Search Results (7,489)

The integration of robotics into agriculture addresses challenges such as labour shortages and sustainable production. Handling delicate and irregular natural products remains difficult for conventional grippers. This paper presents a baromorphic end-effector as a proof-of-concept platform for validating a perception-sensing TCP realignment pipeline rather than as a field-ready agricultural product. The square-to-hexagonal layout choice was made iteratively through prototyping, whereas a finite-element analysis was used in prior work to optimise and characterise the hexagonal cushion unit . The materials, actuation, and robotic platforms serve as the experimental testbeds for the integrated control concept. The system uses a modular array of pneumatically actuated hexagonal cells with sparse embedded pressure sensing, vision-based object detection, a discrete heuristic visual servoing controller to bypass kinematic singularities during the approach, and a closed-loop TCP dynamic realignment algorithm. Under controlled laboratory conditions, the prototype achieved 12/12 successful linear grasps and 9/11 successful angular grasps (82%) on irregular produce, whereas a conventional rigid parallel-jaw gripper failed all attempted baseline trials under the same protocol. Full article

Modern agriculture’s integration of Internet of Things (IoT), Industrial Control Systems (ICSs), and data analytics boosts productivity but introduces significant cybersecurity and data governance challenges. Existing scholarship is divided between policy-focused governance and technical attack analyses, hindering the development of comprehensive, enforceable defenses. This paper introduces an integrated framework that bridges normative data governance in smart farming (SF) with empirical, honeynet-derived threat intelligence. Drawing on the authors’ previous systematic review of SF data governance and a honeynet simulating agricultural IoT/ICS, the study maps governance challenges to quantitative attack indicators from honeynet logs, classifying each pairing as directly supported by telemetry, indirectly supported by telemetry, or not observable using the current methodology. The findings show that the services flagged as governance concerns face sustained attack pressure: the honeynet recorded brute-force attempts against SSH/Telnet on simulated irrigation controllers (149,000 events), connection and login attempts targeting SMB (Server Message Block) and MQTT (Message Queuing Telemetry Transport) on automated machinery (67,156), credential-guessing attempts against management services (14,937), and ICS protocol probes (11,532). Geographic and protocol distributions reveal that legacy industrial protocols and weakly authenticated management interfaces, both highlighted as governance concerns, constitute the primary attack surface. This evidence supports a tiered governance model integrating protocol-level controls, identity governance, and data-sharing policy, demonstrating that effective SF cybersecurity requires empirically calibrated rather than purely policy-driven frameworks. The proposed framework offers actionable guidance for aligning technical defenses with data governance obligations. This work contributes a new methodological protocol (cross-evidentiary mapping), an empirically calibrated tiered framework, and a coherent research agenda at the intersection of governance and measurement, serving as a template for similar analyses in other critical infrastructure sectors. Full article

Cereal–legume forage mixtures provide sustainable agronomic and environmental benefits for arid Mediterranean agricultural systems, but their productivity is often limited by within-field soil spatial variability. Site specific management practices are required to address the yield gaps. This study aimed to identify management zones in a no-till Triticale–Avena–Pea mixture by integrating apparent electrical conductivity (ECa) and NDVI measurements. ECa was mapped using an EM38-MK2 sensor, while NDVI was derived from UAV and Sentinel-2 imagery at four key growth stages. Plant biomass data and soil samples were collected for evaluating and explaining the yield gaps. Using K-means clustering, three management zones were delineated using ECa and NDVI data. Results showed a yield gap of 46% in fresh biomass between high- and low-productivity zones, associated with strong gradients in soil organic matter and total nitrogen availability. The delineation of management zones enabled targeted agronomic interventions, such as adjusting seeding rates in the high-productivity area and prioritizing soil improvement strategies in the low-productivity zone, thereby enhancing input-use efficiency. Full article

Timely computation of vegetation indices from remote sensing imagery would assist in sustainable agriculture monitoring through quick analysis of vegetation state and surface water. However, high-resolution multispectral imagery can be computationally expensive to process, especially on embedded systems. In this research work, we evaluate heterogeneous approaches that aim to enhance the computation of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) through sequential C++, OpenMP, CUDA and OpenCL on desktop and embedded CPU–GPU platforms. We compare multicore and GPU-based computations while also exploring optimizations of the OpenCL kernels with respect to memory management, loop unrolling and work-group settings. OpenMP increased the image-processing throughput from 211.24 to 500.85 images/s on the desktop platform and from 55.30 to 145.11 images/s on the Odroid XU4. These results correspond to speedups of 2.37× and 2.62×, respectively. These results demonstrate that heterogeneous processing can accelerate vegetation index calculation and provide a computational basis for timely, locally available agricultural monitoring. This capability may support precision agriculture applications, including vegetation stress assessment and water management decisions. Nevertheless, the experiments used offline multispectral images; energy consumption, energy per image, water savings, and onboard UAV performance were not measured. Full article

