Search Results (748)

Precise identification of weeds at the maize seedling stage is pivotal for implementing Site-Specific Weed Management and minimizing herbicide environmental pollution. However, the performance of existing lightweight detectors is severely bottlenecked by unstructured field environments, characterized by the “green-on-green” spectral similarity between crops and weeds, diminutive seedling targets, and complex mutual occlusion of leaves. To address these challenges, this study proposes SLD-YOLO11, a topology-reconstructed lightweight detection model tailored for complex field environments. First, to mitigate the feature loss of tiny targets, a Lossless Downsampling Topology based on Space-to-Depth Convolution (SPD-Conv) is constructed, transforming spatial information into depth channels to preserve fine-grained features. Second, a Decomposed Large Kernel Attention (D-LKA) mechanism is designed to mimic the wide receptive field of human vision. By modeling long-range spatial dependencies with decomposed large-kernel attention, it enhances discrimination under severe occlusion by leveraging global structural context. Third, the DySample operator is introduced to replace static interpolation, enabling content-aware feature flow reconstruction. Experimental results demonstrate that SLD-YOLO11 achieves an mAP@0.5 of 97.4% on a self-collected maize field dataset, significantly outperforming YOLOv8n, YOLOv10n, YOLOv11n, and mainstream lightweight variants. Notably, the model achieves Zero Inter-class Misclassification between maize and weeds, establishing high safety standards for weeding operations. To further bridge the gap between visual perception and precision operations, a Visual Weed-Crop Competition Index (VWCI) is innovatively proposed. By integrating detection bounding boxes with species-specific morphological correction coefficients, the VWCI quantifies field weed pressure with low cost and high throughput. Regression analysis reveals a high consistency (R² = 0.70) between the automated VWCI and manual ground-truth coverage. This study not only provides a robust detector but also offers a reliable decision-making basis for real-time variable-rate spraying by intelligent weeding robots. Full article

Late embryogenesis abundant (LEA) proteins play an essential role in plant growth under various abiotic stresses. In this study, we identified 23 RcLEA genes in Rosa chinensis ‘Old Blush’ and subsequently grouped them into eight clades according to phylogenetic relationships and conserved domain features by bioinformatics methods. And conserved protein motifs and gene structure are also analyzed. The cis-regulatory elements of RcLEA promoter are enriched with cis-regulatory elements relevant to abiotic stress adaptation. Comparative transcriptomics between two species revealed tissue-specific and cold-induced expression differences, highlighting distinct functional roles of LEA genes in growth and abiotic stress tolerance between Rosa chinensis ‘Old Blush’ and Rosa beggeriana. Furthermore, Quantitative Real-Time PCR (qRT-PCR validation confirmed divergent cold-responsive expression profiles of LEA genes in R. chinensis ‘Old Blush’ compared with the highly cold-tolerant R. beggeriana in four LEA homologous genes. These findings indicated that LEA acts as a cold-response gene in roses and provide foundation to breed cold-tolerant varieties of roses. Full article

This study investigates whether fecal microbiota transplantation (FMT) can alleviate gut microbiota dysbiosis induced by a high-fat diet (HFD) through modulation of fatty acid metabolism, competition for nutrients, production of short-chain fatty acids (SCFAs), and restoration of mucus layer integrity. To elucidate the mechanisms by which FMT regulates colonic microbial function and host metabolic responses, 80 male Bal b/c mice were randomly assigned to four experimental groups (n = 20 per group): Normal Diet Group (NDG), High-Fat Diet Group (HDG), Restrictive Diet Group (RDG), and HDG recipients of NDG-derived fecal microbiota (FMT group). The intervention lasted for 12 weeks, during which body weight was monitored biweekly. At the end of the experiment, tissue and fecal samples were collected to assess digestive enzyme activities, intestinal histomorphology, gene expression related to gut barrier function, and gut microbiota composition via 16S rRNA gene sequencing. Results showed that mice in the HDG exhibited significantly higher final body weight and greater weight gain compared to those in the NDG and RDG (p < 0.05). Notably, FMT treatment markedly attenuated HFD-induced weight gain (p < 0.05), reducing it to levels comparable with the NDG (p > 0.05). While HFD significantly elevated the activities of α-amylase and trypsin (p < 0.05), FMT supplementation effectively suppressed these enzymatic activities (p < 0.05). Moreover, FMT ameliorated HFD-induced intestinal architectural damage, as evidenced by significant increases in villus height and the villus height-to-crypt depth ratio (V/C) (p < 0.05). At the molecular level, FMT significantly downregulated the expression of pro-inflammatory cytokines (IL-1β, IL-1α, TNF-α) and upregulated key tight junction proteins (Occludin, Claudin-1, ZO-1) and mucin-2 (MUC2) relative to the HDG (p < 0.05). 16S rRNA analysis demonstrated that FMT substantially increased the abundance of beneficial genera such as Lactobacillus and Bifidobacterium while reducing opportunistic pathogens including Romboutsia (p < 0.05). Furthermore, alpha diversity indices (Chao1 and ACE) were significantly higher in the FMT group than in all other groups (p < 0.05), indicating enhanced microbial richness and community stability. Functional prediction using PICRUSt2 revealed that FMT-enriched metabolic pathways (particularly those associated with SCFA production) and enhanced gut barrier-related functions. Collectively, this study deepens our understanding of host–microbe interactions under HFD-induced metabolic stress and provides mechanistic insights into how FMT restores gut homeostasis, highlighting its potential as a therapeutic strategy for diet-induced dysbiosis and associated metabolic disorders. Full article

