Search Results (3,489)

Oil palm is a major commodity crop in Southeast Asia, particularly in Malaysia and Indonesia, but its productivity is severely threatened by basal stem rot (BSR) and upper stem rot (USR) caused by the white-rot fungus Ganoderma boninense. Infected palms can lose up to 80% yield and die within 6–24 months (young) or 2–3 years (mature). Despite extensive field management efforts, disease incidence continues to rise, especially after replanting. Understanding infection mechanisms and validating fungal virulence factors are crucial for effective control, yet functional genomics in G. boninense has been limited. Previous RNAi-based gene silencing provided initial insights but was constrained by off-target effects and transient activity. Here, we report the first successful application of CRISPR/Cas9 genome editing in G. boninense for functional gene studies. Two genes were targeted: pyrG, essential in uridine monophosphate (UMP) biosynthesis; and hyd-2, encoding a hydrophobin, a potential virulence determinant implicated in host invasion. The disruption of pyrG produced a uracil auxotroph, and the knockout was validated by screening on 5-FOA and validated our system as a functional molecular tool. Disruption of hyd-2 reduced infection capability of the fungus by ~72% to ~91% in vitro. Mutations in both gene disruptions, including insertions, deletions, and substitutions, were confirmed by sequencing. Sequencing analysis also revealed incomplete editing events, as wild-type gene sequences were detected alongside edited alleles in the mutants. Future enhancements should focus on improving editing efficiency of the system. This work establishes a robust platform for functional genetic analysis and dissecting pathogenicity in G. boninense, ultimately advancing strategies to mitigate basal stem rot disease in oil palm. Full article

The genus Agastache comprises aromatic and medicinal plants of increasing interest for the diversification of high-value crops. Among them, Agastache foeniculum (Pursh) Kuntze and Agastache mexicana Lint. et Epling represent two contrasting yet complementary models of multipurpose medicinal and aromatic species. This review critically compares both species with respect to taxonomy, origin, domestication, agronomic performance, phytochemical diversity, biological activities, quality standardization, safety, regulatory status, and industrial applications. Current evidence indicates that A. foeniculum is well adapted to temperate cultivation systems and represents a valuable source of essential oils and polyphenol-rich biomass, whereas A. mexicana offers considerable potential for the development of standardized nutraceutical, cosmetic, and phytopharmaceutical products. Nevertheless, the broader commercialization of both species remains constrained by pronounced chemotype variability, limited multi-site agronomic validation, insufficient clinical evidence, safety concerns associated with certain volatile constituents, and an evolving regulatory framework. The available evidence underscores the need to move beyond descriptive studies toward integrated crop development strategies that combine metabolomics-based chemotype classification, genomic and transcriptomic analyses, breeding for stable and safe phytochemical profiles, polyploidy-assisted improvement, climate-smart cultivation practices, and standardized post-harvest processing. Collectively, these approaches will facilitate the sustainable exploitation of both species and support their transition from promising medicinal and aromatic plants to commercially viable, high-value multipurpose crops for sustainable agriculture. Full article

Binocular stereo vision is a low-cost and scalable 3D perception technology that shows strong potential in agricultural phenotyping and smart agriculture. By estimating depth from multi-view RGB images, it enables non-contact, high-precision sensing of crop structure, canopy morphology, growth dynamics, and livestock traits, providing essential support for digital and intelligent agricultural production. With recent advances in deep learning-based stereo matching, multimodal sensor fusion, and 3D reconstruction, its robustness and accuracy in complex field environments have been significantly improved. This paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques, and emerging supervision strategies such as 3D Gaussian splatting. It further summarizes key applications, including high-throughput phenotyping, fruit localization and robotic harvesting, weed detection and precision spraying, autonomous navigation, and livestock body condition assessment, highlighting its role in multi-task agricultural perception systems. Finally, the paper discusses major challenges, including low-texture matching difficulty, occlusions in complex environments, cross-domain generalization, real-time lightweight deployment, and limited dataset availability. Future directions are outlined in foundation model-based visual perception, self- and weakly supervised learning, multimodal fusion, and edge-efficient model design, aiming to support large-scale deployment in smart agriculture. Full article

