Search Results (2,343)

Unmanned aerial vehicles (UAVs) are now widely used in various applications, including agriculture, urban traffic management, and search and rescue operations. However, several challenges arise, including the small size of objects occupying only a sparse number of pixels in images, complex backgrounds in aerial footage, and limited computational resources onboard. To address these issues, this paper proposes an improved UAV-based small object detection algorithm, YOLO11s-UAV, specifically designed for aerial imagery. Firstly, we introduce a novel FPN, called Content-Aware Reassembly and Interaction Feature Pyramid Network (CARIFPN), which significantly enhances small object feature detection while reducing redundant network structures. Secondly, we apply a new downsampling convolution for small object feature extraction, called Space-to-Depth for Dilation-wise Residual Convolution (S2DResConv), in the model’s backbone. This module effectively eliminates information loss caused by pooling operations and facilitates the capture of multi-scale context. Finally, we integrate a simple, parameter-free attention module (SimAM) with C3k2 to form Flexible SimAM (FlexSimAM), which is applied throughout the entire model. This improved module not only reduces the model’s complexity but also enables efficient enhancement of small object features in complex scenarios. Experimental results demonstrate that on the VisDrone-DET2019 dataset, our model improves mAP@0.5 by 7.8% on the validation set (reaching 46.0%) and by 5.9% on the test set (increasing to 37.3%) compared to the baseline YOLO11s, while reducing model parameters by 55.3%. Similarly, it achieves a 7.2% improvement on the TinyPerson dataset and a 3.0% increase on UAVDT-DET. Deployment on the NVIDIA Jetson Orin NX SUPER platform shows that our model achieves 33 FPS, which is 21.4% lower than YOLO11s, confirming its feasibility for real-time onboard UAV applications. Full article

This study empirically investigates the impact and underlying mechanisms of farmers’ digital capability (DC) on large-scale farmland management, utilizing micro-survey data from 1144 rural households across five provinces in China: Anhui, Henan, Shaanxi, Hebei, and Shandong. The analysis employs a double machine learning model (DML). The results demonstrate that DC is positively related to farmers’ farmland inflow, thereby facilitating the realization of large-scale land management. Mechanism analysis reveals that farmers’ DC affects large-scale farmland management by expanding the transaction radius and improving agricultural production efficiency. Heterogeneity analysis indicates that the positive effect of DC on farmland inflow is more pronounced when farmers possess advantages in human capital, income levels, business entity characteristics, and natural endowments. This finding suggests that the impact of farmers’ DC on large-scale farmland management is not yet inclusive. Accordingly, the government should actively construct a cultivation system for farmers’ DC, build an inclusive digital service platform for farmland transfer, help farmers bridge the digital divide, and further unleash digital dividends. In future research, we will conduct follow-up surveys on fixed farmer households to expand the survey scope, optimize the measurement of key variables, and carry out comparative analyses across different institutional contexts, thereby providing a more systematic scientific basis for the development of agricultural modernization driven by digital empowerment. Full article

This study presents a life cycle assessment of a low-cost pilot-scale wastewater treatment system that combines solar photocatalytic oxidation with Nature-based Solutions (NBSs) for a specially constructed wetland (CW). The prototype was designed and assessed for its efficiency in treating urban wastewater and its environmental impact on agricultural irrigation reuse. Evaluations were performed with the SimaPro software, applying the Impact ReCiPe Medpoint methodology, which includes characterization and selection of the relevant environmental issues steps. The results demonstrate the potential of this hybrid system for providing high-quality treated wastewater suitable for agricultural reuse in water-scarce regions. The analysis reveals that the operational phase, mainly driven by energy consumption for pumping, aeration, and photocatalytic processes, accounts for over 85–98% of the total global warming potential (GWP), primarily due to reliance on fossil-based electricity. Conversely, the construction phase significantly impacts land use and toxicity categories, with concrete and substrate production contributing around 95% to land occupation and 97% to human toxicity. The photocatalytic subsystem also contributes notably to embodied carbon at 42.4%, owing to energy-intensive manufacturing. The results underscore the importance of optimizing operational energy efficiency and selecting sustainable materials to mitigate environmental burdens. The integrated system demonstrates promising potential for producing high-quality treated effluent suitable for agricultural reuse in water-scarce regions, supporting sustainable water management. These findings provide important insights for reducing ecological impacts and advancing environmentally sustainable wastewater treatment solutions. Full article