Soybean (Glycine max) is a globally important source of protein and oil, and its capacity for biological nitrogen fixation (BNF) underpins its strategic role in sustainable agriculture. However, BNF efficiency is highly sensitive to abiotic stresses, and conventional agronomic interventions struggle to simultaneously optimize nodulation, nitrogenase activity, and stress resilience. Agricultural nanotechnology offers a unique avenue to address this bottleneck by modulating the tripartite nanomaterial–rhizobium–soybean interaction at multiple scales. This review systematically examines (1) nanomaterial design strategies tailored to the rhizosphere and nodule microenvironments; (2) the ‘Rhizosphere–Nodule–System’ (RNS) cascade model, which integrates rhizosphere interfacial events, nodule metabolic reprogramming, and systemic stress signaling; and (3) field-level efficacy, genotype-dependent responses, and barriers to scalable application. By elucidating the mechanistic logic by which nanotechnology coordinates BNF enhancement with abiotic stress tolerance, this framework provides theoretical support for the targeted deployment of nano-agricultural technologies in soybean production systems. Full article

Páramo ecosystems are among the most biodiverse and hydrologically critical landscapes on Earth, yet their long-term sustainability is increasingly threatened by climate-driven wildfires. Vegetation Cover Fires (VCFs) in these high-altitude environments degrade carbon stocks, disrupt freshwater regulation, and undermine biodiversity conservation goals central to the UN Sustainable Development Goals (SDGs 13, 15, and 6). Between 2001 and 2023, 128 fire events consumed approximately 815 ha in the Sumapaz locality (the world’s largest continuous páramo), representing 64.9% of all fires recorded across Bogotá’s 20 localities. Despite this disproportionate ecological and social impact, no spatially explicit, operational risk management framework has been available for the region, representing a critical sustainability governance gap. This study addresses that gap by proposing an integrated climate-adaptive risk assessment and management strategy based on (i) the Canadian Forest Fire Danger Rating System Fire Weather Index (FWI), derived from ERA5 reanalysis climate data; (ii) spatial and temporal hotspot analysis of MODIS FIRMS active fire detections; and (iii) IDEAM’s multi-component vulnerability and threat scoring protocol. Spatial data were processed using ArcGIS, and FWI sub-indices were computed for each month of the 2001–2023 period. The FWI averaged 0.78 (low danger) across the study period yet peaked at 13.7 in February 2010 (moderate-to-high danger), consistent with the year of highest recorded fire activity (19 events). High- and very high-risk areas (3.70% combined) coincide with slopes >25%, the presence of the invasive and pyrogenic Ulex europaeus, and proximity to populated and agricultural lands. This study concludes with a three-pillar risk management framework—risk knowledge, risk reduction, and disaster management—providing spatially targeted, operationally viable strategies for local and institutional actors that directly support the sustainable conservation of páramo ecosystem services (water supply, carbon sequestration, biodiversity). The framework is designed to be updatable on a monthly basis using freely available ERA5 data, enabling continuous adaptive governance of wildfire risk as a contribution to long-term territorial sustainability. Limitations regarding MODIS detection uncertainty, ERA5 spatial resolution in complex terrain, and the need for probabilistic modeling are explicitly acknowledged. Full article