Brassica crops, encompassing globally important vegetables and oilseeds, face severe threats from diverse biotic and abiotic stresses. Plant secondary metabolites constitute the chemical foundation of defence, and metabolomics has emerged as an effective systems biology tool for comprehensively dissecting stress-induced metabolic changes. Recent progress in applying metabolomics to elucidate defence mechanisms in Brassica crops is systematically synthesised here. Major stresses confronting Brassica crop production and the metabolic basis of plant defence are first outlined. Current analytical platforms, including liquid chromatography–mass spectrometry, gas chromatography–mass spectrometry, ion mobility spectrometry, and mass spectrometry imaging, are critically evaluated alongside data processing workflows and multi-omics integration strategies. Key defence-related metabolite classes identified in Brassica crops, notably glucosinolates (GSLs) and their hydrolysis products, phenolic compounds, and lipid-derived signalling molecules, are surveyed with emphasis on their respective functions in biotic and abiotic stress responses. Metabolomics has been instrumental in revealing distinct metabolic reprogramming patterns triggered by diverse stresses, including pathogen infection, insect herbivory, drought, salinity, temperature extremes, and heavy metal stress. Metabolomics-informed crop improvement strategies, including marker-assisted breeding, genetic and metabolic engineering, and precision agronomic practices, are discussed together with current technical bottlenecks and future directions involving artificial intelligence, metabolic modelling, and spatial metabolomics. The compiled knowledge provides a comprehensive reference for leveraging metabolomics to enhance stress resilience and sustainable production of Brassica crops. Full article

The angular distribution of surface-strands in oriented strand board (OSB) is closely associated with board mechanical properties and mat formation quality. By acquiring surface images through vision sensing and combining them with deep learning-based segmentation, the angle classes of OSB surface-strands can be segmented and statistically analyzed automatically. However, OSB surface images contain complex strand textures, blurred boundaries, local adhesion between adjacent strands, and subtle differences among neighboring angle classes. To address these challenges, this study proposes a direction-aware dual-branch semantic segmentation network (DiBiNet) for pixel-level segmentation of surface-strand angle classes. OSB surface images were collected using a Hikrobot MV-CE120-10UC color industrial camera, and an 11-class dataset was constructed, including the background and ten angle classes from 0° to 90°. The samples were cropped to 512 × 512 pixels, and an improved angle-semantic-consistent Copy–Paste strategy was used to augment the training data. DiBiNet enhances directional feature representation through a Directional Strip Detail Enhancement Module, improves semantic feature modeling by combining MobileNetV3-Small with a DS-MobileViT Block, and fuses the two branches through a Bilateral Gated Fusion Module. Considering the continuity among angle classes, Direction Vector Auxiliary Supervision is introduced to map discrete angle labels into continuous direction vectors, thereby improving discrimination among neighboring classes. Experiments on the self-constructed dataset show that DiBiNet achieves a mean Intersection over Union (mIoU) of 0.8532, an overall pixel accuracy (Acc) of 0.8823, and a Dice coefficient of 0.8623, outperforming several representative semantic segmentation models. After 8-bit integer (INT8) + 16-bit floating-point (FP16) mixed quantization, the model achieves a neural processing unit (NPU) inference speed of 34.0 frames per second (FPS) on the RK3588 platform, demonstrating its potential for vision-based sensing and edge AI inspection. Full article

