Search Results (3,739)

Development of new agricultural fungicides requires reliable computational tools to screen candidate molecules before investing time, money, and effort in experimental assays. In this study, curated datasets of fungicidal and non-fungicidal compounds were employed in the construction of possible predictive models using diverse machine-learning algorithms and molecular descriptors. The best performance was obtained using balanced datasets and topological descriptors. Two classification models based on different kinds of negative class instances were selected, achieving results of accuracy, sensitivity, and specificity for the test set of 0.806, 0.778, and 0.828 for the first model (RF_BF_14), and 0.937, 0.943, and 0.933 for the second model (RF_BF_17). Differences in performance were consistent with the chemical nature of the negative class. Both models showed excellent applicability domain coverage (>99.6%). Predictions of fungicidal likeness were applied to a curated database of about 1.2 million molecules from the ChEMBL database by using an in-house developed Python3 tool: FLiP-Z (Fungicide Likeness Predictor by Zimeck). Roughly 22% of molecules were predicted by positive consensus as fungicidal candidates. A subsequent screening based on Acute Oral Toxicity reduced the set to 295 molecules with low-toxicity and positive fungicide-likeness predictions. Proprietary rights for FLiP-Z are held by Zimeck C.L.; the tool is available by permission. Full article

Small object detection in UAV remote sensing imagery plays a crucial role in applications such as infrastructure inspection, disaster assessment, and precision agriculture, where targets of interest frequently occupy fewer than $32\times 32$ pixels under large ground sampling distance variation and complex cluttered backgrounds. Existing methods still face three main challenges in UAV small-object detection: fine-grained detail loss caused by repeated downsampling, feature inconsistency during cross-scale fusion, and unstable boundary regression in densely distributed aerial scenes. To address these issues, this paper proposes HSAR-DETR, a detection framework that jointly improves hierarchical feature representation, cross-scale refinement, and geometry-aware localization. Specifically, a Hierarchical Enhancement Network (HENet) is introduced to preserve shallow spatial details while strengthening deep semantic-context representation. A Dual-Stream Feature Refinement module (DSFR) is designed at the P4-to-P3 fusion stage, combining spatial-domain structural modeling with frequency-domain phase refinement to improve cross-scale feature consistency. A Coordinate-Guided Adaptive Convolution module (CGAC) is further deployed before the detection head, converting coordinate-guided offset magnitudes into modulation weights for adaptive feature recalibration and improved localization stability. In addition, a conventional high-resolution P2 detection branch is incorporated to enhance small-object representation. Experimental results on the VisDrone, RSOD, and TinyPerson datasets demonstrate improved detection performance. On the VisDrone validation set, HSAR-DETR achieves 50.8% $mA{P}_{50}$ and 31.4% $mA{P}_{50:95}$, outperforming the RT-DETR baseline by 4.2 and 3.0 percentage points, respectively. Full article

Reliable crop-type mapping from satellite image time series is affected by distribution shifts across geographic regions, agricultural years, and heterogeneous label systems. To address this challenge, we propose Cross-Domain Mixup (CDMix), a supervised domain-adaptation method designed to leverage a larger labeled source dataset to improve performance on a smaller labeled target dataset under distribution shifts. We also introduce PixelSet-Slovakia, a new multi-year, parcel-level Sentinel-2 dataset covering three Slovak study regions and several growing seasons. Using a common backbone, we compare CDMix against three families of adaptation strategies: (i) no adaptation, (ii) weight transfer through fine-tuning and encoder freezing, and (iii) feature-space alignment using Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL). All methods are evaluated in two scenarios: geographic supervised adaptation across datasets from two countries and temporal supervised adaptation across different growing seasons. Across both tested source–target settings, CDMix generally achieves competitive performance when initialized from pretrained representations. Under the region-held-out validation protocol, several pretrained adaptation strategies outperform training from scratch. Full article

