Search Results (2,437)

Despite the economic and medicinal value of the tree species Litsea mollis in southern China’s mountain forests, its wild populations remain understudied in terms of their adaptation to altitudinal gradients. This study examined L. mollis populations spanning altitudes of 760–1550 m in Nanshan Park, Hunan Province, to evaluate variation in leaf traits and fruit quality with elevation changes as well as associations with soil chemical properties. Results revealed that increasing altitude corresponded with higher leaf mass, chlorophyll content, soluble compound levels, enzyme activity, and various fruit quality traits (e.g., longitudinal and transverse diameters, weight, and fat, protein, carbohydrate, vitamin A, essential oil, and citral levels). Conversely, leaf area, specific leaf area, petiole length, fruit shape index, fruit stalk length, and ash content declined as altitude rose. Redundancy analysis indicated that specific leaf area, peroxidase activity, and Malondialdehyde content are the primary leaf characteristics influencing fruit quality, and soil pH and total nitrogen, alkaline nitrogen, and available potassium levels were key chemical factors shaping ecological adaptation and fruit quality of L. mollis along the altitudinal gradient. Overall, L. mollis augments light capture and nutrient acquisition by modifying morphological traits, such as leaf area (26.17%) and specific leaf area (44.32%), facilitating adaptation to low-light and nutrient-poor conditions at lower elevations. At higher elevations, plants preferentially allocate resources to increase leaf mass (33.33%) and chlorophyll content (19.02%), improving photosynthetic efficiency, osmotic regulation, and metabolic enzyme activity. This resource allocation promotes nutrient and secondary metabolite accumulation in fruit, enhancing plant stress resistance and fruit quality. This synergistic relationship represents an adaptive adjustment by L. mollis in allocating growth and reproductive resources across different altitude environments. These findings provide a theoretical framework for understanding altitudinal adaptation in L. mollis and offer practical guidance for its introduction, cultivation, and fruit quality improvement in high-elevation regions. Full article

Drought is one of the major abiotic stress factors limiting crop yield and quality, posing a significant threat to sensitive species like cucumber (Cucumis sativus L.). This study evaluated the potential of bitter melon (Momordica charantia cv. Nusret F₁) and squash (Cucurbita pepo cv. Aygır F₁) as rootstocks to improve drought-related performance in cucumber scions (Akıncı F₁ and Baymali F₁). Grafted and non-grafted plants were grown under two irrigation regimes for 21 days: well-watered control (100% field capacity) and a water-withholding drought treatment. Drought stress significantly reduced morphological parameters across most experimental groups. Under drought conditions, the Akıncı F₁/Aygır F₁ combination showed the highest proline accumulation. This biochemical response was accompanied by pronounced reductions in dry leaf and dry biomass. This pattern suggests that proline accumulation is more closely associated with stress severity than with growth maintenance under drought conditions. Conversely, the Baymali F₁/Aygır F₁ combination maintained relatively higher leaf dry weight under drought, suggesting better growth maintenance under drought. Plants grafted onto Nusret F1 generally produced the lowest biomass but showed enhanced proline synthesis, indicating a stronger stress response despite reduced growth. In conclusion, while Aygır F₁ supports higher growth and biomass maintenance under well-watered conditions, drought responses are strongly influenced by scion-rootstock compatibility and distinct physiological strategies, highlighting the importance of distinguishing growth performance from biochemical stress indicators such as proline accumulation. Full article

