Search Results (305)

Accurate detection of maize tassels plays a crucial role in yield estimation of maize in precision agriculture. Recently, UAV and deep learning technologies have been widely introduced in various applications of field monitoring. However, complex field backgrounds pose multiple challenges against the precision detection of maize tassels, including maize tassel multi-scale variations caused by varietal differences and growth stage variations, intra-class occlusion, and background interference. To achieve accurate maize tassel detection in UAV images under complex field backgrounds, this study proposes an MSMT-RTDETR detection model. The Faster-RPE Block is first designed to enhance multi-scale feature extraction while reducing model Params and FLOPs. To improve detection performance for multi-scale targets in complex field backgrounds, a Dynamic Cross-Scale Feature Fusion Module (Dy-CCFM) is constructed by upgrading the CCFM through dynamic sampling strategies and multi-branch architecture. Furthermore, the MPCC3 module is built via re-parameterization methods, and further strengthens cross-channel information extraction capability and model stability to deal with intra-class occlusion. Experimental results on the MTDC-UAV dataset demonstrate that the MSMT-RTDETR significantly outperforms the baseline in detecting maize tassels under complex field backgrounds, where a precision of 84.2% was achieved. Compared with Deformable DETR and YOLOv10m, improvements of 2.8% and 2.0% were achieved, respectively, in the mAP₅₀ for UAV images. This study proposes an innovative solution for accurate maize tassel detection, establishing a reliable technical foundation for maize yield estimation. Full article

Chaperonin 60 proteins plays an important role in plant growth and development as well as the response to abiotic stress. As part of the protein homeostasis system, molecular chaperones have attracted increasing attention in recent years due to their involvement in the folding and assembly of key proteins in photosynthesis. However, little is known about the function of maize chaperonin 60 protein. In the study, a gene encoding the chaperonin 60 proteins was cloned from the maize inbred line B73, and named ZmCpn60-3. The gene was 1, 818 bp in length and encoded a protein consisting of 605 amino acids. Phylogenetic analysis showed that ZmCpn60-3 had high similarity with OsCPN60-1, belonging to the β subunits of the chloroplast chaperonin 60 protein family, and it was predicted to be localized in chloroplasts. The ZmCpn60-3 was highly expressed in the stems and tassels of maize, and could be induced by exogenous plant hormones, mycotoxins, and pathogens; Overexpression of ZmCpn60-3 in Arabidopsis improved the resistance to Pst DC3000 by inducing the hypersensitive response and the expression of SA signaling-related genes, and the H₂O₂ and the SA contents of ZmCpn60-3-overexpressing Arabidopsis infected with Pst DC3000 accumulated significantly when compared to the wild-type controls. Experimental data demonstrate that flg22 treatment significantly upregulated transcriptional levels of the PR1 defense gene in ZmCpn60-3-transfected maize protoplasts. Notably, the enhanced resistance phenotype against Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) in ZmCpn60-3-overexpressing transgenic lines was specifically abolished by pretreatment with ABT, a salicylic acid (SA) biosynthetic inhibitor. Our integrated findings reveal that this chaperonin protein orchestrates plant immune responses through a dual mechanism: triggering a reactive oxygen species (ROS) burst while simultaneously activating SA-mediated signaling cascades, thereby synergistically enhancing host disease resistance. Additionally, yeast two-hybrid assay preliminary data indicated that ZmCpn60-3 might bind to ZmbHLH118 and ZmBURP7, indicating ZmCpn60-3 might be involved in plant abiotic responses. The results provided a reference for comprehensively understanding the resistance mechanism of ZmCpn60-3 in plant responses to abiotic or biotic stress. Full article