Image-level monitoring and variety-level detection of three specialty tomato cultivars, Kiss, Millennium, and White Jade, remain challenging in unstructured orchards because of foliage occlusion, overlapping fruit clusters, and variable illumination. Conventional downsampling may weaken fine spatial details of small targets, whereas larger detectors can impose computational demands that are unsuitable for mobile or edge-based agricultural platforms. To address these limitations, we propose WCAF-YOLO, a lightweight two-dimensional tomato detector based on a modified YOLOv26n architecture. The model replaces the P3 backbone downsampling operation with space-to-depth convolution (SPD-Conv) to retain fine-grained spatial information. Its weighted channel-aware fusion (WCAF) neck combines learnable branch weighting with parameter-free three-dimensional attention to refine fused features. Bounding-box regression uses focaler-minimum point distance intersection over union (Focaler-MPDIoU). Across five random seed runs on the internal held-out test subset of a custom single-site orchard dataset, WCAF-YOLO obtained a mean ${\mathrm{mAP}}_{50–95}$ of $0.9048\pm 0.0013$ and a mean recall of $0.9280\pm 0.0019$. The corresponding mean improvements over the YOLOv26n baseline were 2.14 and 3.42 percentage points, respectively. The model contained 2.36 M parameters and required 6.36 GFLOPs. Under the evaluated protocol, the model combined a compact parameter count with higher mean detection metrics than the YOLOv26n baseline. The detector outputs two-dimensional bounding boxes and variety labels for image-level orchard monitoring and variety-level assessment. Integration into agricultural field platforms remains to be validated. Full article

Exploring the spatial correlation network (SCN) characteristics and influencing factors of the eco-efficiency of cultivated land use (ECLU) in Xinjiang’s counties is crucial for clarifying inter-regional spatial mechanisms and supporting cross-regional collaborative governance. Using a panel dataset covering 85 counties across Xinjiang, this study adopts the super-efficiency SBM model, revised gravity model, social network analysis and QAP model to quantify ECLU, and further investigate its spatial network features as well as driving mechanisms. The results showed that: (1) ECLU exhibited a fluctuating upward trend with significant regional differentiation, and northern Xinjiang performed notably better than southern Xinjiang. (2) SCN remained connected overall, but network density was low, average path length was long, and spatial transmission efficiency was relatively low. (3) Regional differences in economic levels, labor productivity, and industrial structure all had positive effects on the formation of the SCN throughout the study period. Regional differences in fiscal support for agriculture had positive effects only in 2014 and 2017, while differences in the soil and water coordination ratio had a negative effect in 2021. Future policies for sustainable cultivated land use should be differentiated and zone-specific, based on each county’s role within the correlation network, to promote coordinated improvement of ECLU across counties. Full article

Background: Seed dormancy is crucial for plant survival and agricultural productivity, yet its molecular mechanisms, particularly the role of maternal effects, remain poorly understood. Methods: In this study, we applied a SWATH-based, label-free, quantitative shotgun proteomic mass spectrometry approach to investigate the temporal dynamics of dormancy establishment in Xanthium strumarium, a wild plant with two seeds in one burr that, despite sharing the same genetic and environmental conditions, exhibit distinct dormancy states. Results: Our data show that dormant seeds undergo coordinated metabolic suppression, marked by a decrease in energy metabolism, cell cycle arrest, and auxin signaling, explaining their smaller size. Simultaneously, dormant seeds exhibit metabolic re-prioritization towards fatty acid desaturation, cell wall modification, and an active epigenetic program stabilized by dormancy-promoting factors alongside a transcriptionally quiescent state in early–mid development. However, in the late developmental stage, molecular signaling pathways showed a recalibration distinguished by changes in seed metabolism (such as carbon–nitrogen reallocation, sulfur assimilation, and GABA production), hormonal fluctuations, and epigenetic regulation. Notably, previously reported high DOG1 transcript abundance, together with the absence of detectable DOG1 protein in the proteomic dataset, suggests that post-transcriptional mechanisms may contribute to DOG1 regulation. Conclusions: Based on these findings and the available literature, we propose a framework whereby dormancy establishment occurs in two phases: an early DOG1-mediated priming phase followed by a temperature-sensitive plasticity phase during seed maturation. Full article

