, and 10 min. Contents were analyzed using reversed-phase high-performance liquid chromatography. Caffeine levels were highest in black and yellow tea (14 ± 1.0 mg/200 mL and 13.8 ± 0.2 mg/200 mL, respectively), both of which were significantly higher than the levels in green, white, and oolong tea (11 ± 2.1 mg/200 mL; 5.8 ± 0.7 mg/200 mL; and 4 ± 1.6 mg/200 mL, respectively; p < 0.001), whereas Pu-erh tea (13 ± 2.9 mg/200 mL) had caffeine levels comparable to that of black tea (p > 0.05). L-Theanine levels were highest in black and green tea (12.3 ± 2.8 mg/200 mL and 12.5 ± 2.5 mg/200 mL, respectively), and these levels were significantly higher than those in the other tea types (p < 0.001). Brewing temperature only affected caffeine levels n, whereas L-oolong tea (4.4 → 13.5 mg/200 mL, p < 0.05); theanine remained stable (p > 0.05). Longer brewing times significantly raised both components (e.g., yellow tea caffeine levels rose to 53 ± 16.9 mg/200 mL at 10 min; p < 0.05). In black tea, cup-sized bags yielded higher caffeine levels (39 ± 9.9 mg/200 mL) than loose-leaf (24 ± 7.2 mg/200 mL) and teapot bags (p < 0.001). Serving method had no effect on green and white teas (p > 0.05). In conclusion, fermentation rate, brewing conditions, and serving method were found to influence the caffeine and L-theanine levels of tea infusions. Full article

Tea is one of the most widely consumed beverages globally and a significant dietary source of fluoride. This systematic review aimed to identify and evaluate the factors influencing fluoride concentration in tea infusions. A comprehensive literature search was conducted in March 2025 across PubMed, Scopus, and Web of Science databases, following PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the PICO framework. Eligible studies investigated fluoride release in tea infusions, published in English from the year 2000 onward. Thirty articles met the inclusion criteria, and the risk of bias in the articles was assessed using the Joanna Briggs Institute (JBI) quality checklist. Fluoride concentration in tea infusions varied widely across studies, ranging from 0.008 to over 8 mg/L. Key factors influencing fluoride release included tea type (with black and green teas showing the highest values), leaf form (powdered and bagged teas released more fluoride than loose leaves), brewing time and temperature, water composition, and the presence of additives such as spices. A longer brewing time and higher temperature consistently increased fluoride extraction. Lower pH or water hardness also significantly affected fluoride availability. Regional origin of tea and production methods were additional sources of variation. Fluoride release in tea is influenced by a complex interplay of botanical, environmental, and preparation-related factors. These findings are clinically relevant, particularly for populations at risk of fluoride overexposure. Further standardized research is needed to inform safe consumption guidelines and public health recommendations. Full article

Litter decomposition is a fundamental ecological process that drives nutrient cycling and energy flow. However, little is known about the elevational patterns of this process in different stages. We established ten sites on Dongling Mountain in Beijing, China, to investigate the elevational patterns of oak leaf (Quercus liaotungensis) decomposition, as well as the underlying mechanisms. Our results revealed distinct elevational patterns of litter decomposition in different stages. There was no significant altitudinal pattern in the mass loss of Q. liaotungensis leaves at the 2nd, 4th, and 6th months of decomposition. By the 16th month, the mass loss decreased significantly along the elevation gradient (p = 0.008). By the 28th month, a reverse pattern emerged, with greater mass loss observed at higher elevations (p < 0.001). A similar change also took place in the altitudinal pattern of the abundance of invertebrates within the litter bag, which was lower at higher elevations at the 16th month (p = 0.002), but higher at higher elevations at the 28th month (p = 0.002). In addition, we examined the elevational patterns of carbon and nitrogen concentrations in different stages. The results of the structural equation model revealed that the invertebrate abundance at the 4th month influenced the litter residues at the 16th month (p < 0.001), yet nitrogen content at the 16th month affected litter residues at the 28th month(p < 0.001). This study provides novel insights into the temporal dynamics of litter decomposition along an elevational gradient and highlights the underlying mechanisms by which litter chemistry and biological factors regulate this process. Full article

