Search Results (7,172)

Earthworms possess a highly developed innate immune system based on the coordinated activity of coelomocytes and humoral factors present in the coelomic fluid. These immune components play a central role in host defence against pathogens, maintenance of physiological homeostasis, and adaptation to environmental stressors. Coelomocytes exhibit remarkable functional and morphological diversity, including participation in phagocytosis, encapsulation, extracellular trap formation, cytotoxic responses, wound healing, and regulation of oxidative and osmotic stress. In addition, coelomic fluid contains numerous biologically active molecules, such as lysenin, coelomic cytolytic factor 1, perforin, serine proteases, lysozyme, antimicrobial peptides, and pattern recognition receptors, which contribute to cellular and humoral immune responses. Recent studies have demonstrated that earthworm coelomocytes are highly sensitive to environmental pollutants, including heavy metals, pesticides, nanomaterials, and microplastics, highlighting their importance in ecotoxicological research and soil biomonitoring. Furthermore, antifungal, antimicrobial, anti-inflammatory, antipyretic, and cytotoxic activities associated with coelomocytes and coelomic fluid suggest promising applications in agriculture, biotechnology, and pharmaceutical research. This review summarises current knowledge regarding the classification, characteristics, immune functions, toxicological responses, and applied significance of earthworm coelomocytes and coelomic fluid, with particular emphasis on their role in environmental monitoring and potential biomedical applications. Full article

The Chishui River Basin, a core production area for Chinese sauce-aroma Baijiu (exemplified by Moutai), supports sorghum cultivation critical to the liquor’s distinctive quality. The soil environment quality within this region, therefore, directly impacts the safety and quality of both raw material and the final distilled spirit. To underpin the safe production and sustainable development of this iconic beverage, it is essential to assess soil heavy metal contamination in the soils and quantify the contributions from various sources. In this study, 172 surface soil samples were collected from typical sorghum planting bases in the Renhuai area. Concentrations of eight heavy metals (loids) (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) were determined. The contamination status was evaluated using the geostatistical inverse distance weighting interpolation, the Nemerow pollution index (P_N), and the potential ecological risk index (RI). Source identification and quantification were performed using the positive matrix factorization receptor model (PMF). Results revealed significant enrichment of Cd and Hg in the soil, with mean concentrations 2.07 times and 2.54 times the soil background values for Guizhou Province, respectively. Pollution index results (P_i, P_N) indicated that soil Cd contamination is relatively severe, whereas contamination from other elements is minimal. Overall, approximately 86.5% of the study area was classified as clean or only slightly polluted. Cd poses a moderate ecological risk and was the primary contributor to the total ecological hazard. Other elements exhibited lower risk, resulting in a slight overall potential ecological risk. The soil environmental quality in certified organic sorghum bases was generally favorable. PMF analysis identified three principal sources: historic industrial emissions and traffic-related sources (contributing 46%), weathering of carbonate rocks combined with agricultural activities (37%), and natural background coupled with organic fertilizer application (17%). In conclusion, while the overall soil heavy metal pollution level in the sorghum planting areas is low, the notable enrichment and higher ecological risk of Cd necessitate enhanced dynamic monitoring and targeted risk control measures to ensure long-term soil health and product safety. Full article

Agricultural intensification in Eastern Africa has raised concerns about the transport of pesticides and nutrients from farmland into surface waters, posing risks to ecosystems and human health. This study systematically reviews the peer-reviewed literature published between 2010 and 2024 to assess the extent, patterns, and drivers of agrochemical contamination in rivers, lakes, and reservoirs across the region. Reported pesticide concentrations ranged from <0.01 to 0.55 μg L⁻¹, with several studies indicating exceedances of drinking-water or environmental guideline values, particularly for organophosphate and carbamate compounds. Nutrient enrichment was widespread, with nitrate concentrations of 0.99–5.6 mg L⁻¹ and phosphate levels of 0.16–2.0 mg L⁻¹, frequently linked to eutrophication. Many studies showed strong seasonal variability, with higher concentrations during rainy periods due to increased runoff and erosion. Variability among findings reflected differences in land use, catchment characteristics, sampling design, and analytical approaches. Where evaluated, mitigation measures such as vegetated buffer strips, cover cropping, and improved nutrient management were associated with reductions in agrochemical runoff of approximately 50–80%. Overall, agrochemical contamination is widespread across Eastern African basins and influenced by agricultural practices and hydrological dynamics, highlighting the need for strengthened monitoring, improved stewardship, and broader adoption of mitigation strategies. Full article