Agricultural drought poses a serious threat to grain production and regional agricultural sustainability. Accurate assessment of agricultural drought disaster risk (ADDR) and identification of its spatiotemporal differentiation are essential prerequisites for implementing precise risk management. Based on natural disaster risk theory, this study developed an exponent-weighted ADDR assessment model from four dimensions: hazard, vulnerability, exposure, and risk caused by insufficient mitigation capability (RIMC). The model was used to assess and zone ADDR in Henan Province from 2011 to 2022, analyze its spatiotemporal characteristics and interannual variability, and examine the interannual consistency of the assessment results using provincial-level statistical records of agricultural drought losses. The results showed that: (1) The ADDR assessment results were generally consistent with the statistical records of agricultural drought losses in terms of interannual variation, and further correlation analyses showed a significant positive association between the two. (2) ADDR and its constituent components exhibited pronounced spatial differentiation across Henan Province. ADDR showed an overall decreasing pattern from the southwest to the northeast, with Nanyang, Luoyang, Xinyang, and Jiaozuo identified as high-risk areas. Hazard showed a similar decreasing trend from the southwest to the northeast; vulnerability generally exhibited a west-high and east-low pattern; exposure showed the opposite pattern, with higher levels in the east and lower levels in the west; and high-RIMC areas were concentrated in western and southwestern Henan. (3) Temporally, both ADDR and hazard exhibited pronounced interannual fluctuations, vulnerability also showed certain interannual variation, exposure varied only slightly among years, and RIMC showed an overall year-by-year declining trend. ADDR showed high interannual variability in Xinyang and Hebi. The findings provide a scientific basis for zoning-based management of agricultural drought risk, precise allocation of drought-mitigation resources, and enhancement of drought resilience in agricultural systems in Henan Province. Full article

The rapid advancement of autonomous agricultural systems (AASs) is transforming modern agriculture, where labor shortages, sustainability imperatives, and demands for precision farming are driving the adoption of intelligent agricultural platforms. Agricultural production environments present uniquely challenging conditions for autonomous agricultural systems, including unstructured and dynamically changing terrain, biologically variable targets, unpredictable illumination and weather conditions, and safe human–machine coexistence. This review systematically investigates three cornerstone technologies: multi-source perception, intelligent decision-making, and precision control. Furthermore, typical agricultural operations, including soil tillage, planting, irrigation and drainage, fertilization, plant protection, harvesting, and agricultural product processing, are reviewed to illustrate their applications. Based on representative operational scenarios, the research progress and application characteristics of intelligent equipment in environmental perception, operational optimization, and control execution are summarized. Specifically, multi-source perception is evolving from isolated sensor-based acquisition toward multimodal and deep learning-enabled semantic scene understanding. Intelligent decision-making has evolved from experience-driven approaches toward physics-informed, data-driven, and knowledge-enhanced frameworks for adaptive operational optimization. Precision control has progressed from conventional PID control toward adaptive, learning-based, and digital twin-enabled control strategies, achieving robust high-precision closed-loop regulation. However, several critical challenges persist: limited cross-domain generalization and robustness of perception models under environmental distribution shift, constrained interpretability and trustworthiness of data-driven decision systems, and insufficient adaptability of control architectures under multi-disturbance coupled field conditions. To address these gaps, future research should prioritize multi-source heterogeneous data fusion and standardization, collaborative control frameworks integrating mechanistic knowledge with data-driven learning, and explainable artificial intelligence combined with agricultural domain expertise—advancing toward genuinely autonomous, trustworthy, and resilient agricultural systems. Full article

Agricultural ammonium pollution from farmland drainage and low-value crop straw utilization are two critical rural environmental problems that cannot be solved by single remediation approaches. Herein, a novel composite was prepared by immobilizing effective microorganisms (EM) on corn straw biochar to construct a synergistic adsorption–biodegradation system, and its nitrogen removal mechanism was systematically investigated at structural and molecular levels. Metagenomic analysis detected a complete set of heterotrophic nitrification–aerobic denitrification (HN-AD) functional genes (amoA, hao, napA, nirK, norB, nosZ) in the isolated strain Bacillus thuringiensis A1, revealing the genetic potential of this strain for ammonium biodegradation. EM modification optimized biochar pore structure and increased the equilibrium adsorption capacity to 1.215 mg/g, which was 66.4% higher than that of pristine biochar (0.73 mg/g). Sterilization control tests indicated that physicochemical adsorption occupied the dominant position in ammonium removal, while microbial biodegradation acted as an auxiliary removal pathway. Importantly, the synergistic relationship between the two pathways should be interpreted cautiously, since autoclaving may subtly alter biochar physicochemical properties, and direct paired characterization of viable composites before and after sterilization is technically unavailable. Kinetic and thermodynamic results further validated the improved adsorption performance after modification. Overall, EM immobilization promoted ammonium adsorption via pore optimization, while pore-confined microbes achieved sustainable HN-AD biotransformation, jointly realizing synergistic nitrogen removal. This study provides a mechanistic reference for the optimized design and application of biochar–microbe composites in agricultural nitrogen pollution control. Full article