Drought severely limits crop yield every year, making it critical to clarify the genetic basis of drought tolerance for breeding of improved varieties. As drought tolerance is a complex quantitative trait, we analyzed three phenotypic groups: (1) agronomic traits under well-watered (WW) and water-deficit (WD) conditions, (2) stress tolerance indices of these traits, and (3) phenotypic plasticity, using a multi-parent doubled haploid (DH) population assessed in multi-environment trials. Genome-wide association studies (GWAS) identified 130, 171, and 71 quantitative trait loci (QTL) for the three groups of phenotypes, respectively. Only one QTL was shared among all trait groups, 25 between stress indices and agronomic traits, while the majority of QTL were specific to their group. Functional annotation of candidate genes revealed distinct pathways of the three phenotypic groups. Candidate genes under WD conditions were enriched for stress response and epigenetic regulation, while under WW conditions for protein synthesis and transport, RNA metabolism, and developmental regulation. Stress tolerance indices were enriched for transport of amino/organic acids, epigenetic regulation, and stress response, whereas plasticity showed enrichment for environmental adaptability. Transcriptome analysis of 26 potential candidate genes showed tissue-specific drought responses in leaves, ears, and tassels. Collectively, these results indicated both shared and independent genetic mechanisms underlying drought tolerance, providing novel insights into the complex phenotypes related to drought tolerance and guiding further strategies for molecular breeding in maize. Full article

Cardiovascular injury caused by fine particulate matter (PM2.5) exposure is an escalating public health concern due to its role in triggering systemic inflammation and oxidative stress. This study elucidates the sirtuin 1 (SIRT1)-mediated epigenetic mechanisms underlying the protective effects of phytosome-encapsulated Zea mays L. var. ceratina tassel extract (PZT) in a rat model of PM2.5-induced cardiovascular inflammation. Male Wistar rats were pretreated with PZT (100, 200, and 400 mg/kg body weight) for 21 days before and throughout a 27-day PM2.5 exposure period. SIRT1 expression and associated inflammatory and oxidative stress markers were evaluated in cardiac and vascular tissues. The findings revealed that PZT significantly upregulated SIRT1 expression, a key epigenetic regulator known to modulate inflammatory and antioxidant pathways. The activation of SIRT1 inhibited the nuclear factor-kappa B (NF-κB) signaling pathway, leading to a reduction in pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) within cardiac tissue. In vascular tissue, treatment with PZT reduced the levels of tumor necrosis factor-alpha (TNF-α) and transforming growth factor-beta (TGF-β), thereby mitigating inflammatory and fibrotic responses. Furthermore, SIRT1 activation by PZT enhanced the antioxidant defense system by upregulating superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px), which was associated with a decrease in malondialdehyde (MDA), a marker of lipid peroxidation. Collectively, these results demonstrate that PZT confers cardiovascular protection through SIRT1-dependent epigenetic modulation, mitigating PM2.5-induced inflammation, oxidative stress, and tissue remodeling. The dual anti-inflammatory and antioxidant actions of PZT via SIRT1 activation highlight its potential as a functional food-based preventative agent for reducing cardiovascular risk in polluted environments. Full article

Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R² values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R² values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R² values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making. Full article

Maize tassels are critical phenotypic organs in maize, and their quantity is essential for determining tasseling stages, estimating yield potential, monitoring growth status, and supporting crop breeding programs. However, tassel identification in complex field environments presents significant challenges due to occlusion, variable lighting conditions, multi-scale target complexities, and the asynchronous and irregular growth patterns characteristic of maize tassels. In response to these challenges, this paper presents a DMSF-YOLO model for maize tassel detection. In the network’s backbone front, conventional convolutions are replaced with conditional parameter convolutions (CondConv) to enhance feature extraction capabilities. A novel DMSF-P2 network architecture is designed, including a multi-scale fusion module (SSFF-D), a scale-splicing module (TFE), and a small object detection layer (P2), which further enhances the model’s feature fusion capabilities. By integrating a dynamic detection head (Dyhead), superior recognition accuracy for maize tassels across various scales is achieved. Additionally, the Wise-IoU loss function is used to improve localization precision and strengthen the model’s adaptability. Experimental results demonstrate that on our self-built maize tassel detection dataset, the proposed DMSF-YOLO model shows remarkable superiority compared with the baseline YOLOv8n model, with precision (P), recall (R), $mA{P}_{50}$, and $mA{P}_{50:95}$ increasing by 0.5%, 3.4%, 2.4%, and 3.9%, respectively. This approach enables accurate and reliable maize tassel detection in complex field environments, providing effective technical support for precision field management of maize crops. Full article