Waterlogging at the tillering stage, a key early vegetative growth stage, is increasingly limiting wheat productivity worldwide, but the physiological mechanisms underlying genotypic tolerance are not fully understood. To address this, 23 bread wheat (Triticum aestivum L.) genotypes from five germplasm groups were exposed to 14 days of waterlogging at the tillering stage. Morphological traits including leaf (green area, biomass, and senescent biomass proportion) and the elongation rate of the main culm (cm day⁻¹), plant water status (relative water content, RWC), photosynthetic pigment content (SPAD values; total chlorophyll, TChl; total carotenoids, TCar), and photosynthetic performance (maximal photochemical efficiency of photosystem II, F_v/Fm; actual photochemical efficiency of photosystem II, F_v′/F_m′; net photosyntheis, P_n; stomatal conductance to water vapor, g_s), were assessed. Waterlogging induced strong but highly variable responses among genotypes. Sensitive genotypes showed marked reductions in green biomass (up to ~40–60%), TChl content (up to ~80%), TCar (~70%), and photosynthetic performance, including declines in F_v/F_m, F_v′/F_m′, and P_n. In contrast, tolerant genotypes maintained higher photochemical efficiency, P_n, and pigment content, despite stress exposure, underscoring greater functional resilience. Importantly, morphological stability did not consistently translate into functional performance. Several genotypes maintained green leaf area despite pronounced declines in photosynthetic capacity and pigment content, revealing a decoupling between morphological and physiological responses. Multivariate analysis identified an integrated photosynthetic trait axis strongly associated with yield performance under stress, highlighting that tolerance is primarily driven by the capacity to maintain photosynthetic function rather than green biomass alone. Together, these findings emphasize the importance of preserving both physiological functionality and green leaf area to maintain waterlogging tolerance. Integrated physiological markers (e.g., TChl and TCar content, photochemical quenching, leaf gas exchange traits) enable effective early screening and support function-based selection in wheat breeding programs. Full article

Ziziphus mauritiana Lam. (ber or Indian jujube) is a stress-tolerant dryland fruit tree valued for its nutritious fruit and ability to grow on marginal land. However, the phenotypic, cytotypic and genetic structure of its wild-growing/naturalized germplasm in Pakistan remains poorly characterized. This study provides an integrated assessment of phenotypic, cytotypic and simple-sequence-repeat (SSR) diversity in 100 wild-growing/naturalized accessions collected from Khyber Pakhtunkhwa, Punjab and Sindh to establish a baseline for conservation and germplasm management. We recorded 37 morphological and biochemical traits, estimated ploidy levels by flow cytometry (using diploid Z. jujuba ‘Dongzao’ as a reference), and genotyped a representative subset of 60 accessions with 14 SSR markers scored as a binary presence/absence matrix. Substantial phenotypic variation was observed, especially in canopy architecture, leaf traits and stone-related characteristics; fruit quality traits (total soluble solids, vitamin C, and acidity) varied within a narrower range. Province explained only a modest proportion of phenotypic variation (PERMANOVA R² = 0.059–0.109; p < 0.01), with extensive overlap among regions. Flow cytometry revealed polyploid diversity: hexaploid (2n = 6x = 72) accessions dominated (46.7%), followed by octoploid (2n = 8x = 96; 31.7%) and tetraploid (2n = 4x = 48; 21.7%) cytotypes. SSR analysis showed moderate within-province diversity (Nei’s H ≈ 0.51) but negligible genetic differentiation among provinces (R² = 0.030; p = 0.60; Φ ≈ −0.011), indicating weak geographic structuring. Wild-growing/naturalized Z. mauritiana in Pakistan forms a diverse, weakly structured gene pool in which most variation occurs within rather than among provinces. Sampling for conservation and germplasm management should, therefore, prioritize phenotypic distinctiveness, cytotype representation and ecological context rather than geographic origin alone. Experimental validation of any adaptive or agronomic advantages of particular cytotypes is needed before breeding recommendations can be made. Full article