Arid-zone agricultural heritage systems (AHSs) face severe challenges from ecological fragility, water scarcity, and socioeconomic pressures; scientifically understanding their system resilience is a critical prerequisite for achieving sustainable development. This study constructs a three-dimensional resilience evaluation indicator system following the Pressure–State–Response (PSR) framework tailored to arid oasis conditions. Drawing on the entropy weight method, GIS spatial analysis, the ARIMA model, and the obstacle degree model, it adopts statistical and remote sensing data of Xinjiang from 2015 to 2024 to systematically analyze the resilience levels, spatiotemporal evolution characteristics, future development trends, and core obstacle factors of six AHSs. The results reveal that the overall resilience of AHSs in Xinjiang exhibited a fluctuating upward trend over the decade, showing an obvious spatial differentiation pattern of “high in Northern Xinjiang, low in Eastern and Southern Xinjiang.” ARIMA forecasting indicates that resilience will maintain a positive growth trajectory from 2025 to 2028, yet inter-site hierarchical gaps persist. The primary constraints hindering resilience improvement include industrial structure upgrading index, per capita regional GDP, annual NDVI, vegetation coverage, and the number of intangible cultural heritage items, with a clear differentiation between long-term structural bottlenecks and temporary short-term constraints. The study concludes that, although the resilience of AHSs in Xinjiang possesses long-term improvement potential, persistent challenges such as fragile ecological foundations, low-end industrial structures, and insufficient cultural inheritance remain prominent. Targeted differentiated strategies—short-term emergency regulation, medium-term industrial–cultural integration, and long-term adaptive governance—are therefore required to enhance systemic resilience in the future. These findings enrich empirical research on AHS resilience in arid regions and provide case references for the scientific conservation, revitalized utilization, and sustainable development of AHSs in Xinjiang and other global dryland areas. Full article

Unsustainable agricultural practices and overreliance on chemical fertilizers have led to severe environmental issues, such as soil and water pollution, loss of biodiversity, and risks to human and animal health. Moreover, plant diseases continuously decrease crop productivity and threaten global food security. Therefore, there is a strong need to focus on sustainable agricultural practices. Microbial biofertilizers emerge as environment-friendly alternatives to chemical fertilizers that help in nutrient solubilization and availability, soil fertility, and plant growth promotion, in addition to curbing the application of chemical fertilizers. Microbial inoculants enhance agricultural yield by performing complementary roles, such as facilitating nutrient uptake through biological nitrogen fixation and phosphate solubilization, promoting plant growth via phytohormone synthesis, and mitigating diseases by activating plant defense responses. A 2025 meta-analysis of 107 field studies in China reported mean yield increases of 22.3% in wheat, 13.6% in rice, 12.8% in maize, and 65.4% in millet, while a field study in saline soil reported a 25% reduction in NPK fertilizer use in barley without reducing the grain yield. This review provides an overview of the major types of microbial biofertilizers, their modes of action, and their use in important cropping systems. Special emphasis is placed on microbial consortia that can enhance nutrient cycling, plant productivity, and tolerance to abiotic stress factors. The application of nanotechnology, genetically engineered microorganisms, and combinations of microbial inoculants with organic waste are some strategies that could be adopted for next-generation biofertilizer development. The review also addresses the major hurdles in the formulation, field performance, and commercialization of microbial biofertilizers. Future perspectives revolve around optimizing microbial formulations, applying advanced biotechnological tools, and developing enabling policies for the rapid adoption of microbial biofertilizers for sustainable agriculture. Full article

Agricultural new quality productive forces (ANQP) provide an important foundation for high-quality agricultural development and sustainable rural transformation. Using panel data for 30 Chinese provinces from 2011 to 2022, this study examines the effect of digital financial inclusion (DFI) on ANQP and the channels through which that effect operates. The results show that DFI is significantly and positively associated with ANQP. A one-standard-deviation increase in DFI is associated with an increase in ANQP equivalent to approximately 64.35% of its sample mean. Green technological innovation serves as a statistically significant transmission channel, although its indirect effect accounts for only 3.31% of the total effect. The relationship between DFI and ANQP exhibits a double-threshold pattern with respect to the level of DFI and a single-threshold pattern with respect to regional economic development, with the estimated coefficients increasing gradually across regimes. Decomposing DFI shows that coverage breadth, usage depth, and digitization all contribute positively to ANQP. Further analysis of the outcome dimensions indicates that DFI is positively associated with agricultural laborers, agricultural labor objects, and agricultural labor resources. The estimated association is also larger in regions with more developed traditional financial systems. These results provide a multidimensional and stage-based understanding of the relationship between digital finance and agricultural productive-capability upgrading. Full article