The temperate Himalayan states of Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Ladakh, Sikkim, and Arunachal Pradesh in India face unique agro-ecological challenges across agriculture and allied sectors, including pest and disease pressures, inefficient resource use, post-harvest losses, and fragmented supply chains. This review systematically examines 21 critical problem areas, with three key challenges identified per sector across agriculture, agricultural engineering, fisheries, forestry, horticulture, sericulture, and animal husbandry. Artificial Intelligence (AI) and Machine Learning (ML) interventions, including computer vision, predictive modeling, Internet of Things (IoT)-based monitoring, robotics, and blockchain-enabled traceability, are evaluated for their regional applicability, pilot-level outcomes, and operational limitations under temperate Himalayan conditions. The analysis highlights that AI-enabled solutions demonstrate strong potential for early pest and disease detection, improved resource-use efficiency, ecosystem monitoring, and market integration. However, large-scale adoption remains constrained by limited digital infrastructure, data scarcity, high capital costs, low digital literacy, and fragmented institutional frameworks. The novelty of this review lies in its cross-sectoral synthesis of AI/ML applications tailored to the Himalayan context, combined with a sector-wise revenue-loss assessment to quantify economic impacts and guide prioritization. Based on the identified gaps, the review proposes feasible, context-aware strategies, including lightweight edge-AI models, localized data platforms, capacity-building initiatives, and policy-aligned implementation pathways. Collectively, these recommendations aim to enhance sustainability, resilience, and livelihood security across agriculture and allied sectors in the temperate Himalayan region. Full article

The conventional method of flue gas desulfurization gypsum (FGDG) application, i.e., blending with flood irrigation, is hindered by low water efficiency and significant amendment loss due to runoff and uncontrolled leaching, particularly in arid and semi-arid regions in which water scarcity is a major constraint. This study aimed to evaluate a novel integration of FGDG band application with drip irrigation to enhance targeting and resource efficiency. A laboratory-scale experiment investigated the effects of two FGDG application methods (band and blend application) and drip rates (0.3 and 0.6 L h⁻¹) on soil water movement and chemical properties. Band application significantly accelerated initial wetting front advancement by up to 44.9 cm h⁻¹ near the emitter and sustained horizontal propagation, while blend application promoted a more uniform water distribution. Chemically, band application created localized zones of reduced pH (7.57–8.62) and elevated water-soluble Ca²⁺ (up to 492.2 mmol kg⁻¹), facilitating a 79.1% reduction in exchangeable Na⁺ near the emitter. In contrast, blend application resulted in broader but shallower amendment distribution, reducing exchangeable sodium percentage uniformly to 1.99–4.16% across the soil profile. The combination of banded FGDG and drip irrigation achieves targeted amelioration, with superior Na⁺/Ca²⁺ exchange and favorable moisture dynamics resulting from the synergy between amendment placement and water delivery. This approach is a viable strategy for precision reclamation in arid regions. Full article