Macadamia (Macadamia spp.) is an economically important nut crop whose severe recalcitrance to genetic transformation substantially hinders progress in functional genomics and molecular breeding. To overcome this critical technical bottleneck, this study established a highly efficient and broadly applicable in planta hairy root genetic transformation system with integrated visual screening. This system utilizes an Agrobacterium rhizogenes-mediated transformation method, employing multiple visual reporter gene systems (DsRed2, eGFP, RUBY, and AtPAP2) to achieve antibiotic-independent and non-destructive screening of transgenic roots. Notably, the system innovatively incorporates the air layering (marcotting) technique to extend in planta genetic transformation to branches of mature trees in the field. By circumventing the stringent sterile conditions required for conventional in vitro tissue culture, this approach achieves a largely genotype-independent transformation across open-pollinated seedlings with diverse genetic backgrounds (A4, GR1, HAES900, and O.C.). The transgenic hairy root induction frequencies ranged from 39.25% to 47.38%, although the GR1 genotype exhibited a notable developmental stage-dependent decline in transformation efficiency. Furthermore, transgenic hairy roots were successfully induced on mature tree branches, with a maximum induction rate of 28.2%. Gene expression analyses confirmed the stable, high-level expression of the target transgenes in all the transgenic hairy root lines. This in planta transformation system provides a reliable in vivo experimental platform for the rapid functional validation of candidate genes and the investigation of root biology in Macadamia. Moreover, it establishes a novel strategy for plant regeneration via root-to-shoot organogenesis, offering a promising avenue for the genetic improvement of recalcitrant woody plants. Full article

Cereal crops, including wheat and barley, are essential for global food security, but their productivity is strongly affected by nitrogen availability and water limitation. This study investigated the phenotypic responses of two commercially significant spring wheat cultivars, Videodur (DU) and Sensas (SW), and two spring barley cultivars, Tiroler Imperial (SG1) and Amidala (SG2), exposed to two nitrogen regimes, low nitrogen at 25 kg N/ha (N25) and high nitrogen at 130 kg N/ha (N130), under drought and well-watered conditions. Plants were monitored from the late vegetative stage through maturity under controlled multivariable climatic conditions similar to field settings. A high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence. The results revealed cultivar-specific responses to combined nitrogen and drought stress. Under drought conditions, the high nitrogen treatment (N130) increased plant temperature (Tplant) for barley (cv. SG1) and wheat (cv. SW) compared to N25, thereby accelerating early maturation. However, the decline in chlorophyll was not uniformly faster across all cultivars tested. The DU cultivar exhibited superior chlorophyll absorption and reflectance, indicating better drought adaptation compared to other tested species. The high nitrogen treatment (N130) reduced water use efficiency (WUE) in the SW and SG2 cultivars compared to N25, implying that these cultivars used more water. Enhanced nitrogen did not consistently improve water use efficiency but did accelerate the growth cycle. SG2 was particularly sensitive to drought, showing declines in vegetation indices, except for the Water Content Index, highlighting the need for precise water and nitrogen management. Overall, the integration of hyperspectral, thermal, RGB, and water use measurements enabled the identification of trait signatures linked to drought adaptation, nitrogen response, maturation, and senescence. These findings provide practical insights for optimizing nitrogen and irrigation management and for supporting breeding strategies aimed at improving cereal crop resilience under climate-change-associated stress conditions. Full article

Drought poses a major threat to global food security, making it critical to understand the molecular mechanisms underlying plant responses to water scarcity. Epigenetic modifications, including DNA methylation and histone alterations, play central roles in regulating genes and hormonal pathways essential for drought adaptation. MicroRNAs, while primarily functioning as post-transcriptional regulators, can also influence epigenetic pathways and contribute to chromatin remodelling, suggesting a role in modulating epigenetic memory. Investigating these interactions is essential for understanding how plants integrate epigenetic and post-transcriptional regulation during stress. Epigenetic memory in drought-adapted plants provides insights into the transgenerational inheritance of adaptive traits and reveals how plants balance genome stability with flexibility. The crosstalk between epigenetic mechanisms and hormonal signalling is crucial for fine-tuning gene expression, promoting drought resilience. This review proposes a conceptual framework integrating epigenetic, hormonal, and miRNA-mediated regulation of drought responses. It emphasizes the impact of advanced technologies, such as bisulfite sequencing and CRISPR-Cas9, in dissecting plant epigenetic responses to drought. These approaches improve our understanding of drought tolerance mechanisms and offer promising strategies for developing resilient crops for sustainable agriculture. However, direct evidence linking epitranscriptomic modifications to long-term drought memory remains limited, and this emerging regulatory layer requires further experimental validation. Full article

