Search Results (2,955)

Blueberry production has rapidly expanded over the past decade, accompanied by growing demand for efficient and accurate methods to monitor the flowering and fruiting phases of blueberry development, which has a direct impact on yield potential. Accurate determination of blueberry phenology enables growers to make data-driven decisions on freeze protection applications and harvest windows. In addition, objective phenology data of blueberry mapping populations will provide high-quality phenotype data for the discovery of genetic mechanisms regulating blueberry flowering and fruiting times. Traditional approaches, such as manual counting and visual ratings, are labor-intensive and subjective in capturing variation across genotypes. Recent progress in computer vision and deep learning has enabled automated flower detection, but most existing studies on blueberries remain restricted to narrow flowering windows or close-up images, limiting their application at the bush level and across the seasonal development. In this study, we developed BerryFlowerNet, a customized YOLO-based model to detect and count blueberry flower clusters from bud to green fruit stages. A comprehensive dataset was collected on three dates using a field phenotyping robot, covering five flowering stages. The integration of CFNet, a custom module fusing shallow spatial features, and PIoU loss improved the detection performance. Additionally, the Slicing Aided Hyper Inference algorithm was employed to address small-object detection in bush-level images. Experimental results demonstrated that BerryFlowerNet outperformed the baseline YOLO model and three additional detectors, achieving an average mAP0.5 of 0.644 across five independent training runs. The model achieved an accuracy of 0.88 when predicting blueberry flowering stages, indicating its effectiveness and accuracy. Additionally, the results of the bush-level image analysis showed the capability of the model to capture genotype-level differences in flowering dynamics. Overall, this approach offers new opportunities for growers and breeders to determine blueberry phenological development that is critical for optimizing on-farm management strategies and advancing precision phenotyping to facilitate the development of climate-resilient blueberries. Full article

The Gastrodia elata Blume (GE) life cycle is unique, since its successful germination and growth rely on symbiosis with specific fungi (e.g., Armillaria mellea). However, the community succession, tissue specificity and functional potential of endophytic and rhizosphere bacterial communities during the seed formation stage of GE remain unclear. Here, we used high-throughput 16S rRNA gene sequencing to systematically explore the composition, diversity, and dynamic succession of bacterial communities across different stages of seed formation and among various tissues. Our results revealed that the endophytic community remained relatively stable across most developmental stages and tissue types (ANOSIM R = 0.4568, p = 0.001), with significant compositional shifts only occurring at the fruiting stage in specific tissues (stems and seeds). In contrast, the rhizosphere soil bacterial community showed stronger developmental succession (ANOSIM R = 0.7037, p = 0.001), with progressive divergence and the strongest segregation observed between the initial planting and fruiting stages. Alpha diversity peaked at the flowering stage for endophytic bacteria (Shannon index) and at the bud formation stage for rhizosphere soil bacteria, with persistent core taxa (Bacteroides in endophytic bacteria, Pseudarthrobacter in rhizosphere soil bacteria) dominating across stages. Functional predictions revealed stable core metabolic pathways, with stage-specific enrichments of glycolysis or gluconeogenesis at late developmental stages. These results provide novel ecological insights into the spatiotemporal dynamics of bacterial communities across different stages of GE seed formation, highlighting the distinct ecological strategies of endophytic and rhizosphere soil bacteria during the reproductive development of the plant. Full article

Efficient nutrient management through fertigation is essential for sustaining table grape production under water-limited Mediterranean environments. This study evaluated the effects of graded potassium (K) fertigation rates on yield and berry quality of grapevines under semi-arid conditions in northern Jordan. Field experiments were conducted over three consecutive seasons at three locations using four potassium application rates (0, 100, 200, and 300 kg K₂O ha⁻¹) applied through drip fertigation and synchronized with key vine phenological stages. Yield and fruit-quality parameters were analyzed using linear mixed-effects models accounting for treatment, year, location, and their interactions. Potassium fertigation significantly increased total yield, cluster weight, and berry physical attributes, including firmness, volume, weight, and diameter, whereas total soluble solids (TSS) and juice pH were largely unaffected. Relative to the control, potassium fertigation progressively increased total yield per vine by approximately 21%, 47%, and 72% under the 100, 200, and 300 kg K₂O ha⁻¹ treatments, respectively, although the magnitude of response differed among locations and growing seasons. Significant treatment × location interactions indicated that site-specific soil conditions influenced potassium response. These results demonstrate that synchronizing potassium supply with vine phenological demand through fertigation enhances productivity and berry physical quality without compromising fruit chemical composition. The observed improvements are consistent with the established physiological roles of potassium in osmotic regulation, assimilate transport, and berry development, supporting optimized potassium fertigation as a key component of precision nutrient management for sustainable viticulture in semi-arid Mediterranean regions. Full article