Horqin Sandy Land suffers from desertification, drought, and low fertility, limiting soybean production. Agrivoltaics provides a promising integrated model; however, the effects of agrivoltaics combined with water–fertilizer management on crop productivity remain unclear. A 2-year field experiment was conducted in a semi-arid area with three treatments, open-field control (Open), shaded area under panels (Under), and light-exposed area inter-panels (Gap). Results showed that photovoltaic systems combined with integrated water–fertilizer management improved soybean yield, soil water, and nutrient conditions. Soybean grain yield was 60.7% and 38.2% higher in the Gap and Under treatments, respectively, than in the Open. The highest yield in the Gap treatment resulted from both enhanced photosynthesis and improved root development. The Under endured light stress but exhibited morphological plasticity (plant height and leaf area increased by 43.1%, 48.2%), and shading alleviated water stress since soil water content was increased by 81.6–119.0% during growing seasons, transpiration rate (Tr) decreased by 55.1%, and leaf water use efficiency (WUE) increased by 48.8%. The Open suffered from soil degradation and water and fertilizer loss, resulting in severely limited yield. Agrivoltaics increased net income by 1466 CNY·ha⁻¹ and improved soil nutrients, demonstrating economic and ecological benefits. Thus, it is a suitable technical model for semi-arid sandy regions. Full article

Mountain ecosystems of Kazakhstan host geographically isolated Sibiraea populations, whose taxonomic status is complicated by overlapping morphology, environmental plasticity, and conflicting nomenclatural treatments. We compared 177 adult shrubs sampled at nine elevation-stratified sublocalities nested within three regional analytical groups (two S. altaiensis groups, n = 60 each; one S. tianschanica group, n = 57) using whole-shrub morphology, quantitative leaf anatomy, four soil variables, and a preliminary four-locus DNA-barcode comparison based on eight taxon-level consensus sequences (two taxa × four loci). One-way ANOVA identified regional-group differences in bush width (F(2,174) = 8.89, p < 0.001), vegetative offshoot number (F(2,174) = 21.85, p < 0.001), leaf-blade thickness (F(2,174) = 21.09, p < 0.001), soil pH (F(2,174) = 72.61, p < 0.001), electrical conductivity (F(2,174) = 41.22, p < 0.001), total nitrogen (F(2,174) = 40.21, p < 0.001), and total carbon (F(2,174) = 39.00, p < 0.001). In contrast, plant height and several vascular traits did not differ significantly, and most soil-trait associations were weak (|$\mathsf{\rho }$| ≤ 0.33). Across ITS, matK/KIM, psbA-trnH, and rbcL, the concatenated alignment showed 99.49% identity and an uncorrected p-distance of 0.51%; psbA-trnH (0.97%) and ITS (0.69%) were the most variable loci. The combined evidence demonstrates regional phenotypic and edaphic differentiation but does not establish independent evolutionary lineages because taxon and geography are confounded, and individual-level genetic variation was sparsely sampled. By establishing measurable baselines for demographic monitoring, habitat protection, provenance-based ex situ conservation, and future restoration, the study directly supports the sustainable management and long-term resilience of rare mountain-plant populations in Kazakhstan. Full article

Inflorescence degeneration in Areca catechu L. is characterized by growth arrest, tissue shrinkage and browning, ultimately compromising functional inflorescence formation and yield stability. To investigate its developmental window and regulatory basis, inflorescences from different leaf positions at the full-bloom stage were analyzed using anatomical observation, morphological measurements, carbohydrate and hormone assays, and RNA-seq-based transcriptomic analysis with qRT-PCR validation. Inflorescence degeneration was mainly concentrated in axillary inflorescences at the third and fourth leaf positions (BY3 and BY4). Compared with adjacent normal inflorescences, degenerated inflorescences showed reduced sucrose, starch and trehalose contents, increased ABA, JA and MeJA levels, and decreased cZR levels. Transcriptomic analysis revealed clear separation between degenerated and normal inflorescences, and differentially expressed genes were enriched in starch and sucrose metabolism, plant hormone signal transduction and transcriptional regulation. Co-expression network analysis identified modules associated with the degeneration window and key physiological traits, highlighting six candidate hub genes: AcAHP2, AcTIFY4B, AcTPS9-2, AcHXK2, AcWRKY3 and AcMPK1. These findings suggest that inflorescence degeneration is closely associated with carbon metabolic imbalance, hormone network remodeling and co-expression network reprogramming within a specific developmental window, providing a basis for future mechanistic studies and control strategies. Full article

