Search Results (4,171)

Solar insecticidal lamps are important physical control devices for green pest management, but faults in their photovoltaic power supply units can reduce trapping efficiency and shorten service life. To improve fault identification under complex agricultural environments, this study proposes a signal-image-level multimodal fusion network (SIL-MMFN) for detecting and classifying photovoltaic panel operating states in solar insecticidal lamps. The method combines time-series measurements with short-time Fourier transform (STFT)-based time–frequency images. A convolutional image branch extracts spatial features from time–frequency representations, whereas a bidirectional GRU branch with attention models temporal dependencies in the original signals. In addition, physics-informed features based on the illumination–current residual and output power are introduced to enhance discriminative fault information. Field data collected from four agricultural deployment nodes were used to classify normal, open-circuit, and mismatch states. Experimental results show that the proposed method achieved an accuracy of 97.5%, precision of 96.7%, recall of 97.8%, and macro-F1 score of 97.3%, outperforming single-modality and representative comparison models. The results indicate that multimodal fusion helps reduce confusion between open-circuit and mismatch faults and provides a potential approach for operating-state monitoring and maintenance of agricultural photovoltaic equipment. In this study, fault diagnosis refers to the detection and classification of photovoltaic panel operating states, including normal, open-circuit, and mismatch conditions. Full article

Linpan in Chengdu Plain, a distinctive form of dispersed rural settlement on the Chengdu Plain, is composed primarily of traditional rural dwellings embedded within woodlands environments. These settlements play multifunctional roles related to agricultural production, daily life, ecological sustainability, and the preservation of folk culture, thereby holding significant ecological and cultural value. In recent decades, rapid urbanization has profoundly impacted the spatial patterns, ecological environments, and livelihood systems of Linpan in western Sichuan, posing severe challenges to their preservation and development. To investigate the extent and nature of these changes, this study examines the spatiotemporal evolution of Linpan in Chengdu over five time periods from 1980 to 2020, employing both macro- and micro-scale analyses. Settlement types were classified based on their transformation trajectories, and representative cases were selected to identify and interpret the key driving forces behind these changes. The results indicate that: (1) at the macro level, Linpans have undergone a clear transition from small-scale, widely distributed, and irregularly shaped patterns to more centralized, aggregated, and standardized spatial configurations, particularly in the peri-urban areas of Chengdu; (2) at the micro level, the internal composition of Linpan has changed substantially, with a marked decline in woodlands coverage. The original integration of buildings and trees has shifted towards a spatial arrangement characterized by peripheral and fragmented vegetation; (3) Changes in production methods have prompted the spatial restructuring of Linpan settlements, transitioning from uniformly dispersed arrangements to clustered formations along road-adjacent resource points. Concurrent population and housing migration has reduced the total number of Linpan, while individual settlements have increased in size and density. Additionally, planning and construction policies have guided the morphological transformation of Linpan from organically evolved forms to geometrically regular configurations. Full article

Examining how artificial intelligence industry agglomeration (AIIA) affects carbon and pollution reduction is crucial for China’s agricultural sustainability. Existing research mainly examines the effect of artificial intelligence (AI) on the reduction of single pollutants while overlooking how industry agglomeration influences the marginal cost of coordinated abatement, a key issue for the agricultural resource–environment–economy system. Using panel data for 30 Chinese provinces from 2016 to 2024, this study constructs a marginal cost-based indicator of agricultural pollution–carbon reduction synergy (APCRS) and examines the effect of AIIA. The full-sample results reveal that AIIA has a U-shaped relationship with APCRS. Technological progress partially mediates this relationship. Agricultural socialized services and rural industrial integration buffer the initial negative association, whereas agricultural labor productivity strengthens the curvature of the estimated nonlinear pattern. The effect of AIIA also varies with external conditions and is more pronounced in regions with higher levels of marketization and industrialization while remaining significantly U-shaped across grain strategic zones. This dynamic process is more likely to emerge when public innovation investment and rural household income exceed critical thresholds. These findings provide new evidence for understanding how AI-driven agglomeration can support green agricultural transformation. Full article

This study focuses on 91 human individuals from the Western Zhou period excavated from the Jucun cemetery in Jiang County, southern Shanxi Province, and examines their dietary structure and its changes within the context of population movements in early China. Stable carbon and nitrogen isotope analysis was employed, combined with archaeological phase divisions, to compare dietary patterns across different periods. The results show that the Jucun population exhibits a diet dominated by C₄ resources, with a mean δ¹³C value of −8.0 ± 0.7‰ and a mean δ¹⁵N value of 8.6 ± 0.9‰, indicating a relatively low level of animal protein intake. Diachronic analysis indicates that δ¹³C values remain generally stable throughout the Western Zhou period, whereas δ¹⁵N values show a decreasing trend. Regional comparison further shows that populations of different origins all fall within the isotopic range characterized by millet-based agriculture in southern Shanxi. Overall, the dietary structure of this population exhibits a convergence toward an agriculture-based pattern centered on millet. This study provides bioarchaeological evidence for subsistence transformation and cultural integration among mobile populations in the Central Plains during the Western Zhou period. Full article

