Search Results (327)

The coastal wetlands of Yancheng, Jiangsu Province, serve as a crucial overwintering and stopover site for rare waterbirds such as red-crowned cranes. The changes in their landscape pattern are directly related to the protection of regional wetland ecological functions and biodiversity. Using land use data from 2010 to 2022 to extract landscape pattern indicators for each period, this study adopts the Covariance–Analytic Hierarchy Process (Cov-AHP) to construct a comprehensive Landscape Fragmentation Index (LFI) for coastal wetlands, considering aspects including patch density, boundary complexity, spatial connectivity, and landscape diversity. Combined with multi-source indicators of Ecology–Economy–Society (EES), the Generalized Additive Model (GAM) and Geographically Weighted Regression (GWR) are adopted to systematically analyze the nonlinear response relationships and spatially heterogeneous driving mechanisms of landscape fragmentation. GWR is employed to reveal the spatial heterogeneity of the influence of each driving factor on fragmentation by mapping local regression coefficients. The results show that: (1) During the study period, the overall landscape fragmentation of the coastal wetlands in Yancheng, Jiangsu Province, exhibited a slow increase trend, with a spatial gradient pattern of “higher in the north and lower in the south, and higher in coastal areas than in inland areas,” reflecting the combined effects of varying levels of economic development and human activity intensity across different administrative regions and along the coast-inland gradient. (2) Based on the deviance explained by the GAMs, social factors generally had higher explanatory power for LFI than ecological and economic factors. Specifically, human population density and the proportion of construction land showed a significant positive correlation with LFI, while NDVI and the proportion of farmland exhibited obvious nonlinear effects under different fragmentation levels. (3) The GWR results indicated that the regression coefficients of the main driving factors were highly spatially non-stationary, and regions with high coastal development intensity had the most significant promoting effect on landscape fragmentation. The local coefficient maps further reveal that GDP and NDVI exhibit the strongest spatial heterogeneity, with their effects shifting from positive to negative across different sub-regions. The study demonstrates that the integrated framework of Cov-AHP combined with GAM and GWR can effectively characterize the spatiotemporal dynamics and multi-dimensional driving mechanisms of coastal wetland landscape fragmentation, providing a reference for the protection of coastal wetlands in Yancheng, Jiangsu Province, and the conservation of waterbird habitats represented by red-crowned cranes. Full article

Unmanned aerial vehicle (UAV) multispectral imagery provides a promising approach for field-scale retrieval of soil organic matter (SOM) during the bare-soil period. However, tillage-induced surface heterogeneity is often overlooked. This heterogeneity may alter soil spectral responses and model performance. This study examined the effects of surface tillage status on UAV-based SOM retrieval in farmland. UAV multispectral imagery and 108 topsoil samples were collected during the bare-soil period. The SOM values ranged from 1.37 to 30.95 g/kg. Analyses were conducted under three tillage-status settings: undifferentiated tillage status, plowed-leveled status, and plowed-unleveled status. Spectral and textural features were extracted and selected using a genetic algorithm. These features were then used to develop SOM retrieval models with random forest regression, extreme gradient boosting, and support vector regression. For the six original multispectral bands, the correlations between SOM and band reflectance differed among tillage-status settings. They were weak under the undifferentiated tillage status. They were significantly negative under the plowed-leveled status and significantly positive under the plowed-unleveled status. Texture-derived indicators and standard normal variate analysis suggested that the positive correlations under the plowed-unleveled status may be partly associated with surface-structure-related spectral amplitude effects. Integrating textural features improved the overall test-set accuracy metrics. However, statistically detectable reductions in absolute prediction error were mainly observed under the plowed-unleveled status. On the random-split held-out test set, the highest $R^2$ values reached 0.84 and 0.85 under the plowed-leveled and plowed-unleveled statuses, respectively. These results indicate that surface tillage status is an important source of surface heterogeneity. It should therefore be explicitly considered in UAV-based SOM retrieval under the present study conditions. Full article