Soil organic matter (SOM) is a key indicator of arable land quality and the global carbon cycle; accurate regional-scale SOM estimation is vitally significant for sustainable agricultural development and climate change research. This study evaluates a multisource data-fusion approach for improving cropland SOM prediction in Yucheng City, Shandong Province, China. We applied a Local Energy Weighted Discrete Wavelet Transform (LEW-DWT) to fuse multi-temporal Landsat 8 imagery (2014–2023). Quantitative analysis (e.g., Information Entropy and Average Gradient) demonstrated that LEW-DWT effectively preserved high-frequency spatial details and texture features of fragmented croplands better than traditional DWT and simple splicing methods. These were combined with 41 environmental predictors to construct composite Ev–Tn–Mm features (environmental variables, temporal NDVI features, and multi-temporal multispectral information). Random Forest (RF) and Convolutional Neural Network (CNN) models were trained and compared to assess the contribution of the fused data to SOM mapping. Key findings are: (1) Comparative analysis showed that the LEW-DWT fusion strategy achieved the lowest spectral distortion and highest spatial fidelity. Using the fused multitemporal dataset, the CNN attained the highest predictive performance for SOM (R² = 0.49). (2) Using the Ev–Tn–Mm features, the CNN achieved R² = 0.62, outperforming the RF model (R² = 0.53). Despite the limited sample size, the optimized shallow CNN architecture effectively extracted local spatial features while mitigating overfitting. (3) Variable importance analysis based on the RF model reveals that mean soil moisture is the primary single variable influencing the SOM, (relative importance 15.22%), with the NDVI phase among time-series features (1.80%) and the SWIR1 band among fused multispectral bands (1.38%). (4) By category, soil moisture-related variables contributed 45.84% of total importance, followed by climatic factors. The proposed multisource fusion framework offers a practical solution for regional SOM digital monitoring and can support precision agriculture and soil carbon management. Full article

The high brand premium of geographical indication (GI) tea has not been efficiently converted into widespread economic benefits through its supply chain. The current performance evaluation system is confronted with a dual predicament: first, the strong external environment (such as policy support and industrial agglomeration) interference is hard to isolate, making it impossible to distinguish between “environmental advantages” and “true management levels”; second, the general agricultural indicators fail to capture the output essence of GIs centered on “brand value”. Therefore, this study constructs an evaluation framework integrating methodological and indicator innovations. Methodologically, a three-stage DEA model is adopted to eliminate the influence of exogenous environments and random noises, precisely measuring the “pure management efficiency” of the supply chain. Indicatively, common variables are abandoned, and a customized system is established with logistics facilities, production area, and regional digital investment as inputs, and brand reputation, value, and income as outputs. Based on the panel data of twelve representative tea GIs from 2021 to 2024, the study finds that the following: (1) The “pure management efficiency” of the supply chain is the key factor influencing performance evaluation. (2) “Diseconomies of scale” are the main structural bottleneck restricting performance improvement rather than technological backwardness. (3) Solving the above-mentioned management efficiency problems, especially resolving “diseconomies of scale”, is the micro foundation for achieving sustainable industrial development. This research not only provides methodological support and empirical evidence for the refined management and sustainable development of the geographical indication agricultural product supply chain, but also has significant practical significance for promoting the quality and efficiency improvement of the tea industry and facilitating the sustainable development of related agriculture. Full article

Groundwater is a critical resource for agriculture and livelihoods, particularly in semi-arid regions such as Gujarat and Rajasthan in India. However, unsustainable extraction has led to aquifer depletion and increased water insecurity. This study uses Ostrom’s design principles to evaluate how Village Groundwater Cooperatives (VGCs) are transitioning toward self-governance in managing groundwater commons. Through field research in Dharta (Rajasthan) and Meghraj (Gujarat), including 33 key informant interviews and nine focus group discussions, this study assesses institutional robustness, rule enforcement, and community participation. Findings reveal that VGCs have the potential to enhance groundwater security through collective water budgeting and recharge interventions, though institutional robustness is constrained by limited formal enforcement. In Hinta, pipelines connected four wells to distribute water equitably, while in Dharta and Meghraj, traditional water-sharing agreements (two-part and three-part systems) sustained cooperation. Groundwater monitoring by trained “Bhujal Jankaars” helped farmers plan crop cycles, supporting informed crop choices that better aligned with available water supply. Despite these successes, to strengthen VGCs for effective groundwater management, formal sanctioning mechanisms are needed to address rule violations. Additionally, women’s participation in groundwater management decisions and operationalising VGCs is low. Conflict resolution mechanisms are currently informal. This study suggests that because women primarily manage domestic water needs while men manage irrigation, integrating women into decision-making is essential to reconcile competing water demands and ensure the long-term viability of VGCs. The findings provide policy insights for scaling up community-led groundwater governance in semi-arid regions. Full article