Background: Strychnos madagascariensis Poir (Loganiaceae) is a drought-tolerant indigenous fruit tree of East and southern Africa, valued for its food, medicinal, and socio-economic contributions to rural communities. Despite its importance as a candidate food crop, intraspecific morphological and genetic diversity had not previously been characterized, and no simple sequence repeat (SSR) markers had been developed for this species, leaving breeders and conservation planners without the basic diversity baseline needed to prioritize material for domestication. Methods: This study assessed vegetative and reproductive trait variation, variance components, and broad-sense heritability, and SSR-based genetic diversity among 27 morphologically defined S. madagascariensis morphotypes at Bonamanzi Game Reserve, KwaZulu-Natal, South Africa. Three trees were measured per morphotype (81 trees total), over two growing seasons. Genetic diversity was characterized in one representative tree per morphotype using seventeen newly developed SSR loci, the first such markers reported for this species, and analyzed with population structure (STRUCTURE version 2.3.4), PCA, and Nei’s genetic distance. Results: Twenty-seven morphotypes were identified based on leaf colour, shape, hairiness and size, dominated by grey (41%), elongated (59%), less hairy (48%), and medium-sized (>50–90 mm) leaves. Fruit diameter and mass showed the highest inter-morphotype variation (r = 0.949) and also the highest broad-sense heritability (H² = 55.3% and 47.8%, respectively), indicating strong genetic control of these traits and their suitability as targets for selective breeding. Environmental variance exceeded genotypic variance for most traits. A total of 144 alleles were identified across 17 SSR loci (mean 4.24 alleles/locus; mean PIC = 0.31). Population structure gave a preliminary, tentative signal of two genetic clusters (K = 2) with substantial admixture, which we interpret cautiously, given the limited sampling depth. Conclusions: This is the first study to characterize intraspecific morphological variation in S. madagascariensis and the first to develop SSR markers for the species. The results provide a preliminary, single-site framework for conservation genetics and crop improvement that should be validated with larger, multi-site samples. Grey morphotypes GyEvH1, GyEvH2, GyEvH3, GyRlH1 and GyEH2 combined consistent fruiting performance with favourable fruit-trait values and are proposed as priority candidates for further evaluation in domestication and breeding programmes. Full article

Polyploidy is increasingly recognized as a mechanism enhancing physiological resilience in woody fruit crops, yet its functional consequences remain poorly understood in mangoes (Mangifera indica L.), a major tropical species expanding into water-limited environments. Because leaf structure underpins plant water relations and gas exchange, this study evaluated how genome duplication alters foliar traits by comparing diploid and autotetraploid individuals of three polyembryonic cultivars (Gomera-1, Gomera-3, and Kensington Pride). Morphological and anatomical analyses revealed consistent ploidy-related modifications. Autotetraploids exhibited enlarged stomatal guard cells, increased leaf thickness, and changes in mesophyll organization, indicating greater structural investment in leaf tissues. These features are commonly associated with structural strategies that may contribute to water retention and hydraulic regulation, although their direct physiological consequences were not evaluated in the present study. Overall, our results indicate that genome duplication substantially modifies leaf structural traits in mangoes, although the magnitude and direction of these responses were cultivar-dependent. This study provides new insights into how polyploidy reshapes leaf morphology and anatomy in mangoes and advances our understanding of polyploid-induced structural variation in perennial fruit crops. Full article