This review explicitly focuses on agricultural attachments and executing components that interact directly with soil and crops, rather than the tractor vehicle itself. Operating within complex and variable farmland media environments, the key components of agricultural machinery have long been constrained by bottlenecks such as high-energy draught resistance, severe solid–liquid interfacial adhesion, and intense abrasive wear. Bionic functional surfaces, based on the coupling of micro-geometric morphology and surface-interface physical chemistry, provide a scientific approach to overcoming traditional tribological limitations by reconstructing the contact mechanics and fluid dynamics boundaries at the interface. This paper presents a comprehensive review of the latest research progress regarding bionic functional surfaces in the fields of friction reduction, wear resistance, and anti-adhesion in agricultural machinery. The article systematically categorises typical biological prototypes, such as soil-burrowing animals, aquatic organisms, and plant leaves, alongside their multidimensional feature extraction methods. It provides an in-depth analysis of core interaction mechanisms, ranging from static air cushion effects and dynamic wetting evolution to active electro-osmotic soil detachment, interfacial stress redistribution, and microscopic wear debris capture. Furthermore, it evaluates the efficacy of cross-scale coupled numerical simulation technologies in resolving interfacial interactions. At the engineering application level, this review extensively discusses the field performance of bionic structures in typical operational scenarios, including draught reduction in tillage and land preparation, blockage prevention in seed-metering channels, and low-damage harvesting in agricultural machinery. Finally, countermeasures are proposed to address the fatigue degradation of bionic surfaces under alternating field loads and the barriers to the large-scale fabrication of large-sized components. The paper further highlights the development trend towards the deep integration of bionic tribology with digital twins and intelligent wear-state perception technologies, aiming to provide systematic underlying theoretical and technical references for the research and development of the next generation of intelligent agricultural equipment characterised by low energy consumption and a prolonged service life. Full article

Shortwave infrared (SWIR) imagery plays an important role in land–water boundary delineation, coastal monitoring, and complex aquatic environment observation. However, the spatial resolution of SWIR bands is usually lower than that of visible bands, which limits their capability to represent fine-scale targets and boundary structures. To address this problem, this study proposes MLE-ResUNet, a SWIR image super-resolution method that integrates along-track oversampling with visible-light-guided deep learning. The proposed method first exploits dual-view SWIR observations with sub-pixel displacement generated by increasing the sampling line rate in the push-broom imaging process. A maximum likelihood estimation (MLE)-based physical prior module is then introduced to transform multi-view degraded observations into a physically consistent latent high-resolution prior. Finally, high-resolution visible images are used to provide edge, texture, and structural guidance, and a ResUNet-based network is employed for multi-source feature fusion and residual reconstruction. Based on multi-region measured data acquired by the LHRSI (Lightweight High-Resolution Spectral Imager) payload onboard the BlueCarbon-1A satellite, a SWIR super-resolution dataset covering typical urban, farmland, and coastal scenarios was constructed. Comparative experiments were conducted against PCA, BDSD, PanNet, GPPNN, and two additional lightweight-guided deep learning baselines, namely LGPConv and a CANConv-style visible-guided baseline. The results show that MLE-ResUNet achieves the best performance across different scenarios and consistently outperforms the comparison methods in terms of SSIM, SAM, ERGAS, and Q-index. The proposed method effectively enhances spatial detail recovery while maintaining favorable spectral consistency. Ablation experiments further demonstrate that both along-track oversampling information and the MLE-based physical prior contribute to improved reconstruction quality and more stable training convergence. These findings indicate that the proposed method can enhance fine-scale SWIR observation capability without substantially increasing hardware complexity, providing an effective technical solution for shoreline identification, land–water boundary extraction, and complex surface target monitoring. Full article