Salinity is a critical agricultural threat that reduces the productivity of several crops. Tomato (Solanum lycopersicum) is the world’s second most significant horticultural commodity, which struggles due to salt concentrations in irrigation water, even in hydroponic systems. This research evaluated seed priming treatments (hydro-, halo-, bacterio-, and halo-bacteriopriming) at different phenological stages under two salinity conditions (0 and 16 mM NaCl) to improve crop production. After evaluating physiological variables and multivariate statistical analyses, this study’s main breakthroughs are: Priming treatments modified the physiological, nutritional, and productive metabolism of tomato plants. Bacterio- and halo-bacteriopriming using an endophytic and halophytic bacterial consortium reduced germination time, enhancing uniformity and synchronizing seedling emergence. Bacteriopriming enhanced N, P, Ca and Zn absorption in seedlings. In the vegetative and reproductive stages, bacteriopriming consistently increased concentrations of K, Mg, and Zn in leaves and fruits but depleted Na uptake. Improving the nutritional balance resulted in not only a higher concentration of chlorophyll but also an increase in the yield and beta-carotene concentration in tomato fruits. The results demonstrated that halo-bacteriopriming may be a biotechnological strategy for mitigating saline stress, optimizing tomato growth and nutraceutical quality, because it outperformed the plant response in all stages of development compared to the control and hydro- and haloprimed treatments. Full article

Ethanol is a pervasive chemical stressor in fermentative environments and represents a major ecological challenge for Drosophila melanogaster, a species that naturally inhabits decaying fruits. Although ethanol tolerance has traditionally been attributed to detoxification and antioxidant pathways, accumulating evidence indicates that immune-related genes, particularly those encoding immune deficiency (IMD) pathway-associated antimicrobial peptides (IMD-AMPs), contribute importantly to chemical stress adaptation. Previous studies have demonstrated that IMD-AMP induction is required for ethanol tolerance; however, whether elevated IMD-AMP expression alone is sufficient to enhance tolerance has remained unresolved. In this study, we investigated the functional significance of IMD-AMP upregulation in ethanol tolerance using dietary propolis as an experimental immune-modulating agent. D. melanogaster were reared throughout their life cycle on propolis-supplemented diets and subsequently exposed to ethanol. Propolis-fed flies exhibited significantly enhanced survival under ethanol stress compared with control flies. Notably, this increased tolerance was not accompanied by upregulation of classical ethanol metabolism genes or broad induction of antioxidant-related genes. Instead, propolis feeding increased baseline and early-stage expression of IMD-AMP genes, including Diptericin A (DptA), Diptericin B (DptB), Attacin (AttC), and Metchnikowin (Mtk) before and during ethanol exposure. These findings suggest IMD-AMP upregulation is positively associated with enhanced ethanol tolerance in D. melanogaster. Our results establish a proactive role for immune-related pathways in chemical stress resistance and extend the functional scope of AMPs beyond pathogen defense. This study identifies IMD-AMPs as key effectors linking immune activation to physiological adaptation under ethanol-induced chemical stress. Full article