In the context of global climate change and intensifying resource and environmental constraints, improving agricultural eco-efficiency (AEE) has become critical to achieving the green transformation of agriculture. This study develops a comprehensive evaluation index system for AEE that incorporates factor inputs, expected outputs, and undesirable outputs. Using county-level panel data from 2010 to 2022 for the Yangtze River Economic Belt (YEB), it applied the super-efficiency slacks-based measure (SBM) model to quantify AEE. Furthermore, spatial autocorrelation analysis and the spatial Durbin model (SDM) are employed to reveal its spatiotemporal characteristics and influencing factors of AEE. The results indicate that the overall AEE of the YEB exhibited a fluctuating upward trend over the study period, yet significant regional heterogeneity persisted. AEE showed pronounced positive spatial correlations, with regional disparities primarily stemming from hyper-variance intensity, suggesting that high- and low-efficiency counties are spatially interwoven. The SDM results indicate that local temperature, economic development, urbanization, fiscal support for agriculture, and agricultural production structure positively influence local AEE, while rural residents’ income and educational attainment exert negative effects. These factors also demonstrate significant spatial spillover effects, with economic development and ecological conditions in adjacent regions generating positive externalities, while neighboring urbanization and temperature producing negative impacts. This study deepens the understanding of the driving mechanisms underlying AEE from a spatial interdependence perspective, providing a scientific basis for formulating cross-regional collaborative policies aimed at promoting green agricultural development in major river basins. Full article

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production. Full article

Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology for resolving cellular heterogeneity and deciphering gene regulatory networks in complex tissues. Despite challenges such as incomplete genome annotation, technical variability across platforms, and limitations in robust cell-type annotation, scRNA-seq has substantially advanced our understanding of the developmental processes, physiological regulation, and disease responses in pigs, an economically and biomedically important species, thereby providing insights into traits of agricultural and translational relevance. By profiling transcriptomes at the single-cell resolution, scRNA-seq enables the identification of rare cell populations, dynamic cellular states, and lineage trajectories that are critical for reproduction, growth, immunity, and metabolic homeostasis. Recent porcine scRNA-seq studies have generated high-resolution cellular atlases spanning embryos, reproductive organs, immune tissues, skeletal muscle, and the gastrointestinal tract, revealing cell-type-specific regulatory mechanisms associated with reproductive performance, muscle accretion, adipogenesis, immune competence, and intestinal functionality. This review summarizes the fundamental principles and analytical strategies of scRNA-seq, highlights its major applications in porcine biology and production, and discusses current challenges as well as future perspectives for integrating single-cell technologies into livestock science. Full article

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

This study investigates the spatiotemporal dynamics of peri-urban expansion and land use transformation in Izmir, Türkiye, over 36 years (1986–2022) using an integrated GIS-based Multi-Criteria Decision Analysis (MCDA) framework. Multi-source datasets, including Landsat imagery, CORINE land cover (CLC) data, demographic statistics, and spatial variables (slope, transportation proximity, and distance to the city center), were combined to delineate urban, peri-urban, and rural zones. Results reveal a substantial percentage increase in urban areas from 2.8% in 1986 to 10.48% in 2022, corresponding to an expansion of approximately 7.6% (≈908.56 km²). In contrast, agricultural land declined by 5.8%, while forest areas experienced a more severe reduction of 19.1%, indicating significant environmental degradation. Population dynamics further support this transformation, with peri-urban districts exhibiting growth rates exceeding the metropolitan core average of 1.8% (1986–2010), followed by a relative slowdown to 0.5% after 2010, accompanied by outward migration-driven expansion. Spatial analysis demonstrates that peri-urban growth is strongly influenced by accessibility and topography, with development concentrated within 30–50 km of the city center and along major transportation corridors (500–1000 m buffers). Land Surface Temperature (LST) analysis indicates increasing urban heat island intensity, with surface temperatures ranging from 12 °C to 46 °C, particularly in densely built inner peri-urban zones. The MCDA-based classification identifies distinct inner and outer peri-urban belts, characterized by contrasting density, land use patterns, and environmental pressures. Overall, the findings highlight that Izmir’s peri-urbanization is a heterogeneous and rapidly evolving process driven by demographic, spatial, and policy-related factors. The study provides a replicable methodological framework and emphasizes the urgent need for integrated, sustainability-oriented planning strategies to mitigate ecological loss and uncontrolled urban sprawl. Full article