Agricultural nonpoint source pollution mainly causes lake eutrophication in China, largely affected by variations in crop distribution. To analyze the multiscale relationships between the long-term evolution of cropping patterns and lake water quality at the macro scale, this study analyzed nationwide datasets for 2000 and 2020 covering 420 relatively large lakes. We systematically examined the spatial restructuring of six major food and cash crops—wheat, rice, maize, soybean, peanut, and rapeseed—and evaluated their multiscale associations with lake total nitrogen (TN) and total phosphorus (TP) concentrations and how these associations changed over time. The results showed the following: (1) From 2000 to 2020, the spatial distributions of the six major crops underwent substantial restructuring. The dominant production areas of rice, wheat, and maize were maintained or further reinforced, whereas soybean, rapeseed, and peanut exhibited varying degrees of regional redistribution and localized concentration. (2) Lake water quality differed between the flood and non-flood seasons. TN exhibited pronounced seasonal differences between the two study periods, whereas temporal changes in TP were generally limited; both nutrients nevertheless showed marked regional heterogeneity among the five major lake regions. (3) The crop–water quality relationship exhibits significant scale dependence and crop-specific variations. The XGBoost model demonstrated a certain degree of out-of-field (OOF) predictive capability for both TN and TP, with OOF R² values of 0.448 and 0.447, respectively. For TN, the highest OOF R² values were observed in the 1000–2000 m buffer zone in both 2000 and 2020; the optimal prediction scale for TP shifted from 1000–2000 m in 2000 to 2000–5000 m in 2020. SHAP results showed that corn maintained a high and relatively stable predictive importance in the TN model, followed by wheat, peanuts, and rice; in the TP model, corn and rapeseed were the crop predictors with the highest relative SHAP importance. PDP results further indicate that there are generally nonlinear or non-monotonic relationships between different crop coverage proportions and TN and TP. (4) Pronounced spatial heterogeneity was observed across the five lake regions. The Eastern Plain Lake Region was characterized by associations involving multiple crops, whereas maize was the most prominent crop in the Northeast Plain and Mountain Lake Region. In the Inner Mongolia–Xinjiang Plateau Lake Region, maize predominated, with wheat and rapeseed also showing notable importance. In the Tibetan Plateau Lake Region, TN was associated with multiple crops, whereas TP was primarily related to maize and rapeseed. The Yunnan–Guizhou Plateau Lake Region exhibited particularly strong scale-dependent differences. This study provides a nationwide analytical framework for comparing the scale differences and regional variations in the statistical associations between the spatial distribution of crops and lake water quality at the specific crop level. The findings can provide a scientific basis for formulating differentiated agricultural nonpoint source pollution control strategies that are adapted to the evolving characteristics of crop planting structures. Full article

In protected agriculture such as greenhouses, pest and disease monitoring via UAVs and fixed cameras suffers from extremely small object proportions owing to shooting altitude constraints, posing considerable detection challenges. Moreover, fine-grained annotation of numerous small-scale images incurs prohibitive costs. Targeting this bottleneck, this paper proposes a cross-scale unified semantic space learning framework and introduces an end-to-end DS-DETR detector based on DETR. Unlike existing methods relying on domain adaptation, multi-scale fusion, or super-resolution reconstruction, this work explicitly models instance-level cross-scale semantic correlation, transferring fine-grained semantics from large-scale close-up images to small-scale scene feature space. A Single-Point Dual-Shooting (SPDS) strategy is adopted to collect high-fidelity paired images via ordinary smartphones at low cost. A dual-stream encoder with cross-view attention and an instance-level contrastive loss align features of identical instances in a unified semantic space. A self-built CropScale-Det dataset covering three crop diseases is constructed in greenhouse scenarios. Experimental results show that DS-DETR achieves 42.5 ± 1.2% mAP@50 under limited annotations, outperforming YOLOv8-n by 11.2%, with small-target average precision reaching 26.8 ± 1.1%. Ablation experiments and feature visualization validate the effectiveness of the designed mechanism. This approach considerably reduces reliance on large-scale densely annotated data, establishing a data-efficient proof-of-concept for small-scale pest detection in protected agriculture. Full article

The population genetic diversity and demographic history of Hypomeces pulviger in Thailand were examined based on mitochondrial cytochrome c oxidase subunit 1 (CO1) and 16S ribosomal DNA (16S rDNA) sequence data. A total of 171 and 104 individuals from multiple populations were analyzed using CO1 and 16S rDNA sequences, respectively. The CO1 sequence dataset revealed high haplotype diversity (Hd = 0.999) and moderate nucleotide diversity (Nd = 0.0323), whereas the 16S rDNA showed lower diversity (Hd = 0.750, Nd = 0.0026). Population structure analyses showed low to moderate differentiation in CO1 sequences with a significant isolation-by-distance pattern, suggesting distance-limited gene flow, while 16S rDNA sequences showed weaker structure. Neutrality tests and mismatch distribution analyses supported a recent population expansion, as indicated by significantly negative Fu’s Fs and a unimodal distribution. Haplotype network and phylogenetic analyses further demonstrated greater resolution in the CO1 gene compared to the 16S rRNA gene. Collectively, H. pulviger populations in Thailand are genetically diverse, connected, and expanding, likely facilitated by both natural dispersal and agricultural activities. These findings provide important insights for understanding pest dynamics and developing effective management strategies. Full article