Non-food cropping provides a practical strategy for the safe utilization of severely cadmium (Cd)-contaminated farmland. In this study, a field experiment was conducted to evaluate the effectiveness of layered double hydroxides (LDHs) in reducing Cd transfer from soil to Artemisia argyi, a plant used for non-food applications, and to estimate Cd release potential during moxa burning. Our results demonstrated that the application of LDHs increased soil pH and decreased the extractable Cd concentration based on CaCl₂ extraction, suggesting a reduction in Cd bioavailability. Furthermore, BCR fractionation analysis indicated a shift of Cd from more active to more stable forms, further supporting the reduction in Cd bioavailability in the soil. SEM–EDS and FTIR confirmed the lamellar morphology, CaAl composition, and hydroxyl-rich functional groups of the LDH conditioner. Plant growth was not negatively affected by LDH treatment, and Cd concentrations in roots, stems, and leaves were significantly reduced. LDHs also reduced Cd levels in processed moxa and the mass-balance-based estimate of Cd release during combustion. These findings suggest that LDHs application may help reduce Cd transfer in non-food cropping systems on severely contaminated farmland. Full article

In agricultural machinery visual navigation, accurately identifying the navigation line extraction region (NLER) at the center of the field of view is crucial for obtaining a precise navigation centerline. Although deep learning is the predominant method for NLER extraction, existing approaches face challenges in farmland environments characterized by densely distributed and irregularly extended leaves. These challenges result in unstable predictions, slow inference, and large model sizes that impede real-time applications. To address these issues, we propose a lightweight navigation segmentation residual network (LNS-ResNet), which integrates an inhibition–enhancement module (IEM) and a global convolutional residual block (GCRB). The IEM uses row–column one-dimensional convolutions to enhance vertical features between crop rows and suppress leaf-edge interference, producing more robust input features. The GCRB incorporates a full convolutional global attention (FCGA) mechanism to capture global context while preserving local spatial information. LNS-ResNet effectively reduces foliage interference and achieves accurate segmentation, with intersection over union (IoU) scores of 84.71% for crop row and 93.77% for path regions. Based on the segmentation output, we further propose a mask region determination-based navigation line extraction algorithm (MRD-Line), which directly identifies the NLER and connects the centerline within the mask without relying on line fitting. Deployed experiments on the Jetson TX2 demonstrate that the proposed method achieves both accuracy and efficiency, with mean angular deviations of 0.138° (path) and 0.425° (crop row), with average processing times of 64.1 ms (path) and 62.6 ms (crop row). Full article

Cadmium (Cd) contamination in agricultural soils poses serious risks to ecosystem health and food safety, highlighting the urgent need for efficient and environmentally stable immobilization materials. In this study, sodium alginate-based rectorite microspheres (REC beads) and triethylenetetramine-modified rectorite microspheres (TETA-REC beads) were fabricated and applied for the immobilization of Cd(II) in contaminated soils. Structural characterization confirmed that the ionic cross-linking encapsulation process preserved the layered structure of rectorite, while TETA modification introduced abundant amino functional groups that enhanced the interaction between the material and Cd(II). Immobilization experiments demonstrated that both microspheres exhibited rapid and stable Cd(II) passivation performance, with TETA-REC beads showing significantly higher efficiency. The maximum removal efficiencies of water-soluble and available Cd(II) reached 83.87% and 93.33%, respectively. Sequential extraction analysis revealed that the microspheres effectively transformed labile Cd fractions, including exchangeable and water-soluble forms, into more stable species. Mechanistic investigations indicated that Cd immobilization was governed by a synergistic combination of interlayer ion exchange, physical adsorption within the porous alginate structure, and coordination complexation between Cd(II) and amino groups introduced by TETA. Furthermore, microbial community analysis showed that the remediation process promoted the recovery of soil microbial diversity and metabolic functions. The developed TETA-REC microspheres provide a promising strategy for the efficient immobilization of Cd in contaminated soils and offer potential for the sustainable remediation of heavy metal-polluted farmland. Full article