Background and Clinical Significance: Proximal femoral focal deficiency (PFFD), also referred to as congenital femoral deficiency, is a longitudinal limb deficiency and birth defect that affects the lower extremity including the hip and femur, resulting in a deformed and shortened limb. It can be diagnosed and classified using a combination of imaging modalities, including radiographs, ultrasonography, magnetic resonance imaging and computerized tomography. It is crucial to characterize this birth defect in the prenatal period to appropriately prepare parents through counseling. Postnatal imaging should be performed to confirm the diagnosis, prognosticate and predict the patient’s course for treatment and management. Close follow-up and family/patient-centered care contribute to optimized patient outcomes. Case Presentation: Here, we present a series of three cases of varying PFFD severity and presentation, detailing the evaluation process, the limitations and value of imaging, and the treatment outcomes of these patients. Each case has a different PFFD classification and treatment strategy that we utilized according to the data that we attained through continuous patient care and discussion. Conclusions: We highlight the difficulties in identifying and classifying PFFD in the prenatal period while demonstrating how postnatal imaging clarified the diagnosis and informed appropriate counseling and treatment. Close follow-up and the length of patient continuity allowed us to maximize patient outcomes despite the variety in PFFD presentation and severity. Full article

Seasonally dynamic plant communities present challenges for remote mapping, but estimating phenology can help identify periods of peak spectral distinction. While phenology is widely used in environmental and agricultural mapping, its broader ecological applications remain underexplored. Using a temperate wetland complex as a case study, we leveraged NDVI time series from Sentinel imagery to refine a wetland classification scheme by identifying periods of maximum plant community distinction. We estimated plant phenology with ground-reference points and mapped the study area using Random Forest (RF) with both Sentinel and PlanetScope imagery. Most plant communities showed distinct phenological variations between April–June (growing season) and September–October (transitional season). Merging phenologically similar communities improved classification accuracy, with April and September imagery yielding better results than the peak summer months. Combining both seasons achieved the highest classification accuracy (~77%), with key RF predictors including digital elevation, and near-infrared and tasseled cap indices. Despite its higher spatial resolution, PlanetScope underperformed compared to Sentinel, as spectral similarities between plant communities limited classification accuracy. While Sentinel provides valuable data, higher spectral resolution is needed for distinguishing similar plant communities. Integrating phenology into mapping frameworks can improve the detection of rare and ephemeral vegetation, aiding conservation efforts. Full article

Accurately extracting the phenology of maize, one of the three major staple crops, is crucial for assessing regional suitability under climate change, optimizing field management, predicting yield fluctuations, and ensuring food security. This study compares and validates the accuracy of various vegetation indices, including the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), solar-induced chlorophyll fluorescence (SIF), and kernel NDVI (kNDVI), in extracting the phenological phases of summer maize at the sixth leaf (V6), tasseling (VT), and maturity (R6). Additionally, explainable machine learning methods were employed to elucidate how climate and stress factors influence the phenological sequences of summer maize. The results show that compared to NDVI and EVI, SIF and kNDVI are more suitable for extracting the summer maize phenological phase. SIF achieved the highest phenological extraction precision at the V6 and R6 phases, with root mean square errors (RMSEs) of 7.86 and 8.22 days, respectively. kNDVI provided the highest extraction accuracy for the VT phase, with an RMSE of 5 days. SHapley Additive exPlanations (SHAP) analysis revealed that temperature and radiation are the primary meteorological factors influencing maize phenology in the study area. Regarding stress factors, drought and heat stress delayed phenology at the V6 and VT phases, while heat stress prior to maturity accelerated summer maize maturation. In conclusion, this study reveals the potential of emerging vegetation indices for extracting maize phenology, offering both data and theoretical support for regional crop adaptability assessments. Full article