The East Kazakhstan Region is characterized by heterogeneous mountain, steppe, agricultural, and urban–industrial landscapes. However, spatially integrated assessments of land-cover conditions, agricultural dynamics, settlement expansion, and atmospheric trace-gas patterns remain limited. This study combines open multi-source Earth observation datasets and products to assess regional land cover, cropland dynamics from 2003 to 2019, built-up-area expansion from 1985 to 2025, and tropospheric nitrogen dioxide (NO₂) column density from 2019 to 2024. The land-cover classification was based on Sentinel-2 imagery and ancillary geospatial datasets. Its random forest component achieved an overall accuracy of 90.17%, a Cohen’s kappa coefficient of 0.892, and a macro-averaged F1 score of 0.888. Grassland was the largest mapped land-cover class, covering 39.3% of the study region, followed by dense vegetation (16.2%), rock (14.9%), and sparse vegetation (10.6%). Mapped cropland extent increased from 4771.6 km² in 2003 to 5043.9 km² in 2019, representing a net increase of 272.3 km² (5.7%), although a minor decrease occurred after 2015. The harmonized built-up area time series showed expansion within all nine major settlements, with the largest absolute increase observed in Öskemen (Ust-Kamenogorsk). Newly detected built-up pixels during 2000–2024 were mostly associated with transition from grassland and dense shrubs. Annual Sentinel-5P observations showed recurring elevated tropospheric NO₂ column densities in northwestern East Kazakhstan, particularly around Öskemen. Over the built-up footprint, the area-weighted mean increased from 24.1 µmol m⁻² in 2019 to 29.2 µmol m⁻² in 2024. The integrated framework provides a spatially consistent regional baseline while identifying descriptive patterns that require further process-based investigation in future. Full article

In karst regions, sugarcane mapping faces challenges from fragmented fields, undulating terrain, spectral confusion, and persistent cloud cover, which limit traditional optical remote sensing. To address these issues, we propose a fine-scale extraction framework that integrates Sentinel-2 optical and Sentinel-1 synthetic aperture radar (SAR) imagery through image-level fusion, and introduces a UNet-DFH network with a Multi-Scale Edge Fusion (MSEF) module and an Attention-Deformable Fusion Module (ADFM). This study makes three core contributions: (1) we construct a dedicated optical–SAR collaborative sugarcane extraction dataset for typical karst regions, alleviating the scarcity of multimodal labeled samples; (2) we propose the UNet-DFH network, where MSEF enhances boundary preservation and topological detail in shallow decoding stages, while ADFM improves robustness to geometric deformation and local misalignment in deep semantic stages; (3) we demonstrate that the joint mechanism of edge-preserving filtering and deformable adaptation yields a synergistic effect in addressing the precision–recall trade-off. Experiments in a typical karst area of Guangxi, China, demonstrate that optical–SAR fusion achieves an IoU of 80.08% and an OA of 92.09% during the sugar accumulation and maturity stage. During the more challenging tillering stage, UNet-DFH maintains relatively stable performance under optical-only conditions, with an IoU of 72.98%, Recall of 82.78%, and OA of 92.12%. Moreover, optical–SAR fusion improves Recall by 5.5 percentage points over optical-only inputs (from 83.54% to 89.04%), while Precision exhibits a moderate decrease from 91.89% to 88.84%, reflecting the expected trade-off associated with speckle noise. These results confirm the complementary value of multimodal data and the effectiveness of the proposed modules in preserving fragmented plot boundaries and improving segmentation performance in complex karst terrain. The framework offers a promising approach for high-precision crop mapping in the studied karst agricultural landscape. Full article

Although drought is recognized as one of the major hydroclimatic hazards affecting the Mediterranean Basin, local-scale assessments to support planning for its agricultural, hydrological, and food security impacts remain limited. This study investigates historical and future drought dynamics in Manisa (Western Türkiye) using a multi-scale framework integrating the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) with CMIP6 climate projections. A comprehensive dataset including ERA5-Land, CHIRPS, TerraClimate, and station observations was evaluated, revealing strong agreement in temperature but higher uncertainty in precipitation. SPI results indicate recurrent short-term droughts with extreme events reaching −2.5 in 2007–2008, alongside intensified long-term hydrological droughts since the early 1990s, while SPEI reveals a stronger temperature-driven drought signal, peaking near −2.0 during 2020–2024 as the most severe cumulative drought period in the past 40 years. Rapid wet–dry transitions have increased since 2000, indicating a more unstable hydroclimatic regime. Future projections (2025–2100) under a high-emission scenario suggest a progressive intensification of thermally driven drought conditions, with SPEI trends declining more steeply than SPI, indicating that evapotranspiration-driven water deficits may increasingly outweigh precipitation deficits as a driver of future drought risk. These findings offer localized, data-driven evidence relevant to climate adaptation and water resource planning in semi-arid Mediterranean agricultural regions. Full article