Soil health is central to sustainable agriculture, but remains challenging to assess in diversified agroecosystems such as permaculture. Soil condition reflects the interaction of physical, chemical, and biological properties, but practical assessment commonly relies on a limited set of informative indicators. In this study, a PCA-weighted Soil Health Index (SHI) was constructed from five surface soil indicators: organic carbon, total nitrogen, microbial biomass (PLFA), bulk density, and gravimetric water content. The first principal component explained 75.30% of the total variance. The analysis used 84 observations collected between 2019 and 2021 from permaculture and conventional farming systems across nine locations in Germany and Luxembourg, encompassing different land use types and two soil depths. Permaculture plots showed higher SHI values overall than conventional plots, with the same trend observed across all nine locations, although land use imbalance limited fully matched comparisons. To avoid circular prediction of the PCA-derived target, the five surface variables used directly to construct the SHI were excluded from the predictive feature set. Machine learning models were evaluated using grouped validation in which entire locations were held out from model training. The best-performing full-profile Ridge model achieved an out-of-fold $R^2$ of 0.710, an MAE of 0.159, and an RMSE of 0.214. Out-of-fold SHAP analysis indicated that magnesium, zinc, soil pH, subsoil bulk density, and copper made the largest model-specific contributions to SHI estimation. These findings demonstrate that PCA-based soil health assessment can distinguish systematic differences between studied farming systems and that a leakage-aware, interpretable modeling framework can provide moderate predictive performance across held-out locations. The results should be interpreted as internal evidence from a small multi-location dataset rather than as externally validated or causal estimates of management effects. Full article

The study evaluated the potential of low-dose foliar melatonin (MT) treatments to alleviate chilling injury in cucumber plants, with a particular focus on mimicking sudden spring frosts in open-field cultivation. Cucumber seedlings were sprayed with MT solutions at 0.1, 1, and 10 mg L⁻¹ (0.4, 4, 40 µM respectively), then subjected to a controlled chilling regime (4 °C for 72 h). A set of biochemical and physiological parameters was assessed, including stem elongation, leaf area, tissue water content, photosynthetic pigment concentrations, malondialdehyde (MDA) and hydrogen peroxide (H₂O₂) accumulation, antioxidant enzyme activities, and soluble carbohydrate profiles. In addition, the thermostable fraction of chlorophylls a and b was determined as an indicator of photosynthetic system integrity; this parameter substantially increased under MT treatment, particularly at 1 and 10 mg L⁻¹, despite an overall reduction in total pigment content. The results demonstrated that MT pretreatment, especially at 1 mg L⁻¹, significantly mitigated chilling-induced growth inhibition, preserved chloroplast integrity, and reduced membrane lipid peroxidation. Moreover, MT promoted the accumulation of osmo- and cryoprotective sugars, contributing to improved cellular homeostasis under low temperature. These key findings indicate that low-dose MT priming is a promising, nature-derived strategy to enhance the tolerance of cucumber to acute chilling events, supporting the development of sustainable and organic-friendly approaches for spring frost protection. Full article

Tenure insecurity among smallholder farmers undermines incentives for long-term soil health restoration investments, thereby limiting agricultural productivity and household food security. This study examines the impacts of land registration on soil health restoration and household food security among smallholder farmers in Malawi. Using representative data from 506 households, the study employs a corrected selectivity endogenous switching regression model to account for selection bias, reverse causality and unobserved heterogeneity. Food security is measured using multiple indicators to capture its multidimensional nature, including the Women’s Dietary Diversity Score (WDDS), Children’s Dietary Diversity Score (CDDS), the Household Food Insecurity Access Scale (HFIAS), and the Household Food Insecurity Experience Scale (HFIES). The results indicate that land registration significantly improves soil health restoration by 9% (p < 0.00) and food security outcomes across indicators, with average treatment effects on the treated (ATT) of 1.02 for WDDS, 0.50 for CDDS, −0.83 for HFIES, and −2.54 for HFIAS, all significant at the 1% level. Furthermore, household and farm characteristics, such as the age of the household head, membership in farmer groups, landholding size, and adoption of sustainable land management practices enhance welfare. The study suggests that scaling up targeted land registration, alongside promoting complementary sustainable land management practices, has the potential to improve soil health restoration and strengthen food security in Malawi’s smallholder farming systems. Full article