Under global warming, understanding regional drought evolution is essential for drought early warning, food security, climate adaptation, and sustainable water-resource management. This study analyzed meteorological drought in Jiangxi Province during 1961–2022 using SPI-12, SPEI-12, and EDDI-12 from the CHM_Drought high-resolution multi-index dataset. The Mann–Kendall (MK) test, Theil–Sen slope estimator, three-threshold run theory, Morlet wavelet analysis, wavelet coherence (WTC), and cross-wavelet transform (XWT) were used to examine drought trends, event characteristics, periodicity, and inter-index relationships. Results showed a widespread drying tendency. EDDI-12 exhibited a highly significant increase in 99.86% of valid resampled raster pixels, indicating enhanced atmospheric evaporative demand, while SPEI-12 and SPI-12 showed significant decreasing trends in 97.96% and 93.24% of valid pixels, respectively. Stronger drying signals were mainly distributed in central and northern Jiangxi. Run-theory analysis indicated longer-duration cumulative droughts in southern mountainous areas and frequent short-duration drought events in the Poyang Lake Plain and central-northern Jiangxi. Wavelet analysis identified a dominant interdecadal periodicity of approximately 20–21 years. WTC and XWT revealed strong in-phase coherence between SPI and SPEI, whereas SPI/SPEI and EDDI mainly showed anti-phase statistical phase relationships. From a sustainability perspective, these findings provide scientific support for multi-index drought monitoring, adaptive agricultural water allocation, drought early warning, and climate-resilient water-resource management in humid monsoon regions. Full article

Against the backdrop of the deep advancement of the carbon peak and carbon neutrality goals and the superposition of the transportation power strategy, leveraging the spatial restructuring of highway networks to optimize the low-carbon layout of county-level industries has become a crucial lever for balancing economic quality improvement with carbon intensity control. This study selects panel data from 129 counties in Yunnan Province spanning 2015–2024, constructing a comprehensive highway network development index from four dimensions: highway density, road network connectivity, weighted hierarchical structure, and county accessibility. Using a two-way fixed effects benchmark model, a stepwise mediation effect testing framework, and a regional heterogeneity identification strategy, the paper systematically examines the marginal effects, transmission pathways, and spatially differentiated characteristics of highway network development on county-level industrial carbon emission intensity. Key findings are as follows: Enhanced highway network development significantly suppresses the increase in county-level industrial carbon emission intensity, and a well-developed road network can provide long-term empowerment for the low-carbon transformation of county-level industries. Mechanism analysis confirms that highway network development reduces emissions through two core pathways: first, a direct emission reduction effect achieved by optimizing the county-wide freight organization system, reducing inefficient transport energy consumption, and improving overall transport efficiency; second, an indirect low-carbon enabling effect realized by breaking down administrative barriers in county markets, lowering cross-regional business transaction costs, deepening industrial division of labor and collaboration, and forcing resource allocation improvements. Heterogeneity analysis reveals that the low-carbon dividends of highway network development exhibit significant gradient differentiation: the emission reduction enabling effect is strongest in counties within the Central Yunnan urban agglomeration, followed by cultural tourism counties in western Yunnan and border counties in southern Yunnan, with the weakest marginal enabling effect observed in traditional agricultural counties in northeastern Yunnan. Full article

Against the dual objectives of food security and sustainable agriculture, this study examines how agricultural machinery services—China’s primary organized mode of agricultural production—affect agricultural carbon emissions. Using panel data covering 30 provinces in China from 2010 to 2022, this study applies two-way fixed effects, mediation, and moderation models to investigate the effects of these services on carbon emissions as well as the mechanisms involved. The results show: (1) Both carbon emissions and the level of machinery services in China differ by region and over time. Carbon emissions are stabilizing, while machinery services are steadily improving. Both variables cluster in certain areas. (2) Machinery services exhibit a significant inverted U-shaped impact on carbon emissions. As the level of machinery services grows, emissions first rise, then fall. (3) The emission reduction impact of machinery services varies widely. It differs across topographic relief, farmland types, and grain crop types, but the inverted U-shaped relationship remains in most cases. (4) The efficiency of the division of labor and agricultural chemical input intensity partly explain the effect. They help reduce emissions by enabling labor substitution and lower input levels. (5) Large-scale agricultural operations strongly influence how machinery services affect carbon emissions. To accelerate the low-carbon sustainable transformation of Chinese agriculture, efforts should prioritize establishing a differentiated, regionally tailored agricultural machinery socialized service system, improving service efficiency and green development capacity, and optimizing large-scale land management structures. Full article