Accurate tomato maturity detection is critical for optimizing key agricultural operations in precision agriculture, including harvesting, grading, and quality control. Despite advances in deep learning and machine vision, reliable detection in real-world environments remains challenging due to cluttered backgrounds, dense fruit clustering, and subtle color differences between maturity stages. In response to these challenges, we present TAA-YOLOv8, an attention-enhanced detection architecture integrating a novel Tomato-Adaptive Attention (TAA) module that performs sequential channel–spatial feature refinement using an adaptive 1D convolution for channel recalibration and a balanced 5 × 5 spatial kernel for improved localization, enhancing discriminative representation while preserving computational efficiency. The framework is evaluated on three datasets representing diverse agricultural environments: a newly introduced Cross-Regional Tomato dataset collected from open-field farms in Bangladesh and greenhouse facilities in Japan, and two public benchmarks, Laboro Tomato and Tomato Plantfactory. TAA-YOLOv8m outperforms baseline YOLOv8m, achieving mAP@50–95 improvements of +9.29%, +9.00%, and +6.65% with F1-scores of 0.968, 0.976, and 0.955, respectively. It further surpasses attention-enhanced variants and RT-DETR-L, and remains competitive with YOLOv11m. Gradient-Weighted Class Activation Mapping (Grad-CAM) shows concentrated fruit-centered activations, providing transparent decision-making evidence and supporting stakeholder confidence in practical deployment within vision-based agricultural management systems. Full article

Microbial contamination of fresh fruits remains a major food safety concern due to the ability of pathogenic bacteria to persist on fruit surfaces during storage. This study evaluated the antimicrobial efficacy of ExAF-E1, a postbiotic formulation derived from Lactiplantibacillus plantarum strains UTNGt28L and UTNGt2, against multidrug-resistant Escherichia coli L1PEag1 and Staphylococcus epidermidis L4MStp5 on goldenberry (Physalis peruviana L.). Fruits were artificially contaminated, treated, and stored for 7 days at room temperature (RT) and refrigerated (4 °C), with analyses conducted in quadruplicate. At RT, ExAF-E1 significantly reduced total aerobic counts (TAC) and pathogen loads (p < 0.05), achieving early reductions of ~0.4–0.5 log CFU/g in TAC and ~1.0–1.5 log CFU/g in pathogens, with inhibition maintained through day 7. In contrast, the commercial disinfectant (CD) showed transient reductions, with microbial levels not significantly different from the control at later stages (p > 0.05). Under refrigeration, ExAF-E1 produced greater and persistent reductions, reaching ~1.0–1.2 log CFU/g in TAC and ~1.5–2.5 log CFU/g in pathogens by day 7 (p < 0.05), whereas CD exhibited strong initial reductions followed by partial regrowth. Fruit quality parameters (pH, TA, TSS, TPC, AOX, AAC) were not significantly affected by treatments (p > 0.05). Ultrastructural analyses using transmission and scanning electron microscopy revealed disruption of bacterial cell envelope integrity, including membrane damage, cytoplasmic leakage, and morphological deformation. These findings demonstrate that ExAF-E1 provides rapid and sustained antimicrobial activity under both storage conditions while preserving fruit quality, supporting its application as a postharvest strategy for improving the microbial safety of fresh produce. Full article

Tomato (Solanum lycopersicum) cultivation is highly vulnerable to fungal, bacterial, and viral leaf diseases that can significantly reduce crop yield and fruit quality when not detected at early stages. Although recent deep learning approaches have achieved remarkable performance in plant disease classification, many state-of-the-art architectures remain computationally expensive and therefore difficult to deploy on resource-constrained edge devices commonly used in smart agriculture environments. To address this challenge, this paper introduces EcoTomHybridNet, an adaptive resource-aware CNN–Transformer framework designed for efficient tomato leaf disease classification under edge-computing constraints. The proposed architecture combines a lightweight convolutional backbone with a dual-branch inference mechanism composed of a fast convolutional branch for computationally efficient prediction and a Transformer-enhanced branch with local self-attention for richer contextual feature extraction. Unlike conventional lightweight hybrid models relying on static inference pipelines, EcoTomHybridNet integrates a lightweight policy-guided routing mechanism that dynamically allocates inputs between the fast convolutional branch and the Transformer-enhanced branch according to input complexity. This adaptive inference strategy dynamically reduces unnecessary Transformer computations for simpler samples while preserving strong predictive performance on more challenging inputs through policy-guided branch allocation. To further improve representation capability without significantly increasing computational complexity, the proposed student network is trained using knowledge distillation from a ViT-Tiny teacher model. Experimental results on the PlantVillage tomato dataset demonstrate that EcoTomHybridNet achieves 99.42% test accuracy and 99.0% validation accuracy under the full hybrid inference configuration. Additional validation strategies, including 5-fold cross-validation and robustness evaluation under Gaussian noise and motion blur perturbations, indicate stable performance across different data splits and moderate image degradations, suggesting improved generalization capability beyond simple dataset memorization. Furthermore, adaptive routing experiments using a lightweight threshold-based policy mechanism achieved 99.20% test accuracy while reducing computational complexity from 0.36 GFLOPs to 0.25 GFLOPs per image, corresponding to approximately 30% computational savings. These results demonstrate the effectiveness of policy-guided adaptive inference for balancing predictive performance and computational efficiency in edge-oriented plant disease classification. Overall, EcoTomHybridNet provides an efficient and adaptive framework for intelligent plant disease monitoring in IoT-enabled smart agriculture systems. Full article