The occurrence of hurricanes in the southern U.S. is on the rise, and assessing the damage caused to forests is essential for implementing protective measures and comprehending recovery dynamics. This work aims to create a novel data integration framework that employs LANDSAT 8, drone-based images, and geographic information system data for change detection analysis for different forest types. We propose a method for change vector analysis based on a unique spectral mixture model utilizing composite spectral indices along with univariate difference imaging to create a change detection map illustrating disturbances in the areas of McDowell County in western North Carolina impacted by Hurricane Helene. The spectral indices included near-infrared-to-red ratios, a normalized difference vegetation index, Tasseled Cap indices, and a soil-adjusted vegetation index. In addition to the satellite imagery, the ground truth data of forest damage were also collected through the field investigation and interpretation of post-Helene drone images. Accuracy assessment was conducted with geographic information system (GIS) data and maps from the National Land Cover Database. Accuracy assessment was carried out using metrics such as overall accuracy, precision, recall, F score, Jaccard similarity, and kappa statistics. The proposed composite method performed well with overall accuracy and Jaccard similarity values of 73.80% and 0.6042, respectively. The results exhibit a reasonable correlation with GIS data and can be employed to assess damage severity. Full article

Maize is vital for global and Chinese food security. Yet, in Henan Province, a key maize-growing region in China, water scarcity, uneven rainfall, and inefficient irrigation and fertilization limit its yield and quality. This study combines a two-year field experiment (2023–2024) with the DSSAT model to optimize irrigation and fertilization for typical hydrological years (wet, normal, and dry). After calibration and validation with field data, the DSSAT model showed strong performance. Results indicate that optimal irrigation timing and volume vary with hydrological years: no irrigation is needed in wet years, one 30 mm irrigation at the tasseling (VT) stage in normal years, and three irrigations (total 90 mm) at the emergence (VE), jointing (VT), and grain filling (R2) stages in dry years. The optimal nitrogen fertilizer is 240 kg·ha⁻¹ in water-rich and normal years and 180 kg·ha⁻¹ in dry years. These optimized schemes can achieve 98–100% of maximum potential maize yields across hydrological years, offering practical insights for enhancing agricultural water and nutrient management in central Henan to support sustainable development and reduce environmental impacts. Full article

Tropical forests provide essential ecosystem services, playing a critical role in climate regulation, biodiversity conservation, and regional hydrological cycles while also supporting livelihoods. However, they are increasingly threatened by deforestation and land-use change. Accurate land cover (LC) mapping is vital to monitor these changes, but mapping tropical forests is challenging due to complex spatial patterns, spectral similarities, and frequent cloud cover. This study aims to improve LC classification accuracy in such a heterogeneous tropical forest region in Southeast Asia, namely Kulen, Cambodia, which is characterized by natural forests, regrowth forests, and agricultural lands including cashew plantations and croplands, using Sentinel-2 imagery, recursive feature elimination (RFE), and Random Forest. We generated 65 variables of spectral bands, indices, bi-seasonal differences, and topographic data from Sentinel-2 Level-2A and Shuttle Radar Topography Mission datasets. These variables were extracted from 1000 random points per 12 LC classes from reference polygons based on observed GPS points, Uncrewed Aerial Vehicle imagery, and high-resolution satellite data. The random forest models were optimized through correlation-based filtering and recursive feature elimination with hyperparameter tuning to improve classification accuracy, validated via confusion matrices and comparisons with global and national-scale products. Our results highlight the significant role of topographic variables such as elevation and slope, along with red-edge spectral bands and spectral indices related to tillage, leaf water content, greenness, chlorophyll, and tasseled cap transformation for tropical land cover mapping. The integration of bi-seasonal datasets improved classification accuracy, particularly for challenging classes like semi-evergreen and deciduous forests. Furthermore, correlation-based filtering and recursive feature elimination reduced the variable set from 65 to 19, improving model efficiency without sacrificing accuracy. Combining these variable selection methods with hyperparameter tuning optimized the classification, providing a more reliable LC product that outperforms existing LC products and proves valuable for deforestation monitoring, forest management, biodiversity conservation, and land use studies. Full article