Accurate localization of bounding boxes is a prerequisite for enabling vision-driven precision agriculture pipelines, as downstream tasks such as morphological feature extraction, growth monitoring, and digital-twin synchronization depend directly on the geometric quality of the detected boxes. Distance-IoU (DIoU) and Complete-IoU (CIoU) improve upon simple overlap-based objectives by incorporating a normalized center-distance term into the regression loss, along with the overlap and, for CIoU, an aspect-ratio penalty, but that term remains embedded in a single composite formulation with an implicit, non-adjustable weight. We propose a Directional Alignment Penalty (DAP), an auxiliary localization regularizer that introduces an independently weighted normalized center-displacement term into the bounding-box regression objective without modifying the detector architecture. The proposed DAP-YOLOv8 increased mAP@0.5:0.95 from 51.69% to 53.76% and mAP@0.5 from 80.03% to 80.36% while preserving precision and recall; repeated-seed experiments further showed that the improvement in fine-grained localization was consistent across random initializations on a purpose-built oyster-mushroom (Pleurotus ostreatus) dataset collected from a real-world smart greenhouse. Results show that explicitly modeling the center-distance factor as an independent and tunable component can improve fine-grained localization without sacrificing the computational efficiency of the base detector, thereby providing a lightweight plug-in extension for agricultural detection and digital-twin applications. Full article

Seed germination and early survival are important phenotypes for plant breeding and agricultural management, yet they are still commonly assessed through labor-intensive manual stand counting. We present the Maize Seedling Detection Dataset (MSDD), a curated high-resolution red–green–blue (RGB) dataset derived from unmanned aerial vehicle (UAV) imagery collected over the 2019–2022 growing seasons. MSDD contains 3152 images and 163,921 annotated objects across three classes—single (92.47%), double (6.07%), and triple (1.45%) clusters of seedlings—and captures substantial variability in growth stage (V2–V12), illumination, soil appearance, wind, and camera viewpoint. Unlike many existing datasets, MSDD explicitly annotates clustered seedlings as double and triple classes, which are important for stand evaluation. We benchmarked YOLOv9, YOLO11, YOLOv12, and Faster-RCNN on MSDD to evaluate detection accuracy, class-specific performance, inference efficiency, and generalization across field conditions. Single-seedling detection was reliable across models, with the best mean average precision at 0.5 IoU (mAP@0.5) reaching 0.916, whereas double- and triple-seedling detection remained challenging because of class imbalance, occlusion, and annotation ambiguity. Detection was most reliable in high-contrast scenes and declined under wind, strong shadows, and bright soil backgrounds. YOLO11 provided the fastest evaluation throughput among the tested models (≈27 frames per second (fps)), while YOLOv9 achieved the strongest single-seedling detection performance. Synthetic augmentation improved class balance but did not improve generalization to naturally occurring clustered seedlings. Frames, labels, and trained models are available at Google Drive and Hugging Face. MSDD provides a public benchmark for maize seedling detection and for evaluating stand counting models under realistic field conditions. Full article

Heavy metal deposition along high-traffic roadsides poses a persistent threat to agricultural safety, yet the partition barrier efficiency across rhizosphere–root–shoot interfaces in perennial nut crops remains poorly understood, representing a significant research gap. This study determined the concentrations of potentially toxic elements in the rhizosphere soils and distinct organs (leaves, pericarp, and edible seeds) of Siirt pistachio trees along a distance gradient (0, 50, and 100 m, plus a control site) in the Siirt and Tillo districts. To filter analytical baseline noise, all raw datasets were subjected to strict solid-matrix limit of detection (LOD) screening using a standardized dilution factor of 30 mL/g (DF = 15 mL final volume/0.5 g sample mass). Soil analysis revealed that the alkaline pH (6.90–7.27) and highly calcareous nature (21.97–65.75%) of the rhizosphere acted as a powerful edaphic barrier, immobilizing metals in the soil and limiting their translocation to aboveground tissues. Plant accumulation followed a leaf > pericarp > seed hierarchy, proving the canopy’s role as an effective vegetative filter. Crucially for food safety, highly toxic Cd (<1.74 µg/kg) and Bi remained entirely below detection limits in edible seeds. Cr peaked in leaves (730.42–795.00 µg/kg) but was highly restricted in seeds. Detected kernel concentrations of As, Co, Ni, Pb, and Sb were strictly below international toxic thresholds, while essential Cu physiologically concentrated in seeds and leaves. Consequently, the cumulative Hazard Index (HI) remained exceptionally below the 1.0 critical safety limit for both adults (<0.18) and children (<0.32). This confirms that roadside pistachios pose zero non-carcinogenic health hazards and are completely safe for human consumption. Full article