This study investigated the comprehensive effects of harvest timing and stir-frying on Ziziphi Spinosae Semen (ZSS) quality using chemical profiling, Caenorhabditis elegans bioassays, and intelligent sensory analysis (electronic nose (E-nose), electronic tongue (E-tongue), and gas chromatography-ion mobility spectrometry (GC-IMS)). Results indicated that delaying harvest to 15 September significantly promoted bioactive accumulation, with total saponins reaching 9.54 g kg⁻¹ at this stage. Stir-frying the optimal raw material further enhanced pharmacological efficacy; spinosin content increased 1.48-fold, and C. elegans motility cessation time significantly shortened from 240 s to 180 s, demonstrating superior sedative activity. Additionally, stir-frying improved the total sensory score from 53.8 to 80.4, characterized by a harmonized balance of bitterness and umami. GC-IMS analysis identified Maillard reaction products, specifically 2-methylpyrazine and 2-methylbutanal as key markers responsible for the distinctive roasted aroma. Consequently, harvesting the fruits of Ziziphus jujuba var. spinosa at physiological maturity, followed by the stir-frying of ZSS effectively enhances its sedative effects and flavor profile. Full article

Background: Iron deficiency (ID) progresses through three stages: iron deficiency stores (IDS), iron deficiency erythropoiesis (IDE), and iron deficiency anemia (IDA). Neglecting subclinical ID may be harmful to school-aged children and increase the public health burden. Although diet is a key modifiable factor, most studies only focus on overall ID or merely the clinical IDA stage. This study combines a dietary index with pattern analysis to take advantage of their complementary strengths and explore their associations with ID progression. Methods: This cross-sectional study included 2493 participants from rural Guangzhou between June 2022 and May 2023. Demographic, lifestyle, anthropometric, and dietary data were collected via structured questionnaires. Blood samples were analyzed for iron status. Factor analysis identified dietary patterns, and the Chinese Dietary Guidelines Index for Children and Adolescents [CDGI(2021)-C] assessed dietary quality. We used ordinal logistic regression, multivariable logistic regression, and restricted cubic spline (RCS) models to examine dietary associations with ID stages. Results: IDS, IDE, and IDA proportions were 68.22%, 17.45%, and 14.33%, respectively. All four dietary patterns correlated positively with CDGI(2021)-C, most strongly for the fruit–vegetable (r_s = 0.552) and cereal–tuber–legume patterns (r_s = 0.386). Higher CDGI(2021)-C (OR = 0.852, 95% CI: 0.751–0.966, p-trend = 0.012), fruit–vegetable (OR = 0.866, 95%CI: 0.748–0.993, p-trend = 0.047), and meat–offal patterns (OR = 0.733, 95%CI: 0.611–0.868, p-trend < 0.001) were inversely associated with advancing ID stages, while the snack–fast food pattern was positively associated (OR = 1.233, 95% CI: 1.094–1.381, p-trend < 0.001). In IDS, higher adherence to CDGI(2021)-C, fruit–vegetable, and meat–offal patterns was associated with lower odds (all p-trend < 0.05). RCS showed nonlinear associations for the snack–fast food and cereal–tuber–legume patterns, with risk peaking at moderate-to-high adherence to these patterns (both p-nonlinear < 0.05). In IDE and IDA, the snack–fast food pattern risk rose steeply at moderate-to-high adherence (p-nonlinear = 0.036), whereas the cereal–tuber–legume pattern’s ORs fluctuated near 1 (p-nonlinear = 0.020). Conclusions: Dietary pattern and index analyses showed consistent associations across ID stages. Adherence to dietary guidelines slows ID progression, especially in early subclinical stages. More fruits, vegetables, and heme-iron-rich foods, alongside less fast food and snacks, slow ID progression. Though dietary intervention effects weaken in later stages, reducing fast food and snacks confers long-term benefits. These findings inform targeted nutrition policies to prevent ID progression in children. Full article