Aboveground biomass (AGB) serves as a crucial indicator of the effectiveness of the Grain for Green Program (GGP), and its accurate estimation is essential for evaluating forest health and carbon sink capacity. However, due to the dominance of sparse forests in GGP stands, research in this area remains significantly limited. In this study, we developed the optimal tree height-diameter at breast height (DBH) growth models for major tree species and constructed a high-quality AGB sample dataset by integrating airborne LiDAR data and tree species information. Then, the AGB of the GGP stands was estimated using the Sentinel-2 data and the gradient boosting decision tree (GBDT) algorithm. The results showed that the AGB sample dataset constructed using the proposed approach exhibited strong consistency with field-measured data (R² = 0.89). The GBDT-based AGB estimation model shows high accuracy, with an R² of 0.96 and a root mean square error (RMSE) of 560 g/m². Key variables such as tasseled cap greenness (TCG), red-edge normalized difference vegetation index (RENDVI), and visible-band difference vegetation index (VDVI) were identified as highly important. This highlights that vegetation indices and tasseled cap transformation index information are key factors in estimating AGB. The AGB of major tree species in the new round of the GGP stands in Inner Mongolia ranged from 120 to 9253 g/m², with mean values of 978 g/m² for poplar, 622 g/m² for Mongolian Scots pine, and 313 g/m² for Chinese red pine species. This study offers a practical method for AGB estimation in GGP stands, contributing significantly to sustainable forest management and ecological conservation efforts. Full article

This research investigates the influence of water–nitrogen coupling on soil water content, nitrogen dynamics, and root distribution in farmland, along with the interactions among soil water, nitrogen transport, root distribution, and crop yield. A field experiment was conducted under moderate drought stress (50–60% of field capacity) and three nitrogen application rates (100, 200, and 300 kg·ha⁻¹, split-applied at 50% during sowing and 50% at the jointing stage, labeled as N₁, N₂, and N₃) at the two critical growth stages (jointing stage P₁ and tasseling-silking stage P₂) of maize (Denghai 605). The results demonstrated that maize root morphological parameters exhibited the trend N₂ > N₁ > N₃ under different nitrogen treatments. Compared to N₂, low nitrogen (N₁) decreased root morphological parameters by 35.01–49.60% on average, whereas high nitrogen (N₃) led to a reduction of 49.93–61.37%. The N₂ treatment consistently maintained greater water uptake, with the highest yield of 13,336 kg·ha⁻¹ observed under the CKN₂ treatment, representing increases of 16.1% and 9.2% compared to the P₁N₂ and P₂N₂ treatments, respectively. Drought stress at the jointing stage (P₁) inhibited root development more severely than at the tasseling-silking stage (P₂), demonstrating a bidirectional adaptation strategy characterized by deeper vertical penetration under water stress and increased horizontal expansion under nitrogen imbalance. Correlation analysis revealed a positive correlation between soil nutrient content and maize yield indicators. At the same time, root characteristic values were significantly negatively correlated with yield (p < 0.05). Appropriate water–nitrogen management effectively stimulated root growth, mitigated nitrogen leaching risks, and improved yield. These findings offer a theoretical foundation for optimizing water and nitrogen management in maize production within the Yellow River Basin. Full article

In Northeast China, waterlogging has emerged as a significant challenge due to climate change, particularly during the June–August period when spring maize (Zea mays L.), at the post-tasseling phase, impedes a comprehensive understanding of responses and the development of resistance technologies. 2-(3,4-dichlorophenoxy) triethylamine (DCPTA) is suitable for the entire lifecycle of various economic and food crops, improving crop quality and enhancing stress resistance. The study investigated the ear leaf photosynthesis in relation to the root antioxidant systems’ differential responses of spring maize to waterlogging among the tasseling (VT), vesicle (R2) and dough (R4) stages, and the exogenous DCPTA regulating effect. Results revealed that waterlogging inhibited root physiological activity due to oxidative damage. Consequently, the stomatal restriction and non-stomatal restriction on photosynthesis appeared successively, and R4 was the most sensitive stage. Pretreatment with DCPTA reduced stomatal restriction by maintaining water transfer to the leaf through maintaining root physiological activity via enhanced ascorbate–glutathione cycle. Delayed non-stomatal restriction appeared due to relatively stable chlorophyll content and photosynthetic activities, and VT stage exhibited the highest susceptibility to DCPTA. The study provides a necessary theoretical foundation for comprehending the physiological mechanisms underlying yield formation of spring maize under waterlogging stress in Northeast China, and offers valuable insights for the development of chemical regulation technology. Full article