Fungi are a promising source of biological control agents for the management of phytopathogens such as Cercospora apii, the causal agent of celery early blight. Exploring native fungal isolates associated with agroecosystems near celery production is essential for identifying biocontrol candidates and supporting sustainable, integrated disease management strategies. In this study, fungal isolates were obtained from leaves and soil samples collected across agricultural and natural environments and their antagonistic potential against C. apii was evaluated using in vitro assays. A total of 48 fungal isolates were screened for growth inhibition, of which 12 reduced pathogen colony size by more than 50% in vitro, representing five morphological and taxonomic groups: Aspergillus fumigatus, Fusarium spp., Mucor spp., Neopestalotiopsis sp. and Nigrospora sp. Notably, isolates exhibiting the highest antagonistic activity over time were predominantly derived from leaf samples (p < 0.0001). Two isolates, Mucor nidicola KX3187 and M. irregularis KX3197, consistently showed strong inhibition of C. apii in vitro (up to 85%), and M. nidicola significantly suppressed disease development in planta. This preliminary study identifies Mucor nidicola KX3187 as a potential biocontrol candidate that showed promising activity in greenhouse trials for celery early blight and provides a foundation for future studies to further evaluate its potential as a component of sustainable disease management strategies. Full article

LeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping. The dataset comprises 9708 high-quality leaf scans acquired during collection campaigns conducted between 2015 and 2025, covering seven orchard crop species: apple, pear, sweet cherry, sour cherry, plum, peach, and apricot. In total, the dataset includes 67 cultivar labels. All samples were acquired using flatbed scanning under controlled conditions on a uniform background, ensuring high visual consistency and minimal background variability. The original scans were captured at 1200 dpi and subsequently converted into a public release format at 300 dpi, stored as lossless TIFF images to preserve morphological and textural details. Each image corresponds to a single leaf and is organized in a hierarchical directory structure by species, cultivar, and acquisition year, accompanied by image-level metadata and aggregated species–cultivar–year counts. LeafScans-Orchard is suitable for plant species classification, cultivar recognition, leaf morphology analysis, texture analysis, and general visual feature extraction. In addition to the main release, a representative subset of 300 original 1200 dpi scans is provided to support high-resolution analyses. The dataset is particularly suited for fine-grained classification, morphology-driven analysis, and methodological studies under controlled imaging conditions. Full article

Quantitative phenotyping of pepper seedlings is important for greenhouse plug tray seedling cultivation, but it remains constrained by inefficient manual monitoring, complex greenhouse backgrounds, and growth-stage-dependent discrepancies between two-dimensional image traits and actual leaf biomass. In this study, a cascaded vision framework with stage-specific morphological correction was developed for nondestructive seedling phenotyping. The framework integrated Visual Dynamic Momentum YOLO (VDM-YOLO) for individual seedling localization and growth-stage recognition, Variance Guided Strip Ghost Gated UNet (VSG-UNet) for lightweight, high-resolution leaf segmentation, and a stage-aware correction model for leaf dry biomass estimation. In performance evaluation, VDM-YOLO achieved a mean average precision at an intersection over union threshold of 0.5 (mAP_0.5) of 89.27%, improving mAP_0.5 by 1.82 percentage points over YOLOv12. VSG-UNet achieved a mean intersection over union (mIoU) of 83.9% and a Dice coefficient of 81.8%, while reducing floating point operations (FLOPs) and parameters by 44.2% and 61.2%, respectively, compared with U-Net. After stage-aware calibration, the coefficient of determination (R²) between segmented area and leaf dry weight increased from 0.764 to 0.813, and the root mean square error (RMSE) decreased from 0.0210 g to 0.0190 g. These results demonstrated that the proposed framework provided a proof of concept approach based on RGB images for the nondestructive assessment of leaf area and leaf dry biomass in pepper seedlings under restricted experimental conditions. Full article

Celosia cristata and Alternanthera philoxeroides both belong to the family Amaranthaceae. Of the two species, C. cristata serves as a medicinal herb as well as an ornamental plant, whereas A. philoxeroides is a notorious invasive weed. In 2024, leaf spot symptoms were observed on C. cristata and A. philoxeroides in Jingzhou City, Hubei Province, China. Based on morphological characteristics and multilocus phylogenetic analysis using sequences of ITS, GAPDH, TEF1, RPB2, and Alt a 1, the pathogen isolated from both hosts was identified as the same species, Alternaria alternantherae. However, differences in morphology were observed between the strains from different hosts. Pathogenicity assays confirmed that this species can cross-infect both host plants. In addition, sensitivities of the pathogen to four fungicides (prochloraz, tebuconazole, azoxystrobin, and carbendazim) were tested in vitro and in vivo. The results revealed that the pathogen was highly sensitive to fungicides prochloraz and tebuconazole. These findings provide valuable insights into the management of leaf spot disease on C. cristata and the development of integrated control strategies for A. philoxeroides. Full article