Preharvest selenium (Se) biofortification is a promising strategy to enhance both the nutritional value and postharvest performance of vegetables. However, its effects on cherry tomato quality during storage, particularly in relation to ripening stage at harvest, remain poorly understood. This study evaluated the impact of foliar Se application (0.5 mM, as Na₂SeO₄) on carpometric, compositional, and functional traits of cherry tomatoes harvested at two ripening stages (orange-red and deep red) and stored for 0, 10, and 20 days at 11.0 ± 0.5 °C. The Se application increased fruit Se concentration (∼30-fold) and improved dry matter (+8.1%) and firmness (+8.3%) throughout storage. At the end of storage, all fruits showed reduced firmness (up to −44%) and increased fresh weight loss (up to 8.5%), although Se-biofortified fruits consistently maintained a higher dry matter content. The effects of Se on compositional traits were ripening stage-dependent, as it enhanced glucose (+8.2%), fructose (+10.0%), and total sugars (+9.4%) in fully ripe fruits, while increasing titratable acidity in less mature ones (+8.2%). Moreover, Se reduced total carotenoids in fully ripe fruits (−13.2%) but increased ascorbic acid during storage (+19.4%), irrespective of ripening stage. Overall, Se biofortification effectively enriched cherry tomatoes and modulated their postharvest behavior. However, the contrasting, stage-dependent effects of Se biofortification on the functional compounds of cherry tomatoes emphasize the need to refine the biofortification strategy in order to achieve a more consistent and comprehensive improvement in fruit quality. Full article

Walnut leaf necrosis causes leaf desiccation and premature abscission, substantially reducing photosynthetic efficiency, impairing fruit development, and ultimately leading to yield loss and quality deterioration. In severe cases, it accelerates branch senescence or even whole-tree mortality, resulting in considerable economic damage to the walnut industry. Rapid and accurate monitoring of this disease is therefore essential for sustainable production. This study aimed to characterize the different stages of walnut leaf necrosis using spectral analysis and develop classification models for stage-specific identification. Leaf samples representing healthy leaves and the early, middle, and late stages of necrosis were analyzed for spectral responses. Sensitive bands were identified using the variable importance in projection (VIP), successive projections algorithm (SPA), and the combined VIP-SPA method, and corresponding vegetation indices were constructed. The selected features were incorporated into classification models based on random forest (RF), extreme gradient boosting (XGBoost), and convolutional neural networks (CNNs). Results revealed that the red-edge (640–700 nm) and near-infrared (720–1000 nm) regions were identified as key diagnostic spectral ranges. Among the vegetation indices evaluated, the Simple Ratio Index (SRI) calculated from reflectance at 705.7 nm and 707.1 nm, the Normalized Difference Index (NDI) using the same band pair, and the Difference Index (DI) derived from 417.1 nm and 638.7 nm emerged as the most sensitive indicators of disease severity. Classification accuracies for different necrosis stages reached 0.9583, 0.9583, and 0.9333, respectively. These findings demonstrate that the identified spectral bands and vegetation indices provide robust tools for monitoring the progression of walnut leaf necrosis. Full article