Coffee intercropped with Urochloa decumbens modifies nutrient uptake, and consequently the yield and quality of coffee, by the greater release of nutrients and efficient nutrient cycling. There is little information about the increasing nutrient content in Arabica coffee plants intercropped with Urochloa decumbens. The objective of this study was to evaluate the effect of Urochloa decumbens intercropped with two coffee cultivars (Coffea arabica L.) on the levels of macro- and micronutrients and coffee crop yield. The experiment was conducted at Embrapa Cerrados, Planaltina-DF, and was arranged in a completely randomized block design with three replications, in a factorial design. The first factor consists of two management systems: with (WB) and without (NB) Urochloa decumbens intercropped; the second factor is composed of Arabica coffee cultivars ‘IPR-103’ and ‘IPR-99’. There was no significant difference in yield of the coffee cultivars with and without Urochloa decumbens intercropped between the rows. The treatment with ‘IPR-99’ coffee cultivar intercropped with Urochloa decumbens achieved 400 kg ha⁻¹ (8 bags) more than the other treatments. The presence of Urochloa decumbens increased leaf nutrient contents of the macronutrients Ca and Mg and micronutrients Mn and Fe. Thus, the Arabica coffee–Urochloa decumbens intercropping system is an efficiency management strategy which improves nutrient content for the main crop with consequent yield gains. Full article

The aim of this study was to improve understanding of the impacts of low-light stress induced by branch-bagged shading on photosynthetic physiology and biochemical composition. Eight-year-old ‘Tieton’ sweet cherry leaves and white parchment bags with a 23% shading rate were selected to cover ten 50 cm long branches for 10 d, 20 d, and 30 d followed by 10 d light restoring. The results indicated that when shading for 30 d, the net photosynthetic rate (P_N) of the leaves, including stomatal conductance (g_s), transpiration rate (E), intercellular CO₂ concentration (C_i), superoxide dismutase (SOD), peroxide (POD), catalase (CAT), starch, and sugar contents were lower, whereas chlorophyll (Chl) and malondialdehyde (MDA) concentrations were higher than those in CK leaves. After 10-10 treatments, leaf parameters including SOD, POD, CAT, starch, and sugar levels were almost the same as those in control (CK; no shading) leaves; the opposite was true for Chl and MDA. However, after 10 d of no bag following 20 and 30 d of shading, the P_N, C_i, E, and SOD, CAT, glucose, sorbitol, sucrose and starch levels were lower than those in CK leaves, whereas MDA levels were higher. At 20-10, there was no difference in leaf fructose levels compared to those in CK leaves; the Chl levels were higher. At 30-10, leaf fructose levels were reduced compared with those in CK leaves; Chl levels showed no difference. Therefore, sweet cherry leaves have robust recovery abilities; however, prolonged low-light stress can impede physiological restoration. Full article

Background: Fresh produce such as leafy green salads have recently become recognized as a potential source of food-borne infection by enteric pathogens This study investigated whether compounds released from damaged salad leaves were recognized by Listeria monocytogenes strain EGD and if they impacted its growth and virulence. Methods: The effects of extracts of salad leaves or salad bag fluids were tested on the growth, biofilm formation, and colonization of salad leaves and host cell virulence. Results: The presence of salad extract at a concentration of less than 0.5% v/v and salad bag fluids at a concentration of 10% v/v enhanced the growth in water and serum-based medium by more than 10,000 times over un-supplemented control cultures. Light and scanning electron microscopy, as well as eukaryotic Caco-2 and Galleria mellonella models of infection, showed that leafy green extracts from rocket, lettuce, spinach, and their salad bag fluids significantly increased the ability of Listeria to establish biofilms and infect host cells. Conclusions: This investigation showed that salad leaf extracts can markedly enhance bacterial virulence, which has implications for bagged salad leaf consumer safety if the leaves become contaminated with pathogenic bacteria such as Listeria. Full article

The production of high-quality coffee seedlings is essential to meet the demands of the coffee sector, requiring more efficient and sustainable water management practices. In this context, the use of hydroretentive polymers, particularly biodegradable ones, emerges as a promising alternative to optimize water use, reduce the environmental impact associated with synthetic polymers, and improve the agronomic traits of seedlings. Therefore, this study aimed to evaluate the effects of different irrigation intervals and hydroretentive polymer doses on the water consumption and agronomic characteristics of Coffea arabica L. seedlings. This study was conducted in a protected environment using a randomized block design with split plots and four replicates. The plots consisted of two irrigation intervals (2 and 4 days), and the subplots included four doses of hydroretentive polymer (0%, 0.25%, 0.5%, and 1%), applied in 0.5 dm³ polypropylene bags. Results showed that the 0.5% polymer dose combined with a 2-day irrigation interval resulted in the highest water consumption, while the combination of 0% polymer and a 4-day irrigation interval led to the lowest water consumption. The 0.25% hydroretentive polymer dose with irrigation every 2 days showed the best performance in gas exchange, promoting photosynthesis without causing water saturation. This management also promoted better seedling growth, increasing biomass, height, leaf area, and root volume compared to longer irrigation intervals. The crop coefficients (Kc × Ks) were 0.20, 0.28, and 0.45 during the periods of 0–50, 51–80, and 81–150 days after sowing, respectively. A dose of 0.25% hydroretentive polymer with a 2-day irrigation interval is recommended for the production of Arabica coffee seedlings, contributing to agricultural practices aligned with environmental preservation and productive efficiency. Full article