Accurate and robust detection of leaf diseases is a key enabler for precision agriculture and large-scale crop health monitoring. Despite the strong generalization of modern one-stage detectors (e.g., YOLOv8), two domain-specific challenges remain: (i) weak or blurry lesion boundaries hinder precise localization, and (ii) low color contrast between diseased and healthy tissues forces models to rely on subtle texture patterns rather than salient shapes. To tackle these challenges, we reframe the core agricultural disease detection task as the identification of “asymmetric morphological anomalies” and propose a domain-tailored enhancement framework. First, we introduce an Edge Enhancement Module (EEM) that explicitly strengthens boundary-aware representations. Inspired by the natural symmetry of healthy leaves, our EEM is specifically designed to capture symmetry-breaking boundary discontinuities and localized asymmetric edges caused by disease lesions. Our method enhances edge and texture cues that are indicative of disease lesions, which often exhibit local asymmetries and boundary discontinuities. The EEM includes a Differential Normalized Pooling Block (DNPB) that highlights edge responses through discrepancies between max pooling and average pooling, which also models cross-group edge correlations. Second, the Lightweight Texture-Sensitive Feature Enhancement (LTSFE) mechanism amplifies texture-discriminative channels under low-contrast conditions by leveraging complementary global statistics and efficient channel mixing, all with negligible computational overhead. We evaluated our method on a self-constructed dataset of 106,434 images with 225,640 annotations covering diverse crops. Experiments show that the proposed method achieves state-of-the-art accuracy (81.54% mAP@0.5:0.95) while maintaining real-time inference (142 FPS), consistently outperforming strong baselines. Ablations confirm the effectiveness and complementarity of EEM and LTSFE, demonstrating that domain-specific architectural design, inspired by biological symmetry, can substantially improve agricultural vision systems. Full article

With the occurrence of global climate change and the depletion of agricultural water resources, there is a growing need to develop rapid, non-destructive, and autonomous plant health monitoring systems. As an economically valuable crop, Ocimum basilicum L. (basil) is sensitive to changes in water availability and may exhibit stress-related morphological variations under drought and over-irrigation conditions. However, due to the visual similarity of leaf symptoms under drought stress, waterlogging stress, and optimal irrigation conditions, accurately distinguishing these conditions remains challenging in practical applications. To address this challenge, this paper presents an attention-based dual-branch deep learning framework designed to extract both subtle leaf details and channel-related features from high-resolution plant images. By combining the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) mechanism in a parallel structure, the proposed network improves the analysis of high-resolution images with an input size of 720 × 720 pixels. Under controlled environmental conditions, with ground-truth labels obtained using soil moisture sensor measurements, the proposed model was compared with eight deep learning architectures, including DenseNet121, InceptionV3, and VGG16. The proposed model achieved a hold-out evaluation accuracy of 99.54%, outperforming the second-best model, DenseNet121, which achieved 96.43%. In addition, the proposed model reached a class-specific precision value of 100% for the Drought Stress category and achieved an area under the receiver operating characteristic curve of 1.00 under the controlled experimental setting. Taylor Diagram analysis also indicated that the model closely preserved the variability pattern of the reference data. These results suggest that the proposed application-specific framework may support non-destructive basil water-stress detection under controlled conditions. After further validation with larger datasets, different cultivars, variable environmental conditions, and real-world agricultural scenarios, the proposed approach may contribute to precision irrigation management and sustainable agricultural production. The contribution of this study should be interpreted as an application-specific implementation and evaluation of complementary attention mechanisms for controlled-environment basil water-stress classification, rather than as the introduction of a fundamentally new deep learning methodology. Full article