Optimizing orchard mulching regimes is a pivotal strategy for mitigating the detrimental effects of water scarcity and soil degradation on kiwifruit productivity in the Guanzhong Plain, China. To characterize the integrated effects of varying mulching patterns, a two-year field study was conducted in a kiwifruit (Actinidia deliciosa) orchard, evaluating four treatments: (1) FG: intra-row fabric with inter-row grass (multiple mulch); (2) FN: intra-row fabric with inter-row bare soil; (3) NG: intra-row bare soil with inter-row grass; and (4) NN: intra-row bare soil with inter-row bare soil. Understanding the impacts of these regimes on the edaphic environment, photosynthetic performance, and sugar metabolism is essential for improving kiwifruit production under semi-arid conditions. The results demonstrated that the FG treatment significantly improved soil water storage (SWS), with an increase of 1.83–55.16 mm, and enhanced the soil nutrient content (NH₄⁺-N, NO₃⁻-N, and soil organic matter), thereby optimizing the rhizosphere environment. During the critical phenological stages, the FG treatment increased the leaf photosynthetic parameters, such as the net photosynthetic rate (Pn), transpiration rate (Tr), and stomatal conductance (Gs), while reducing the intercellular CO₂ concentration (Ci). Specifically, grass mulching (FG and NG) elevated the chlorophyll a content during early growth and carotenoids levels throughout reproduction, whereas fabric mulching (FG and FN) enhanced the chlorophyll b content throughout the entire reproductive period. Collectively, these improvements bolstered photosynthetic efficiency and may have contributed to improved carbon allocation and sugar accumulation. All three mulching treatments (FG, FN, and NG) significantly improved the fruit yield-related parameters, including the total fruit number per plant (PFN), single fruit weight (SFW), and yield (Y), as well as the fruit sugar-related indices, such as soluble solids content (TSS), total soluble sugar content (TS), reducing sugar (TRS), and the sugar–acid ratio (SAR). The partial least squares path modeling (PLS-PM) revealed that these improvements were primarily driven by the synergistic optimization of SWS and photosynthetic productivity. Notably, the model identified a physiological trade-off between yield formation and sugar accumulation, while the overall fruit quality exerted a strong positive influence on sugar metabolism. The correlation analysis indicated that the higher fruit sucrose accumulation under the FG and FN treatments were associated with increased sucrose phosphate synthase (SPS) and sucrose synthase (SS) activities, suggesting a potential link between mulching-induced improvements in plant physiological status and sucrose metabolism. These findings suggest that the combined use of intra-row fabric and inter-row grass mulching (FG) provides a sustainable strategy for enhancing soil conditions and fruit quality in water-limited kiwifruit orchards. Full article

Plums are primarily sold fresh, but post-harvest softening and rotting can cause significant economic losses. Understanding quality changes across different maturity stages is crucial for meeting consumer demand for high-quality fruit. This study systematically analyzed the dynamic changes in the physicochemical properties, phenolic content, and cellular structure of ‘French’ plums during six growth and development stages (D1–D6), and comprehensively evaluated fruit quality using correlation analysis and principal component analysis (PCA). The results showed that firmness declined considerably with maturity, whereas the soluble solids content (SSC) increased and titratable acidity (TA) decreased. The peel color progressed from green to a purplish-red. The levels of sugars, such as glucose and fructose, increased, whereas those of major organic acids decreased. Phenolic content varied with developmental stage, with catechin and epicatechin peaking at the D3 stage (pre-color green stage), demonstrating exceptional antioxidant potential. At the D5 stage (purple stage), the fruit exhibits an ideal balance of sweetness, acidity and moderate firmness. Although at the D6 stage (full purple ripe stage), SSC reached its highest levels, fruit cell walls were compromised, vesicles ruptured, and firmness significantly decreased. At this stage, phenolic content declined, indicating that the fruit had attained full maturity. At this maturity level, the fruit should be promptly consumed or processed. Full article

Blue honeysuckle (Lonicera caerulea L.) is subject to environmental stressors, leading to variability in both severe fruit drop during development and fruit quality. Zinc, an essential micronutrient, is critical to sustainable fruit tree production by enhancing yield and nutritional quality. Different forms of zinc fertilizers, particularly nano-zinc versus conventional ionic zinc, exhibit marked differences in absorption efficiency and agronomic performance, thereby determining their practical efficacy. In this two-year study, we evaluated the effects of foliar-applied zinc forms, ZnO nanoparticles (30, 50, and 90 nm) and ionic zinc (ZnCl₂ and ZnSO₄), applied at the young fruit, veraison, and maturity stages on yield and fruit quality. Results showed that ZnO nanoparticles were more effective than ionic zinc at 80 mg/L. In particular, among the ZnO NP treatments, 90 nm ZnO NPs exhibited the best overall effect, significantly improving fruit quality. The 30 nm ZnO NPs treatment performed best in terms of single fruit weight, yield per plant, and fruit firmness. This study highlights the potential of nano-zinc to enhance productivity and quality in blue honeysuckle, providing a theoretical basis for selecting optimal zinc fertilizer types and particle sizes in specialty berry production, with implications for sustainable, high-quality fruit cultivation. Full article