Under the dual constraints of global food security and ecological protection, conventional agriculture is hampered by low resource efficiency and sluggish environmental response. Crop digital twin technology establishes a dynamic virtual reality system that integrates crops, environment, and water to enable real-time interaction and optimization. Based on the existing literature, this paper reviews the concept, architecture, and core modules of this technology and summarizes its applications in precision irrigation and crop monitoring. There are three major bottlenecks that persist, including limited high-frequency multi-source sensing and spatiotemporal fusion, insufficient parameter calibration and dynamic updating, and weak cross-scale integration from plant to watershed. Water is increasingly recognized as the key constraint and control variable and acting as both the central physiological driver of crop growth and the mass-flow link that connects the soil–plant–atmosphere continuum. The spatiotemporal dynamics of crop water deficit, compensatory root water uptake, evapotranspiration feedback, and the hydraulic behavior of irrigation-district canal systems constitute the core hydrological processes that must be simulated within the digital twin. Synchronizing crop water demand, soil moisture dynamics, atmospheric evapotranspiration, and irrigation scheduling within a unified spatiotemporal framework establishes a complete sensing, diagnosis, prediction and regulation technical chain. This chain offers a core pathway for alleviating agricultural water scarcity, improving irrigation efficiency, and ensuring food security. Full article

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

Unmanned Aerial Vehicle (UAV) image object detection holds significant application value in the low-altitude economy, traffic monitoring, intelligent agriculture, and disaster rescue. However, due to the top-down perspective, UAV images typically suffer from challenges such as small target scales, dense object distribution, severe occlusions, and complex backgrounds. These issues often limit the recall and localization accuracy of general-purpose detectors when they are directly applied to UAV small-object detection scenarios. To address these aforementioned challenges, this paper proposes an Adaptive Dynamic Aggregation YOLO network, termed ADA-YOLO. The novelty of ADA-YOLO lies in its highly efficient combinatorial design specifically tailored for UAV small object detection, while retaining the efficient backbone of YOLOv8, we systematically reconstruct the neck and detection head to improve accuracy. Specifically, a high-resolution P2 detection branch is incorporated to construct a P2–P5 multi-scale prediction structure. Furthermore, the lightweight DySample dynamic upsampling module is adopted to replace traditional upsampling methods, and an Adaptive Spatial Feature Fusion (ASFF) mechanism is introduced to alleviate semantic conflicts and noise interference during multi-scale feature fusion. This synergistic combination explicitly addresses multi-scale representation challenges and enhances small-object detection performance in complex scenes. Comparative experiments with the baseline YOLOv8n on the VisDrone2019 dataset demonstrate that ADA-YOLO achieves an improvement of 11.3% in mAP@0.5 and 8.2% in mAP@0.5:0.95. The improved model achieves these performance gains with a modest parameter increase and acceptable computational complexity. Finally, ablation experiments further validate the effectiveness of each individual module and their synergistic gains. Full article

Soil quality assessment, which considers numerous physical, chemical, and biological indicators, has long been a challenge for monitoring soil functions and ensuring sustainable resource use in agriculture. In this study, different indicator selection and weighting methods were compared to derive a reliable Soil Quality Index (SQI) in semi-arid agroecosystems. A total of 117 topsoil samples were taken from the Ap horizon within a 14,200 ha area of the Honam sub-catchment, southwestern Iran. Twenty-one soil indicators were measured and analyzed to assess the overall SQI. Soil indicator selection was performed using Principal Component Analysis (PCA), considering standard and norm value strategies, as well as component rotation. Four weighting approaches, including PCA, Coefficient of Variation (CV), correlation score (r), and Expert Opinion (EO), were applied to the Minimum Dataset (MDS) and Total Dataset (TDS) to compute the Integrated Quality Index (IQI), Nemoro (NQI), simple additive (IQIa), and Fuzzy Fertility Index (FFI). The performance of the SQI models was evaluated using the Sensitivity Index (SI) and their relationships with crop yield. The results showed that the combination of the norm value approach without component rotation was more effective in selecting the influential indicators for SQI determination. The Soil Stability Index (SSI), which integrates soil organic carbon and textural properties, was the key indicator with the highest contribution, ranging between 6.3% and 37.5% in most of the models. Among the evaluated approaches, the IQI-CV-MDS showed the highest sensitivity (SI = 6.8) and the strongest correlation (r = 0.53) with rainfed barley yield. The majority of the samples exhibited moderate SQI values, indicating a general risk of soil quality decline in the study area. The findings of this study highlight that appropriate indicator selection and weighting strategies are essential for improving the reliability of SQI assessments in semi-arid environments with diverse mountainous topography. Full article