This study evaluated the effects of placing wood chips (F-Wood chip) or pumice (F-Pumice) at the bottom of poultry feather compost on open-field ginger soilless media production. Root control bags were prepared with 10 L of wood chips or pumice overlain by 20 L of feather compost, with three ginger rhizomes planted. Crops were drip-irrigated without synthetic fertilization and replenished with compost three times. Results indicated that bottom-placed wood chips or pumice improved water infiltration. Ginger yield was significantly higher in the F-Wood chip treatment than in the F-Pumice, with fresh weights of 3.4 and 2.5 kg, and dry weights of 451.8 and 332.7 g, respectively. Furthermore, F-Wood chip significantly increased rhizome calcium levels. Although no significant differences were observed between treatments regarding leaf and post-harvest media nutrient contents, the F-Wood chip group exhibited higher microbial abundance (9.5 ± 4.5 × 10⁵ CFU ⁻¹) and greater endophytic diversity, spanning 7 genera and 11 species with potential plant growth-promoting and stress-resistance functions. Overall, this innovative integrated crop management strategy demonstrates great potential to substitute for fossil-fuel-based chemical fertilizers, this innovative production mode eliminates the need for fossil-fuel-based chemical fertilizers, offering an applicable and sustainable soilless cultivation solution for open-field ginger production under extreme weather conditions like typhoons and heavy rainfall. Full article

Symbiotic nitrogen fixation (SNF) in forage legumes is a key biological process that provides nitrogen inputs and enhances soil fertility in agroecosystems. Enhancing SNF efficiency helps reduce dependence on synthetic nitrogen fertilizers and improve nitrogen use efficiency. Humic substances (HS) have been reported to stimulate root development and nutrient acquisition in different crop species. However, their effect on root nodulation and SNF in forage legumes remains poorly understood. This study investigated the effects of a humic acid-based soil amendment (liquid Humalite) on root growth, nodulation, SNF, and plant nitrogen acquisition in red clover. Red clover seedlings were grown in a modified Leonard jar system placed under controlled environmental conditions and treated with 0.1, 0.2, 0.4, and 0.8% (v/v) liquid Humalite. Plant biomass, root morphological traits, nodulation parameters, SNF, and shoot nitrogen accumulation were assessed after 6 weeks. SNF was quantified using the ¹⁵N-isotope dilution method. Application of liquid Humalite significantly affected plant growth and SNF responses in red clover. The 0.2% liquid Humalite treatment significantly increased root and total plant biomass by 35% compared to the untreated control. Total root length, surface area, and volume increased by 20–25% at 0.2% liquid Humalite. Nodule number, nodule dry weight, shoot nitrogen concentration, and shoot C:N ratio did not differ among treatments. However, the percent nitrogen derived from the atmosphere (%Ndfa) was significantly higher under the 0.2% liquid Humalite treatment (47%) as compared to the untreated control (28%). Under the 0.2% liquid Humalite, total fixed and accumulated shoot nitrogen increased by 134% and 37%, respectively, compared to the control. Overall, these findings indicate that humic substances may enhance SNF and nitrogen accumulation in red clover, highlighting their potential as a management strategy to improve forage productivity. Full article