Methane (CH₄), a significant greenhouse gas, ranks second only to carbon dioxide in its contribution to global warming. The application of Chinese herbs as a strategy to mitigate CH₄ emissions in ruminants has shown promise. However, there is limited information regarding the efficacy of Chinese herb straw in reducing CH₄ emissions in ruminants. This research aimed to investigate the beneficial effects of varying levels of Astragalus straw supplementation on methane emissions and to elucidate the underlying molecular mechanisms. The study examined the effects of different supplementation levels (0%, 5%, 10%, 15%, 20%) on in vitro rumen fermentation, CH₄ emissions, and ruminal microbial community in Lanzhou fat-tailed sheep using an in vitro fermentation method. The findings indicated that IVDMD, gas production, and CH₄ production significantly decreased with increasing levels of Astragalus straw supplementation (p < 0.05). Simultaneously, the lowest levels of AA, AA/PA, and NH₃-N, along with the highest concentrations of PA, BA, and MCP, were observed in the 20% supplementation group after 48 h of fermentation. In addition, supplementation with Astragalus straw resulted in an increased abundance of Bacteroidota, Spirochaetota, and Actinobacteriota, while decreasing the abundance of Firmicutes, Fibrobacterota, and Verrucomicrobiota. At the genus level, there was an observed increase in the abundance of Prevotella and Streptococcus, accompanied by a decrease in Rikenellaceae_RC9_gut_group. In conclusion, the supplementation of Astragalus straw has the potential to reduce CH₄ production by altering ruminal fermentation patterns, fermentation parameters, and microbial dynamics. Full article

Lycium barbarum L. is an important economic crop in Qinghai province, China. However, root rot seriously reduces the economic results of L. barbarum. Here, we collected the diseased L. barbarum roots from Nuomuhong Farm of Haixi Mongolian and Tibetan Autonomous Prefecture, Qinghai Province, China, to clarify the diversity, pathogenicity, and biological characteristics of its root rot pathogens. A total of 125 isolates were collected, and based on morphological characteristics and rDNA ITS, TEF-1α, and RPB2 genes sequence analysis, they were identified as Fusarium equiseti, F. avenaceum, F. solani, F. citri, F. acuminatum, F. culmorum, F. sambucinum, F. incarnatum, F. oxysporum, F. tricinctum, Microdochium bolleyi, and Clonostachys rosea. These fungi were used to inoculate the roots of 1-year-old L. barbarum seedlings using scratching and root-irrigation inoculation methods, and all isolates caused root rot. This is the first report that M. bolleyi, F. avenaceum, and F. citri caused root rot in L. barbarum. And the best media, the lethal temperatures, and the optimum carbon sources and nitrogen sources of the 12 pathogen species were determined in this study. Moreover, our findings provide a theoretical foundation for root rot management in the future. Full article

(1) Background: This study used Qingjian No. 1 forage pea (Pisum sativum L.) as a plant material to study its metabolic mechanisms in response to different stresses, given that saline–alkali stress and drought stress often occur simultaneously in natural environments and severely affect the growth and yield of forage pea, while the regulatory network underlying the adaptation of forage pea to combined stress remains poorly elucidated. (2) Methods: The metabolic mechanisms of forage pea in response to different stresses were elucidated by integrating phenotypic, physiological, and metabolomic analyses. (3) Results: The results show that compared to the control, all stress treatments significantly inhibited seed germination and seedling growth, with the combined saline–alkali and drought stress exhibiting the strongest inhibitory effect. In terms of physiological and biochemical responses, peroxidase (POD) activity increased with the complexity of the stress, with the highest POD activity observed under combined saline–alkali and drought stress, showing a 61.71% increase compared to the control (p < 0.05). Non-targeted metabolomic analysis revealed that isoflavone biosynthesis, nucleotide metabolism, and cutin–suberin–wax biosynthesis are the core responsive pathways. Correlation analysis revealed that isocorydine and phosphatidylinositol phosphate showed strong positive correlations with the vigor index, main root length, and superoxide dismutase (SOD) activity, and glycerophospholipid metabolites were positively correlated with ferric ion-reducing antioxidant capacity. (4) This study deepens understanding of the stress resistance mechanisms in forage peas and provides a theoretical basis for stress-resistant forage pea breeding. Full article