The transition to a circular bioeconomy enhances the valorization of agricultural by-products. Hawthorn leaves (Crataegus spp.), generated in large quantities from orchard maintenance, represent a promising yet underutilized biomass. This comprehensive narrative review synthesizes recent advances regarding their bioactive compounds, extraction methods, and applications. A systematic literature search was conducted to identify relevant studies. The analysis reveals that hawthorn leaves are rich in polyphenols (e.g., flavonoids, procyanidins), with their content often exceeding that found in fruits. Modern “green” extraction techniques (e.g., ultrasound- and microwave-assisted) demonstrate superior efficiency in recovering these thermolabile compounds compared to conventional methods. The broad spectrum of associated biological activities—including antioxidant, cardioprotective, neuroprotective, antimicrobial, and insecticidal effects—underpins their potential in nutraceuticals, cosmetics, and functional foods. Crucially, this review highlights the significant promise of hawthorn leaf extracts as a source for developing natural, plant-based biopesticides, aligning with sustainable agriculture and integrated pest management principles. To fully realize this “waste-to-wealth” potential, future research should prioritize the scaling of eco-friendly extraction, field trials for crop protection efficacy, and the standardization of extracts. Full article

The growing demand for sustainable and distributed energy solutions has driven increasing interest in triboelectric nanogenerators (TENGs) as platforms for energy harvesting and self-powered sensing. Biowaste-based triboelectric nanogenerators (BW-TENGs) represent an attractive strategy by coupling renewable energy generation with waste valorization under the principles of the circular bioeconomy. This review provides a comprehensive overview of BW-TENGs, encompassing fundamental triboelectric mechanisms, material categories, processing and surface-engineering strategies, device architectures, and performance evaluation metrics. A broad spectrum of biowaste resources—including agricultural residues, food and marine waste, medical plastics, pharmaceutical waste, and plant biomass—is critically assessed in terms of physicochemical properties, triboelectric behavior, biodegradability, biocompatibility, and scalability. Recent advances demonstrate that BW-TENGs can achieve electrical outputs comparable to conventional synthetic polymer TENGs while offering additional advantages such as environmental sustainability, mechanical compliance, and multifunctionality. Key application areas, including environmental monitoring, smart agriculture, wearable and implantable bioelectronics, IoT networks, and waste management systems, are highlighted. The review also discusses major challenges limiting large-scale deployment, such as material heterogeneity, environmental stability, durability, and lack of standardization, and outlines emerging solutions involving material engineering, hybrid energy-harvesting architectures, artificial intelligence-assisted optimization, and life cycle assessment frameworks. Full article

Estimating grape yields in viticulture is hindered by persistent challenges, including strong occlusion between grapes, irregular cluster morphologies, and fluctuating illumination throughout the growing season. This study introduces YOLOv11-IMP, an improved multiscale anchor-free detection framework extending YOLOv11, tailored to vineyard environments. Its architecture comprises five specialized components: (i) a viticulture-oriented backbone employing cross-stage partial fusion with depthwise convolutions for enriched feature extraction, (ii) a bifurcated neck enhanced by large-kernel attention to expand the receptive field coverage, (iii) a scale-adaptive anchor-free detection head for robust multiscale localization, (iv) a cross-modal processing module integrating visual features with auxiliary textual descriptors to enable fine-grained cluster-level yield estimation, and (v) aross multiple scales. This work evaluated YOLOv11-IMP on five grape varieties collecten augmented spatial pyramid pooling module that aggregates contextual information acd under diverse environmental conditions. The framework achieved 94.3% precision and 93.5% recall for cluster detection, with a mean absolute error (MAE) of 0.46 kg per vine. The robustness tests found less than 3.4% variation in accuracy across lighting and weather conditions. These results demonstrate that YOLOv11-IMP can deliver high-fidelity, real-time yield data, supporting decision-making for precision viticulture and sustainable agricultural management. Full article