Early and accurate plant disease detection is essential for reducing crop losses and supporting sustainable agricultural management. Although deep learning-based approaches have achieved strong performance in plant disease analysis, many existing models require substantial computational resources, which limits their use in resource-constrained agricultural environments. This study proposes ShuffleNetV2-hSimKD, a lightweight integration framework for plant disease detection. It adopts ShuffleNetV2 as the backbone and incorporates the parameter-free SimAM attention mechanism to enhance disease-related feature representation without introducing additional learnable parameters. In addition, the standard ReLU activation function is replaced with h-swish to improve nonlinear feature extraction and preserve informative feature responses. A hybrid knowledge distillation strategy is further employed to transfer both output-level and feature-level knowledge from a high-capacity teacher model to the lightweight student network during training. Unlike previous studies that apply these techniques in isolation, ShuffleNetV2-hSimKD synergistically integrates parameter-free SimAM, h-swish optimization, and hybrid KD to overcome the representation limitations of lightweight backbones in subtle disease symptom detection. The proposed framework was evaluated on a balanced subset of the PlantVillage dataset, in which leaf images were categorized as healthy or diseased. ShuffleNetV2-hSimKD achieved an accuracy of 90.41% with only 1.4M parameters and 151M FLOPs. Compared with representative lightweight Convolutional Neural Networks (CNNs), the proposed model achieved improved accuracy and recall while maintaining low computational complexity. These results demonstrate that ShuffleNetV2-hSimKD provides an effective balance between detection performance and computational efficiency, highlighting its potential as a lightweight candidate for plant disease detection in resource-constrained agricultural scenarios. Full article

Regenerative agriculture is increasingly promoted as a pathway towards sustainable food production, climate resilience, and the restoration of soil ecosystem functions. However, evidence on the actual uptake of regenerative practices and the factors influencing their adoption remains limited. This study compared the self-reported prevalence of 17 soil-health-oriented regenerative practices among farmers in Poland, Germany, and Belarus using a questionnaire survey conducted in 2025 (N = 150). The survey also examined farmers’ motivations, perceived barriers, knowledge sources, and definitions of regenerative agriculture. Adoption frequencies were assessed using a five-point Likert scale and analysed using non-parametric statistical methods. Several practices, including crop rotation and soil pH management, were widely implemented across all three countries and showed only slight variation. In contrast, more complex, system-based practices, such as agroforestry, biological soil monitoring, and crop–livestock integration, showed lower and more variable levels of adoption. Additional subgroup analyses were conducted to assess the robustness of the observed cross-country patterns. Although some associations weakened after stratification, many significant differences persisted. Across all countries, improving soil health was the primary motivation for adopting regenerative agriculture, whereas financial constraints and limited equipment access were the main barriers. Digital media served as the primary source of knowledge about regenerative agriculture across the surveyed countries, although in Belarus, peers and neighbours also represented a highly important source of information. Farmers in all three countries expressed a preference for online communication channels for further learning about regenerative agriculture; however, Polish and Belarusian farmers prefer social media, whereas German farmers preferred webinars and dedicated websites. In-person training sessions also attracted considerable interest among Polish and Belarusian farmers, but were the least preferred information source among German respondents. On the basis of these results, targeted investment support and direct financial incentives appear to be key priorities for promoting the further uptake of regenerative agriculture across all surveyed countries. However, communication and knowledge-transfer strategies are likely to require greater adaptation to country-specific preferences, although digital media are likely to represent the most effective primary channel for disseminating information on regenerative agriculture. Full article

Splicing factors, as core determinants of splice-site selection and dynamic spliceosome assembly, play pivotal roles in stress responses. This review systematically categorizes splicing factors involved in plant abiotic stress responses according to their functions as major spliceosomal components, dividing them into small nuclear ribonucleoproteins (snRNPs) and associated components, spliceosome assembly and disassembly factors, splicing regulatory factors, and proteins related to non-canonical RNA splicing. On this basis, we summarize their regulatory mechanisms of these factors under salt, drought, abscisic acid (ABA) signaling, temperature, and oxidative stresses. Through analyses across multiple species—including Arabidopsis thaliana, rice, maize, soybean, and wheat—we reveal both the evolutionary conservation and species-specific divergence of splicing-factor-mediated regulation. Currently, a large amount of research is still mainly at the transcriptome analysis or single phenotype validation stages, lacking in-depth analysis of direct targets, splicing isomer functions, and molecular mechanisms. Furthermore, current research is heavily concentrated on Arabidopsis, with relatively insufficient functional validation and breeding applications in crops such as maize and wheat. Despite substantial progress, several bottlenecks remain for translational applications in breeding, such as functional redundancy among splicing factor family members, growth penalties associated with overexpression, and tissue-specific and developmental-stage-dependent effects. To address these challenges, we discuss promising strategies, including CRISPR/Cas9-mediated splice-site editing, the use of inducible or tissue-specific promoters, and targeted modulation of upstream kinases, although extensive field trials and rigorous evaluations remain necessary. Collectively, this review provides a theoretical framework for understanding the roles of splicing factors in RNA-level regulation of plant stress adaptation and highlights their potential for breeding improvement. Full article