Accurate, high-resolution irrigation-related spatial information is paramount to diverse applications, including water resources management, food security, and agricultural planning. To address this need, our study leveraged machine learning algorithms and integrated multi-source data to extract and analyze land use types and spatiotemporal dynamics of irrigated farmland across provinces in the lower reaches of the Yellow River Basin over the 2008–2022 period. The results indicate that cultivated land remained dominant and largely stable, although localized losses occurred in peri-urban areas due to urban expansion. Construction land increased significantly, particularly in Shandong where it expanded by more than 15%, while forest and grassland areas grew under national ecological programs. The Random Forest (RF) algorithm achieved robust performance in identifying irrigated farmland, with overall accuracy exceeding 85% and regression with statistical irrigation data yielding R² values above 0.9 over the past 15 years at the city level. Spatiotemporal analysis showed strong variability in Henan, with irrigated area declining by 8–12% during drought years and recovering in wetter years, while Shandong experienced relative stability but a gradual 5% decline since 2015, driven by groundwater depletion and stricter regulation. The findings suggest irrigation expansion has reached near-saturation, given stable cultivated land and continuous improvements in water use efficiency. Future strategies should prioritize water use efficiency, water saving technologies, and equitable allocation to ensure sustainable agricultural development. Full article

Visual scene matching navigation (VSMN) for unmanned aerial vehicles (UAVs) boasts advantages such as high precision, high reliability, and autonomy. The biggest challenge lies in the tension between local fine-grained information and global semantics, as well as limited generalization ability in real-world environments. While existing Transformer-based cross-view geolocation methods enhance global context modeling capabilities, they still generally face issues such as high demands on training data and computational resources, insufficient fusion of local fine-grained information and global semantics, and real-time performance in real-world complex environment. To address these problems, we propose a scene matching and localization algorithm based on the Siamese-ViT. For feature extraction, we use the ViT model to extract global features and K-means clustering to aggregate local features. Combined with the global features extracted by the ViT, a robust local–global feature representation vector is generated. For feature matching, incremental principal component analysis (IPCA) is used to reduce the dimensionality of the high-dimensional feature space, and a KD-tree is constructed for fast feature retrieval to improve matching efficiency. We validated our algorithm on the University-1652 dataset and a dataset of real-world satellite-drone image pairs. The results show that our Siamese-ViT outperforms other models in both Recall and AP. We conduct flight experiments in real-world environments, capturing drone images of complex scenes, including farmland, urban buildings, and waterways. The results show that, at a flight altitude of 350 m, our algorithm achieves an average absolute value of 6.2063 m for latitude, 6.7552 m for longitude, and 10.1922 m for horizontal error. Therefore, our Siamese-ViT demonstrates ideal overall positioning accuracy. Full article

Farmland resources are fundamental to human survival and play a vital role in ensuring global food security. However, farmland boundary detection remains a significant technical challenge due to the low proportion of boundary pixels, multi-scale variations, and weak boundary continuity. To address these issues, this study proposes DCFENet, a dual-branch collaborative feature enhancement network. Specifically, a multi-scale feature fusion attention module TA-ASPP (Task-Aware Atrous Spatial Pyramid Pooling) is designed, which effectively enhances the network’s perception of farmland boundary features by integrating multi-scale dilated convolutions with skeleton-aware attention. In addition, a dual-branch decoding structure is proposed to enhance boundary localization and global topology modeling through boundary-aware gating and cross-branch feature fusion, thereby improving the boundary continuity. Furthermore, a collaborative constraint mechanism is proposed for dual-branch decoding, which supervises the two decoders using boundary loss and skeleton loss, thereby enhancing structural consistency and topology preservation. Experimental results demonstrate that DCFENet achieves precision, recall, and boundary IoU of 74.5%, 68.1%, and 77.4%, respectively, representing an improvement of 26.8%, 36.3%, and 13.2% compared with ResNet18_UNet. It also outperforms mainstream methods such as UNet, EdgeNAT, and EDTER. In terms of computational efficiency, DCFENet contains 26.43 M parameters and 37.43 G FLOPs, with a memory usage of 1.03 GB and an inference speed of 97.97 FPS, achieving a good balance between accuracy and efficiency. The results demonstrate the efficiency and accuracy of DCFENet in extracting farmland boundaries from high-resolution remote sensing images, providing technical support for farmland management and the advancement of precision and digital agriculture. Full article