The monitoring of perennial and annual crops requires different analytical approaches due to their contrasting phenological dynamics and management practices. This study investigates the temporal behavior of the Normalized Difference Vegetation Index (NDVI) derived from Harmonized Landsat and Sentinel-2 (HLS) imagery to characterize apple, grape, soybean, and maize crops in Vacaria, Southern Brazil, between January 2024 and April 2025. NDVI time series were extracted from cloud-free HLS observations and analyzed using raw, interpolated, and Savitzky–Golay, smoothed data, supported by field reference points collected with the AgroTag application. Distinct NDVI temporal patterns were observed, with apple and grape showing higher stability and soybean and maize exhibiting stronger seasonal variability. Descriptive statistics derived from 112 observation dates confirmed these differences, highlighting the ability of HLS-based NDVI time series to capture crop-specific phenological patterns at the municipal scale. Complementary analysis using the SATVeg platform demonstrated consistency in long-term vegetation trends while evidencing scale limitations of coarse-resolution data for small perennial plots. Overall, the findings demonstrate that the NDVI enables robust monitoring of mixed agricultural landscapes, with complementary spatial resolutions and analytical tools enhancing crop-specific phenological analysis. Full article

Identifying crops at the plot level is essential for developing effective agricultural management policies across diverse scales. The agricultural landscape of the Comunitat Valenciana (CV) region in Spain is characterized by a high density of small plots and a wide variety of crops, ranging from rice fields to vine and tree orchards, the latter being the predominant type. This fragmentation poses challenges for current crop monitoring using satellite imagery provided by the Sentinel-2 (S2) mission, largely because its relatively low spatial resolution results in pixels overlapping field boundaries. However, this study proposes a methodological approach that exploits the high temporal resolution of S2 to help overcome these limitations and automatically classify the six most representative crop types in this fragmented landscape. The study analyzed temporal variations in the correlation structure of common spectral indices over the year, leading to the selection of the Normalized Difference Moisture Index (NDMI), Normalized difference Red Edge Index (NDRE), and Plant Senescence Reflectance Index (PSRI) for complementary information. Fourier coefficients of a year time series of these indices served as inputs for a random forest classifier. Relative importance of indices for the classification was also assessed. Additionally, a new metric for classification confidence at plot level is introduced. This metric enables strategies to balance between classification precision and the proportion of classified plots. The model achieved an overall accuracy of 86.85% and a kappa index of 0.82 without considering classification confidence levels. Applying a 70% confidence threshold increased overall accuracy to 93.44% and the kappa index to 0.91 at a cost of 16.19% of plots unclassified. Full article

High-precision detection of rice targets in precision agriculture is crucial for yield assessment and field management. However, existing models still face challenges, such as high rates of missed detections and insufficient localization accuracy, particularly when dealing with small targets and dynamic changes in scale and morphology. This paper proposes an accurate rice detection model for UAV images based on Adaptive Aware Dynamic Convolution, named Adaptive Dynamic Convolution YOLO (ADC-YOLO), and designs the Adaptive Aware Dynamic Convolution Block (ADCB). The ADCB employs a “Morphological Parameterization Subnetwork” to learn pixel-specific kernel shapes and a “Spatial Modulation Subnetwork” to precisely adjust sampling offsets and weights—realizing for the first time the adaptive dynamic evolution of convolution kernel morphology with variations in rice scale. Furthermore, ADCB is embedded into the interaction nodes of the YOLO backbone and neck; combined with depthwise separable convolution in the neck, it synergistically enhances multi-scale feature extraction from rice images. Experiments on public datasets show that ADC-YOLO comprehensively outperforms state-of-the-art algorithms in terms of AP50 and AP75 metrics and maintains stable high performance in scenarios such as small targets at the seedling stage and leaf overlap. This work provides robust technical support for intelligent rice field monitoring and advances the practical application of computer vision in precision agriculture. Full article