The mutualistic association between plants and their seed-associated microbiota has emerged as a key determinant of crop productivity, influencing plant nutrition, immunity, and tolerance to abiotic stress. Seed biopriming, the controlled application of beneficial microorganisms to seeds before sowing, exploits this interaction to enhance germination, seedling establishment, and stress resilience. Unlike conventional chemical priming, seed biopriming induces coordinated molecular reprogramming through changes in the seed metabolome, proteome, and epigenome. This review synthesizes current evidence demonstrating that seed biopriming promotes the accumulation of osmoprotectants, strengthens antioxidant defenses, enhances secondary metabolism, and generates priming-specific proteomic responses. We further examine how these changes interact with phytohormonal signaling networks and epigenetic mechanisms, including DNA methylation, histone modification, and small RNA-mediated regulation, to establish stress memory and improve plant adaptation. The review also discusses recent advances in synthetic microbial communities and nanobiotechnology for improving inoculant stability and efficacy. Despite promising progress, large-scale application remains constrained by inconsistent field performance, formulation stability, and regulatory challenges. Finally, we highlight the integration of multi-omics and artificial intelligence as promising approaches to improve mechanistic understanding, optimize microbial selection, and accelerate the development of reliable seed biopriming strategies for sustainable agriculture under climate change. Full article

The Hetao Irrigation District in arid northwestern China presents a significant challenge in balancing agricultural intensification and water conservation, particularly in its terminal lake, Wuliangsu Lake. This study examined how changes in Land Use/Land Cover (LULC) and cropping structures influenced the lake’s water quality. By using remote sensing data for LULC classification and agricultural statistics for crop composition, we analyzed the spatio-temporal variation in LULC and cropping structure in the irrigation district and quantified the associated agricultural non-point source pollution loads (total nitrogen, total phosphorus, and chemical oxygen demand) entering the lake. A calibrated Environmental Fluid Dynamics Code model was applied to evaluate water quality responses to cropping structure optimization. Our findings revealed significant shifts in LULC and cropping structure during the study period, driven by agricultural intensification, ecological restoration policies, urbanization, market forces, and national food security strategies. Concurrently, agricultural non-point source pollution loads into the lake showed a steady declining trend from 2018 to 2023, with total nitrogen (TN) decreasing by 15%, total phosphorus (TP) by 16.9%, and chemical oxygen demand (COD) by 19.4%. Model simulations demonstrated that optimizing the cropping structure, specifically by reducing the area of high-fertilizer crops (sunflower) and expanding low-fertilizer crops (spring wheat) and forage crops for ecological purposes, could further improve lake water quality. Under the intensive adjustment scenario, the inflow loads of TN, TP, and COD decreased by 10%, 11.7%, and 10.9%, respectively, while the corresponding in-lake concentrations decreased by 22.1%, 19.8%, and 18.7%, respectively. TP exhibited the highest sensitivity to such adjustments. By linking cropping structure adjustments with hydrodynamic-water quality modeling, this study provides a quantitative framework for assessing water quality responses in arid irrigated systems, offering a scientific basis for balancing agricultural production and water ecosystem protection in the Hetao district and similar regions. Full article