Behavior recognition and multi-object tracking of dairy cows in free-style cow barn environments play a crucial role in monitoring their health status and serve as an essential means for intelligent scientific farming. This study proposes an efficient embedded algorithm, MTW-BYTE, for dairy cow behavior recognition and multi-object tracking. It addresses challenges in free-style cow barn environments, including the impact of lighting variations and common occlusions on behavior recognition, as well as trajectory interruptions and identity ID switching during multi-object tracking. First, the MTW-YOLO cow behavior recognition model is constructed based on the YOLOv11n object detection algorithm. Replacing parts of the backbone network and neck network with MANet and introducing the Task Dynamic Align Detection Head (TDADH). The CIoU loss function of YOLOv11n is replaced with the WIoU loss. The improved model not only effectively handles variations in lighting conditions but also addresses common occlusion issues in cows, enhancing multi-scale behavior recognition capabilities and improving overall detection performance. The improved MTW-YOLO algorithm improves Precision, Recall, mAP50 and F1 score by 4.5%, 0.1%, 1.6% and 2.2%, respectively, compared to the original YOLOv11n model. Second, the ByteTrack multi-object tracking algorithm is enhanced by designing a dynamic buffer and re-detection mechanism to address cow trajectory interruptions and identity ID switching. The MTW-YOLO algorithm is cascaded with the improved ByteTrack to form the multi-target tracking algorithm MTW-BYTE. Compared with the original multi-target tracking algorithm YOLOv11n-ByteTrack (a combination of YOLOv11n and the original ByteTrack), this algorithm improves HOTA by 1.1%, MOTA by 3.6%, MOTP by 0.2%, and IDF1 by 1.9%, reduces the number of ID changes by 11, and achieves a frame rate of 43.11 FPS, which can meet the requirements of multi-target tracking of dairy cows in free-style cow barn environments. Finally, to verify the model’s applicability in real-world scenarios, the MTW-BYTE algorithm is deployed on an NVIDIA Jetson AGX Orin edge device. Based on real-time monitoring of cow behavior on the edge device, the pure inference time for a single frame is 16.62 ms, achieving an FPS of 29.95, demonstrating efficient and stable real-time behavior detection and tracking. The ability of MTW-BYTE to be deployed on edge devices to identify and continuously track cow behavior in various scenarios provides hardware feasibility verification and algorithmic support for the subsequent deployment of intelligent monitoring systems in free-style cow barn environments. Full article

Understanding the genetic basis of agronomic traits in quinoa adapted to the Qinghai–Tibet Plateau is essential for developing high-yield cultivars, as conventional breeding is constrained by limited molecular tools. In this study, 300 cultivated accessions were evaluated for five quantitative traits, and whole-genome resequencing generated 3.69 million high-quality SNPs. Population structure analysis and genome-wide association study (GWAS) were conducted, with integration of seed developmental transcriptomes to refine trait-associated loci. A highly admixed genetic background (K = 7) was revealed, and 11 significant QTLs across seven chromosomes were identified, involving genes related to metabolism, transport, and cell-wall formation. Among these, CesA4 (CQ042210) showed a strong association with thousand grain weight (TGW) and a distinct expression maximum at the early seed-filling stage. These results provide a genomic framework for understanding trait variation in plateau-adapted quinoa and highlight promising targets for marker-assisted breeding. Full article

Agricultural listed companies are key nodes in the agricultural industry chain, providing capital, technology and market access to upstream producers and downstream processors. When these firms face delisting risk, the resilience and sustainability of the industry chain are threatened. Using data from 897 observations of Chinese A-share listed companies in the agriculture, forestry, animal husbandry, and fishery sector over 2010–2021, this study links multidimensional firm vulnerability to subsequent delisting risk. We construct 30 internal and external indicators covering financial performance, innovation input, supply chain concentration, government support and market competitiveness. Clustering method is applied to capture heterogeneity in firms’ multidimensional structural, gradient boosting models are used to predict ST (Special Treatment) status within three years, and SHAP analysis is used to identify the main risk. The results show that a small subset of firms with high leverage, tight liquidity, weak profitability, insufficient innovation, and highly concentrated key customers and suppliers accounts for most ST cases. Strengthening capital buffers, diversifying critical supply-chain relationships, and maintaining stable innovation investment are thus crucial for reducing delisting risk and enhancing the resilience of agricultural listed companies. Full article