Cultivated-meat production relies on robust animal cell-line engineering, scalable tissue-engineering strategies, and clearly defined regulatory standards. This review examines the developmental pipeline from primary tissue biopsy to large-scale expansion and regulatory evaluation, focusing on stable and safe immortalized cell platforms. We compare muscle satellite cells, mesenchymal stromal/adipogenic progenitors and induced pluripotent stem cells, highlighting trade-offs among proliferative capacity, lineage commitment, genomic stability, and food-safety considerations. We then analyze immortalization strategies, including spontaneous senescence bypass, telomerase reactivation and CRISPR-based checkpoint modulation, highlighting their impact on genomic stability and food-safety risks. Recent advances in serum-free media, extracellular matrix-mimetic biomaterials and staged co-culture protocols have enabled centimeter-scale tissues with improved texture and marbling; however, cost, reproducibility and scalability remain bottlenecks. Integrating multi-omics surveillance with life-cycle assessment reveals that environmental benefits (land, water and antibiotic reduction) are attainable only when energy inputs and growth-factor sourcing are optimized. Finally, we examine regulatory frameworks that distinguish food-grade immortalized cells from pharmaceutical substrates and genetically modified crops. By integrating cell biology, animal biotechnology, and bioprocess engineering, this review identifies technical priorities for advancing cultivated meat from laboratory development to industrial implementation, positioning genomic monitoring as an essential framework for assessing biological stability, functional predictability, and food-production suitability. Full article

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

Precision agriculture increasingly relies on machine learning applied to high-resolution data acquired by unmanned aerial vehicles (UAVs) to support crop monitoring, stress detection, and yield forecasting. This survey presents a structured review of machine learning methods for UAV-enabled precision agriculture and organizes over 100 peer-reviewed studies within a unified four-dimensional taxonomy defined by sensing modality, data type, model family, and analytical task. The taxonomy enables systematic comparison across RGB, multispectral, hyperspectral, LiDAR, and IoT data sources and across classical machine learning, deep learning, hybrid sequential models, and emerging transformer-based architectures. We analyze how modeling choices interact with data characteristics to influence robustness, cross-environment generalization, computational efficiency, and deployment feasibility on UAV and edge platforms. Recurring challenges include limited labeled data, domain shift across seasons and fields, multimodal heterogeneity, occlusion, and real-time processing constraints. We identify emerging research directions, including data-efficient learning, representation-level multimodal fusion, domain adaptation, lightweight architectures for embedded deployment, and uncertainty aware decision support. By formalizing the landscape through a unified taxonomy, this survey provides a foundation for designing scalable, robust, and deployable machine learning systems for next-generation precision agriculture. Full article

Seleniferous tea seedlings from Enshi, China, face cadmium (Cd) contamination risks due to the co-occurrence of selenium and cadmium in local soils, posing food safety concerns. While Astragalus sinicus-modified substrates are commonly applied for cadmium remediation, the performance of different monitoring techniques remains inadequately evaluated. This study compared four monitoring methods—growth traits, photosynthesis, chemical Cd removal rate, and plant electrophysiological parameters—in a pot experiment under cadmium stress (10 mg/kg Cd²⁺). Two tea varieties, Longjing 43 (Camellia sinensis ‘Longjing 43’. LJ 43) and Yulu 1 (Camellia sinensis ‘Yulu 43’. YL 1), were treated with four modified substrates (M1–M4). Specifically, compared to the control (M1), LM3 increased metabolic activity (MA), electrical impedance (EGC), and electrochemical response (ECR) by 140.27%, 122.5%, and 124.41%, respectively. These increases were significantly greater than those observed for the conventional metrics: 52.70% in total biomass (TB), 109.31% in photosynthetic rate (Pn), and 64.15% in chemical Cd removal (R_Cd). Similarly, in the YM4 treatment, MA and EGC increased by 214.91% and 178.66%, respectively, which also significantly exceeded the increments in TB (48.74%), Pn (116.19%), and R_Cd (75.26%). Among the electrophysiological parameters, MA proved to be the most sensitive indicator, showing a strong correlation with Cd removal capacity. In conclusion, plant electrophysiology enabled real-time, in situ monitoring of cadmium remediation efficiency, offering a novel technological pathway to ensure the safety of seleniferous tea seedlings and advance precision agriculture. Full article