Rapid land use and land cover (LULC) changes in densely populated watersheds pose serious challenges to the sustainability of ecosystem services (ES), yet their spatially explicit economic consequences remain insufficiently understood. This study analyzes the spatio-temporal dynamics of LULC and ecosystem service values (ESVs) in the Citarum Watershed, Indonesia, one of the country’s most critical and intensively transformed watersheds. Multi-temporal Landsat imagery from 2003, 2013, and 2023 was classified using a Random Forest algorithm, while future LULC conditions for 2043 were projected using a Multi-layer Perceptron–Markov Chain (MLP–MC) model under three scenarios: Business-as-Usual (BAU), Protecting Paddy Field (PPF), and Protecting Forest Area (PFA). ESVs were quantified at multiple spatial scales (county, 250 m grids, and 100 m grids) using both the Traditional Benefit Transfer (TBT) method and a Spatial Benefit Transfer (SBT) approach that integrates biophysical indicators with socio-economic variables. The contribution of LULC transitions to ESV dynamics was further assessed using the Ecosystem Service Change Intensity (ESCI) index. The results reveal substantial historical forest and shrubland losses, alongside rapid expansion of settlements and dryland agriculture, indicating intensifying anthropogenic pressure on watershed functions. Scenario analysis shows continued degradation under BAU, limited mitigation under PPF, and improved forest retention under PFA; although settlement expansion persists across all scenarios. Total ESV declined from USD 2641.33 million in 2003 to USD 1585.01 million in 2023, representing a cumulative loss of 46.13%. Projections indicate severe ESV losses under BAU and PPF by 2043, while PFA substantially reduces, but does not eliminate economic degradation. ESCI results identify forest and shrubland conversion to settlements and dryland agriculture as the dominant drivers of ESV decline. These findings demonstrate that integrating multi-scenario LULC modeling with spatially explicit ESV assessment provides a more robust basis for ecosystem-based spatial planning and supports sustainable watershed management under increasing development pressure. Full article

Accurate and timely agricultural mapping is essential for supporting sustainable agricultural development, resource management, and food security. Despite its importance, Vietnam lacks detailed and consistent large-scale agricultural maps. In this study, we produced the first national-scale agricultural map of Vietnam for 2024 using a UNet++ deep learning architecture that integrates multi-temporal Sentinel-1 and Sentinel-2 imagery with Global-30 DEM data. The resulting product includes 15 land-cover categories, eight of which represent the most popular agricultural types in Vietnam. We further evaluate the model’s transferability by applying the 2024 trained model to generate a corresponding map for 2020. The approach achieves overall classification accuracies of $83.01\%\pm 1.37\%$ (2020) and $80.09\%\pm 0.76\%$ (2024). To address class imbalance within the training dataset, we introduced an adaptive weight combined loss function that automatically adjusts the weight of dice loss and cross-entropy loss within a combined loss function during the model training process. Full article

Assessment of crop water requirements (ETc) and their meteorological driving mechanisms are critical for irrigation management in arid inland river basins. Taking the Tailan River Irrigation District (Xinjiang, China) as a case study, temporal changes in cropping structure, crop-specific ETc, and irrigation-district–scale agricultural water demand, as well as the meteorological controls on ETc, were quantified for the period 2000–2024 using Google Earth Engine-based crop mapping, the CROPWAT model, and path analysis. The results demonstrated that the 2024 random forest classification model achieved high accuracy (overall accuracy = 0.902; Kappa = 0.876), and validation against statistical yearbook data confirmed the reliability of crop-area estimation. Cotton dominated the cropping structure (228.6–426.0 km²), while the orchard area expanded markedly from 206.5 km² in 2000 to 393.2 km² in 2024; wheat exhibited strong interannual variability, and maize occupied a relatively small area. Crop-specific ETc differed markedly among crop types, following the order orchard > cotton > maize > wheat, with orchards maintaining the highest water requirement across all growth stages. Total agricultural water demand, estimated by integrating crop-specific ETc with remotely sensed planting areas, increased from approximately 260 million m³ to over 500 million m³ after 2010, mainly due to orchard expansion and cotton cultivation. Path analysis indicated that interannual ETc variability exhibited a stronger statistical association with wind speed than with other meteorological variables. These results provide a quantitative basis for cropping-structure optimization and water-saving irrigation management under changing climatic conditions. Full article