Tomatoes are a rich source of nutrients that are essential for human health. However, in greenhouse environments, the complex growth patterns of tomatoes and stems often result in mutual obstruction and overlapping, posing significant challenges for accurate ripeness detection and stem segmentation. Furthermore, the current detection and segmentation tasks are typically executed in isolation, resulting in suboptimal inference efficiency and substantial computational expenses. To address these issues, this study proposes the YOLOP-Tomato (YOLO-Based Panoptic Perception for Tomato) based on YOLOv8n, enabling simultaneous tomato detection and stem and branch segmentation. Two RSU (ReSidual U-blocks) modules establish feature connection mechanisms between the backbone and head. SPPCTX (SPP Context) was developed at the neck of the model to perform multi-scale contextual feature fusion and enhancement. The SCDown (Spatial-Channel Decoupled downsampling) is employed to lightweight the backbone’s terminal structure. The experimental results demonstrate that YOLOP-Tomato achieves precision, recall, mAP₅₀, and mAP_50–95 of 94.9%, 85.0%, 93.6%, and 60.9% for detection, and mIoU of 77.6% for segmentation. These results represent improvements of 2.5%, 0.1%, 0.5%, 1.1%, and 1.4%, over YOLOv8n. The trained model was deployed on the NVIDIA Jetson AGX Orin platform, an efficient inference speed of 5.67 milliseconds was achieved. The proposed YOLOP-Tomato provides reliable and efficient technical support for tomato detection, ripeness identification, stem and branch segmentation in greenhouses, and holds great significance for improving the level of intelligent agricultural production. Full article

Alpine grasslands on the Qinghai–Tibet Plateau are highly sensitive to climate change and human disturbances, and their degradation poses serious threats to ecosystem stability and soil conservation. Belowground bud banks form the foundation of vegetative regeneration, yet their variation along degradation gradients and the soil factors regulating these changes remain insufficiently understood. In this study, we investigated the density and composition of belowground buds in grasses, sedges, and forbs across four degradation levels during the peak growing season and examined the soil controls shaping these responses. The results showed that moderate degradation significantly increased total bud density, indicating enhanced clonal renewal capacity, whereas severe degradation markedly reduced bud-bank potential. Bud types from different functional groups responded differently to soil conditions: rhizome buds of grasses were mainly driven by soil fertility, while tiller buds were more sensitive to soil compaction and carbon–nitrogen availability; rhizome buds of sedges could still develop in compact, nutrient-poor soils; and bud types of forbs were more responsive to variations in soil nutrient status or soil structure. Structural equation modeling further revealed that the formation of the belowground bud is primarily influenced by soil physico-chemical properties, particularly soil nutrients, which regulate regenerative capacity under degraded alpine grasslands. This study reveals the variation patterns of belowground bud banks along degradation gradients in alpine grasslands on the Qinghai–Tibet Plateau and their responses to soil factors, and it elucidates the pathways through which degradation mediates belowground bud bank dynamics via soil physico-chemical properties, particularly soil nutrients, thereby providing a scientific basis for understanding the regeneration potential of alpine grasslands and for the sustainable management and ecological restoration of degraded alpine grasslands. Full article

Oat is a forage with high protein value (10–14% DM) and good palatability, and is considered one of the main feed sources for ruminants. In this experiment, Lacticaseibacillus rhamnosus and Lentilactobacillus buchneri were selected as silage additives to investigate the fermentation quality, nutrient composition, microbial community and relationship between fermentation products and bacterial community of small-bale oat silage after ensiling. The experiment was set up with three treatment groups and three replications in each group, which were the control (C) group, L. rhamnosus (LR) group and L. buchneri (LB) group, and oat silages were subjected to 10-day and 30-day storage periods. The results show that both LR and LB additions significantly increased water-soluble carbohydrate, crude protein, lactic acid, propionic acid and acetic acid contents, and decreased pH, butyric acid, acid detergent fiber, neutral detergent fiber, and ammonia nitrogen contents and yeast and enterobacteria numbers in small-bale oat silage, compared with the C group. The highest content of acetic acid and the lowest numbers of enterobacteria and yeast were found in the LB group after 30 days of fermentation. Lentilactobacillus and Lacticaseibacillus were the dominant genera in the LB and LR groups, regardless of fermentation time. Lentilactobacillus and Lacticaseibacillus were positively correlated with a correlation value of 0.9, but both were negatively correlated with Bacillus. Lentilactobacillus and Lacticaseibacillus were positively correlated with acetic and lactic acids, while pH and butyric acid were positively correlated with Bacillus. This experiment revealed that the addition of homofermentative and heterofermentative lactic acid bacteria enhanced the relative abundance of Lentilactobacillus and Lacticaseibacillus, reduced harmful microbes, and improved fermentation quality of small-bale oat silage. Full article