Land Surface Temperature (LST) is essential for climate monitoring, drought assessment, and urban heat analysis. Despite its importance, the Landsat-8 Collection 2 Level-2 (C2L2) LST product has not been rigorously validated using ground measurements—a critical gap this study addresses. We present the first comprehensive accuracy assessment using 382 coincident satellite–ground observations collected from seven Surface Radiation Budget Network (SURFRAD) stations distributed across diverse climatic regions of the United States during the period 2023–2025. The validation results indicate strong overall agreement between satellite-derived and ground-measured temperatures, yielding an RMSE of 4.20 °C, a coefficient of determination (R²) of 0.91, and a Pearson correlation coefficient (r) of 0.98. These statistics demonstrate the high reliability of the C2L2 LST product across a wide range of environmental conditions. Nevertheless, a systematic warm bias of 1.75 °C was observed, indicating a tendency toward temperature overestimation. Model performance exhibited pronounced seasonal variability. The highest accuracy was achieved during winter conditions (RMSE = 2.17 °C; r = 0.99), whereas performance declined considerably during summer months (RMSE = 5.84 °C; r = 0.91). Analysis of atmospheric water vapor content revealed significant associations with retrieval errors at high-elevation and arid locations, particularly at FPK (r = 0.78) and DRA (r = 0.75), based on 106 matched observations. These relationships provide important insight into the atmospheric factors contributing to seasonal variations in retrieval accuracy. Temperature-dependent analyses further demonstrated that retrieval uncertainty increases with surface temperature. Performance progressively deteriorated from cooler to warmer thermal regimes, with RMSE values increasing from approximately 2.05 °C for temperatures below 20 °C to 5.71 °C for temperatures exceeding 40 °C. Spatial evaluation also revealed substantial differences among stations. Relatively homogeneous, low-elevation sites exhibited superior performance (GWN: RMSE = 2.60 °C; SXF: RMSE = 2.55 °C), whereas stations located in mountainous or topographically complex environments showed reduced accuracy (TBL: RMSE = 5.14 °C; FPK: RMSE = 5.62 °C). These outcomes emphasize the influence of terrain complexity and atmospheric heterogeneity on LST retrieval performance. Overall, this study establishes the first comprehensive benchmark for evaluating the reliability of Landsat-8 C2L2 LST products. The results provide valuable guidance for their application in climate research, precision agriculture, hydrological modeling, and environmental monitoring. Furthermore, the findings identify specific environmental conditions requiring enhanced validation efforts and suggest opportunities for future algorithm refinement through improved atmospheric correction procedures and more accurate surface emissivity characterization. Full article