In saline–alkali environments, soil salinity imposes severe abiotic stress on maize growth by inhibiting root activity and nutrient uptake. Traditional destructive sampling methods struggle to enable cross-growth stage, large-scale dynamic fertilizer effect assessment. This study, conducted in saline–alkali farmlands of Inner Mongolia, utilized UAV multispectral remote sensing to extract 20 vegetation indices and 40 texture parameters, constructing a multi-source feature set. An ensemble learning framework integrating Random Forest (RF), Decision Tree (DTR), AdaBoost and Gradient Boosting Regression (GBR) was developed to achieve precise monitoring of maize plant height, leaf area index (LAI), and yield. In addition, the study aimed to evaluate the dynamic effects of seven fertilizer treatments (six controlled-release composite fertilizers, T1–T6, and conventional CK) and to identify the optimal fertilization scheme, with particular emphasis on comparing the two best-performing treatments, T1 and T2. Results showed that: (1) The ensemble model improved prediction robustness, with R² values of 0.88, 0.76, and 0.76 for plant height, LAI, and yield across the entire growth cycle, respectively. The integration of texture features effectively mitigated spectral saturation during peak growth stages (e.g., tasseling and filling). (2) For fertilizer evaluation, T1 performed best in growth and yield at jointing, tasseling, and filling stages, with a yield increase rate of up to 40.18% at the jointing stage. Although T2 slightly outperformed T1 in yield increase at maturity (15.42%), T1 was identified as the optimal fertilizer scheme for the region based on whole-growth-stage growth performance, measured yield, LAI, and yield increase rate. These results demonstrate that UAV-based multi-source feature fusion combined with ensemble learning provides an effective and non-destructive approach for fertilizer evaluation and precision nutrient management in saline–alkali regions. Full article

Fragmented farmland and persistent cloud–snow interference in the high-altitude cold regions of the Qinghai–Tibet Plateau, coupled with unstable crop phenology, pose significant challenges for accurate cereal and oil crop identification using single-date imagery or low-dimensional features. This study focused on the agricultural areas of the Shigatse River Valley in the Qinghai–Tibet Plateau. Leveraging the Google Earth Engine (GEE) cloud computing platform, we integrated Sentinel-2 remote sensing data with field survey sampling data to extract the planting structures, distribution patterns, and cultivated areas of cereal and oil crops. Three machine-learning classifiers—Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosted Trees (GBT)—were evaluated to investigate the influence of different feature sets and classifier combinations on mapping accuracy. The results indicated that when all feature bands were utilized, the RF classifier achieved the highest performance, with an overall accuracy of 84.77% and a kappa coefficient of 0.64, outperforming both the SVM and GBT models. The incorporation of phenological and topographic features further enhanced classification accuracy, providing a robust framework for identifying cereal and oil crops in high-altitude environments. Based on the optimal model estimation, the cultivated areas in 2021 were 581.52 km² for highland barley, 295.39 km² for wheat, and 386.81 km² for rapeseed. Their spatial patterns closely aligned with the valley-terrace topography and local irrigation conditions. These findings offer novel insights and a reliable methodology for the rapid extraction of crop spatial information in regions with complex planting structures. Full article