Data-driven models can be used to understand basin-wide hydrological processes and generate predictions for future conditions, particularly in cases of scarce data availability related to basin characteristics. Although they have long been applied in hydrological modeling, there is still limited information regarding their ability to produce reliable long-term projections under climate change conditions. This study evaluates the long-term predictive performance of data-driven models by employing a hybrid deep learning architecture combining Wavelet Transform (WT) and Deep Neural Network (DNN). The dataset used in this study was obtained from the Yamula Reservoir Basin, a semi-arid agricultural basin in Türkiye. Monthly streamflow was simulated based on climate projection data from the HadGEM2-ES model under the RCP4.5 and RCP8.5 scenarios. Results showed that the WT–DNN framework was successful in learning the system dynamics and reproducing observed streamflow behavior. The model produced continuous projections for the future period; however, these projections should be interpreted with caution due to the increasing uncertainty associated with long-term climate forcing and the sensitivity of data-driven approaches to shifts in climatic and hydrological regimes. Full article

This paper shifts the research perspective from “Buddhist monasteries” to “monastic Buddhism,” using Nālandā Mahāvihāra as a micro-level case to illuminate the broader support mechanism of Indian Buddhist monasteries, with particular focus on the concept of “non-exhaustible endowment”. Drawing on epigraphic evidence, Vinaya texts, and Chinese pilgrims’ records, it finds that major donors supported monasteries through religious rituals, land grants, and cash investments, primarily in the form of landed property and gold and silver currency, which were designated as non-exhaustible endowments. Monasteries then engaged in agriculture, handicrafts, building industry, commerce, and lending, transforming static assets into a non-exhaustible cycle of capital that benefited both monastics and laity. Systems such as Yizhi (robe funds) and Gongfu zhi Zhuang (robe-providing estates) reveal mature financial services that not only liberated monks from economic constraints but also stimulated the cotton textile trade between India and China. The wealth possessed by monasteries was not static but perpetually engaged in a dynamic cycle of capital. Major Buddhist monasteries thus emerged as regional economic engines, which became the core value for continuous royal patronage, as well as the key incentive for their violent destruction by Turkic Muslims. However, the transformation of the religious landscape and economic network in late medieval Bihār was not a simplistic process. Faced with a changing political and religious environment over time, Sufi saints, Jain followers, Shaiva ascetics and other religious communities, each grounded in their own faiths, landholdings, commercial networks and educational systems, gradually displaced, restructured and undermined the Buddhist monastery-centered endowment mechanism, causing Buddhism to progressively lose its regional dominance as an institutionalized religion. Full article

The digital transformation of agrifood systems demands an integrated infrastructure to ensure traceability, trust, and intelligent decision-making across complex and heterogeneous value chains. METROFOOD-IT, a large-scale national research infrastructure in food metrology aligned with the ESFRI METROFOOD-RI, addresses these challenges by combining advanced experimental facilities with a comprehensive digital ecosystem. This paper focuses on the IT kernel of METROFOOD-IT and presents an integrated architectural model that brings together four key technological paradigms: data acquisition through Internet of Things (IoT) and laboratory infrastructures, an Open Data Platform for interoperability and sharing, blockchain-based notarization for integrity and provenance, and Artificial Intelligence (AI) for knowledge extraction and decision support. Rather than describing these components in isolation, the paper abstracts from their implementation within the Italian National Recovery and Resilience Plan (NRRP) project METROFOOD-IT to distill a coherent and reusable architectural pattern in which data management, trust enforcement, and intelligent analytics are tightly coupled. Five explicit design principles are identified and articulated: federated data with centralized metadata, selective on-chain anchoring, user-unobtrusive trust infrastructure, explainability as a first-class architectural concern, and machine learning as the backbone of decision-making. Two empirical case studies—one centered on explainable AI for hyperspectral crop nitrogen assessment and the other on IoT-driven sustainable agriculture monitoring secured by distributed ledger technology—serve a dual role: they motivate and shape the architectural pattern, and they exemplify the operational regimes the resulting design supports. A reference deployment on the Ethereum Sepolia public test network, grounded on an IBM Power E1050 and IBM Storage Scale enterprise substrate, provides quantitative evidence for the proposed hybrid on-chain/off-chain pattern with streaming hash-only notarization. The architecture illustrates how research infrastructures can evolve into integrated digital platforms that enable transparent, verifiable, and scalable agrifood systems, and offers a foundation for generalizable design principles in data-intensive and trust-sensitive settings. Full article