4-Chlorophenoxyacetic acid (4-CPA), a synthetic auxin analog, is employed in agriculture both as a plant growth regulator and as a constituent of herbicide formulations. Consequently, the establishment of simple and rapid detection methods is essential for effective environmental monitoring. This study reports the first development of a homogeneous fluorescence polarization immunoassay (FPIA) for the determination of 4-CPA. The monoclonal antibody (M1), raised against 4-CPA, was evaluated as a recognition element. Furthermore, two fluorescently labeled 4-CPA tracers—with ethylenediamine fluorescein thiocarbamate and aminohexylaminocarbonylfluorescein—were synthesized and purified, and their structures were unequivocally confirmed by high-performance liquid chromatography coupled with high-resolution mass spectrometric detection (HPLC-HRMS). Optimal concentrations of monoclonal antibodies and tracers were established, yielding a limit of detection of 1.2 ng/mL. The assay demonstrated a broad dynamic range of 2.3–300 ng/mL and a rapid analysis time of 15 min. Validation via the standard addition method in authentic open water samples resulted in recovery rates of 98–112%. To address the cross-reactivity with the prevalent herbicide 2,4-dichlorophenoxyacetic acid (2,4-D), two novel strategies were devised and successfully implemented. The first approach involves the concurrent execution of two separate FPIAs—one for 2,4-D and one for 4-CPA—followed by the mathematical resolution of two analyte concentrations from the two measured binding values. The second strategy entails the preliminary selective removal of 2,4-D from sample matrices using affinity chromatography columns with immobilized anti-2,4-D antibodies prior to FPIA for 4-CPA. These proposed methodologies appear highly promising for overcoming the inherent limitations of traditional immunoassays when faced with significant cross-reactivity among structurally analogous compounds. Full article

Increasing pressure on surface water resources in intensive agricultural regions has driven the search for sustainable alternatives for irrigation supply, especially in areas where water quality limits crop safety and export opportunities. In this context, riverbank filtration (RBF) systems offer a nature-based solution by utilizing physical, chemical, and biological processes associated with river–aquifer exchange. However, their implementation depends on suitable site selection supported by hydrogeological, geomorphological, and hydraulic criteria. This study developed an integrated methodology to identify zones with potential for implementing RBF systems in the Roldanillo–Unión–Toro irrigation district, located in northern Valle del Cauca, Colombia. This region requires irrigation water over 10,256 ha of agricultural land (mainly sugarcane, maize, grapes, and guava). We combined geophysical methods (vertical electrical soundings, 2D electrical resistivity tomography, and passive seismic), geotechnical methods (CPTu tests), and hydraulic characterization of the river reach to evaluate subsurface stratigraphy, preliminary hydrogeological suitability, inferred river–aquifer connectivity conditions, and channel stability. The evaluation covered four sectors along an approximately 21 km stretch of the Cauca River’s left-bank alluvial valley. The results revealed pronounced lateral and vertical heterogeneity of alluvial materials. However, the “El Palmar” sector was identified as the best-supported priority sector for future RBF validation, due to the presence of profile-scale evidence of potentially permeable sandy and gravelly units with intermediate resistivity values (52–61 Ω·m), favorable stratigraphic organization, and stable river-reach conditions during the field campaign. In contrast, the other three sectors (La Esperanza, Candelaria, and Cayetana) showed more fine-grained sediments with deeper permeable strata. River-flow measurements during the July 2025 field campaign indicated high discharge conditions at the evaluated reach, while river-channel observations showed active fine-sediment transport; these findings provide hydraulic and sedimentary context for the future evaluation of induced infiltration and potential clogging, but do not constitute direct evidence of river–aquifer exchange. This study highlights the value of integrated screening approaches for prioritizing candidate RBF sites in agricultural alluvial settings, while indicating that pumping tests, piezometric monitoring, hydraulic-gradient analysis, and water-quality validation remain necessary before engineering implementation. Full article

With increasing constraints on extensive farming—including soil degradation, salinisation and more frequent climatic anomalies—the development of ‘smart’ agriculture requires the integration of affordable, non-invasive methods for monitoring the physiological state of plants. A key indicator for assessing productivity and the early detection of stress is the rate of photosynthetic CO₂ assimilation (A); however, widely available commercial gas analysers are characterised by high cost, technical complexity and considerable weight, which limits their use in large-scale field studies. Here, a new handheld system for measuring assimilation was developed and tested, based on the accumulative principle of recording changes in CO₂ concentration using simple infrared sensors and without maintaining a constant air flow around the leaf. A comparison was carried out between a prototype of the developed system and a commercial gas analyser when measuring leaf assimilation under irrigation and simulated drought conditions. The results demonstrated the consistency of the readings from the two systems. The developed system is characterised by its compact size, low cost, and the absence of moving parts and consumables. The proposed system has the potential to be effective for large-scale screening tasks and rapid diagnosis of stress-induced changes; it represents a promising, affordable tool for addressing applied tasks in precision agriculture, environmental monitoring and physiological research. Full article