Food insecurity occurs due to the impact of climate change and intense global conditions. Thus, understanding crop farming plans and monitoring crop yields have become major tasks for decision makers. Previous work has applied remote sensing techniques and empirical methods to predict the yields and analyse the relationships between spectral indices and historical crop yield data. However, a limitation of these studies is that they do not extract the values of spectral indices by crop types when the testing area is regional with multiple farmlands and requires a crop classification process. This can cause inaccurate results when investigating the correlations between the yield and the spectral indices. This research develops a yield prediction framework with historical crop maps by means of unsupervised classification with zero ground truth using Sentinel-2 imagery to retrieve the values of spectral indices of winter barley. The extracted spectral indices and the meteorological and historical yield data in North Norfolk, UK, are implemented in 1D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and CNN–LSTM for winter barley yield predictions. LSTM has outstanding performance overall and the best result approaches a Root Mean Square Error (RMSE) of 0.406 kg/hectare, a Mean Square Error (MSE) of 0.165 kg/hectare and a Mean Absolute Error (MAE) of 10.495 kg/hectare. The EVI in April, May and June is the most important feature in the LSTM model and shows strong positive correlation with the yield of winter barley. The developed framework with unsupervised crop classification and LSTM can be applied to multiple crop types and in different regions using opensource datasets, historical yields, spectral indices and meteorological data. Correlations between these datasets indicate that higher EVI and maximum and minimum temperature and sun hours at the germination and seedling growth stages increase the yields of winter barley, but excess Water Content (WC) in plants with a higher Normalised Difference Moisture Index (NDMI) from April to June leads to a decline in the yields of winter barley. Full article

Early detection of agricultural fires with Unmanned Aerial Vehicles (UAVs) is important for environmental safety, yet it remains difficult because ignition cues are extremely small, smoke patterns vary widely, and farmland scenes often contain strong background interference such as specular reflections. Model development is further constrained by the scarcity of data from the early ignition stage. To address these challenges, we propose a joint data and model optimization framework. We first build a hybrid dataset through an ROI-guided synthesis pipeline, in which latent diffusion models are used to insert high-fidelity, carefully screened fire samples into real farmland backgrounds. We then introduce EF-YOLO, a detector designed for high sensitivity to small targets. The network uses SPD-Conv to reduce feature loss during spatial downsampling and includes a high-resolution P2 head to improve the detection of minute objects. To reduce background clutter, a Dual-Path Frequency–Spatial Enhancement (DP-FSE) module serves as a lightweight statistical surrogate that extracts global contextual cues and local salient features in parallel, thereby suppressing high-frequency noise. Experimental results show that EF-YOLO achieves an ${AP}_S$ of 40.2% on sub-pixel targets, exceeding the YOLOv8s baseline by 15.4 percentage points. With a recall of 88.7% and a real-time inference speed of 78 FPS, the proposed framework offers a strong balance between detection performance and efficiency, making it well suited for edge-deployed agricultural fire early-warning systems. Full article

To address the challenges of modal differences in multimodal farmland images and insufficient segmentation accuracy for small targets, this paper proposes a multi-source image fusion branch (DAMFusion) based on modal competitive selection. The branch dynamically selects infrared and visible light features through the Competitive Query Module (CQM) using Top-K screening, combined with IOU-aware loss optimization to avoid cross-modal interference. The multimodal fusion module (MMFormer) employs cross-modal attention and symmetric mechanisms, enhancing single-modal features through a self-enhancement module and unifying multimodal distributions via linear projection. The Boundary Region Attention Multi-level Fusion Module (BRM) extracts boundary information through feature differencing, strengthens it with spatial attention, and fuses it with shallow features to achieve cross-layer detail recovery. Through the collaborative design of dynamic modal feature selection, cross-modal distribution unification, and boundary region enhancement, DAMFusion effectively solves the problems of multimodal differences and small target segmentation in multispectral images, providing precise feature representation for fine farmland segmentation. Experiments on the OUC-UAV-MSEG dataset show that DAMFusion achieves 93.25% OA, 91.71% F1, and 89.70% mIoU, demonstrating clear advantages over representative comparison methods. In addition, ablation results verify the effectiveness of the proposed modules, where CQM improves OA from 91.00% to 93.25%, confirming the importance of discriminative modality selection before fusion. Full article