Search

Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional methods for stress identification in plants are often laborious and time-intensive, prompting the exploration of more efficient approaches. A total of six classic convolutional neural network (CNN) models—AlexNet, GoogLeNet, ResNet50, ShuffleNet, SqueezeNet, and MobileNetv2—are evaluated for salt stress recognition based on three types of ChlF images. Results indicate that ResNet50 outperforms other models in classifying salt stress levels across three types of ChlF images. Furthermore, feature fusion after extracting three types of ChlF image features in the average pooling layer of ResNet50 significantly enhanced classification accuracy, achieving the highest accuracy of 98.61% in particular when fusing features from three types of ChlF images. UMAP dimensionality reduction analysis confirms the discriminative power of fused features in distinguishing salt stress levels. These findings underscore the efficacy of deep learning and ChlF imaging technologies in elucidating plant responses to salt stress, offering insights for precision agriculture and crop management. Overall, this study demonstrates the potential of integrating deep learning with ChlF imaging for precise and efficient crop stress detection, offering a robust tool for advancing precision agriculture. The findings contribute to enhancing agricultural sustainability and addressing global food security challenges by enabling more effective crop stress management. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

The challenge of analyzing microarray datasets is significantly compounded by the curse of dimensionality and the complexity of feature interactions. Addressing this, we propose a novel feature selection algorithm based on maximum conditional mutual information (MCMI) to identify a minimal feature subset that is maximally relevant and non-redundant. This algorithm leverages a greedy search strategy, prioritizing both feature quality and classification performance. Experimental results on high-dimensional microarray datasets demonstrate our algorithm's superior ability to reduce dimensionality, eliminate redundancy, and enhance classification accuracy. Compared to existing filter feature selection methods, our approach exhibits higher adaptability and intelligence. [ABSTRACT FROM AUTHOR]

Interconnecting multiple microgrids (MGs) to form a MG cluster (MGC) is significant to improve the MG power supply reliability and increase the new energy accommodation capacity. However, for the traditional MGC in complex operational conditions, the following technologies need to be further researched, such as mutual power support among MGs, voltage support and power quality improvement and so on. Focusing on the above problems, a novel virtual synchronous port control (VSPC) strategy for MGC is proposed. Firstly, a multi‐port soft open point (SOP) is used to interconnect each sub‐MG in the MGC, to achieve a better flexible interconnection among sub‐MGs. Secondly, on this basis, the VSPC strategy is proposed. By adopting the DC voltage control of multi‐port SOP and the virtual synchronisation control of interconnecting ports, multiple voltage source converters (VSCs) are cluster controlled to provide the sub‐MGs with superordinate ports with virtual synchronisation characteristics, which ensures the dynamic balance of the power inside the MGC and achieves better voltage and frequency support capability. Finally, the example results verify the feasibility and effectiveness of the proposed VSPC strategy. [ABSTRACT FROM AUTHOR]

Urban tailwater typically has a low carbon-to-nitrogen ratio and adding external carbon sources can effectively improve the denitrification performance of wastewater. However, it is difficult to determine the dosage of additional carbon sources, leading to insufficient or excessive addition. Therefore, it is necessary to prepare solid slow-release carbon source (SRC) materials to solve the difficulty in determining the dosage of carbon sources. This study selected two SRCs of slow-release carbon source 1 (SRC1) and slow-release carbon source 2 (SRC2), with good slow-release performance after static carbon release and batch experiments. The composition of SRC1 was: hydroxypropyl methylcellulose/disodium fumarate/polyhydroxy alkanoate (HPMC/DF/PHA) at a ratio of 3:2:4, with an Fe3O4 mass fraction of 3%. The composition of SRC2 was: HPMC/DF/PHA with a ratio of 1:1:1 and an Fe3O4 mass fraction of 3%. The fitted equations of carbon release curves of SRC1 and SRC2 were y = 61.91 + 7190.24e−0.37t and y = 47.92 + 8770.42e−0.43t, respectively. The surfaces of SRC1 and SRC2 had a loose and porous morphological structure, which could increase the specific surface area of materials and be more conducive to the adhesion and metabolism of microorganisms. The experimental nitrogen removal by denitrification with SRCs showed that when the initial total nitrogen concentration was 40.00 mg/L, the nitrate nitrogen (NO3−-N) concentrations of the SRC1 and SRC2 groups on the 10th day were 2.57 and 2.66 mg/L, respectively. On the 20th day, the NO3−-N concentrations of the SRC1 and SRC2 groups were 1.67 and 2.16 mg/L, respectively, corresponding to removal efficiencies of 95.83% and 94.60%, respectively. The experimental results indicated that SRCs had a good nitrogen removal effect. Developing these kinds of materials can provide a feasible way to overcome the difficulty in determining the dosage of carbon sources in the process of heterotrophic denitrification. [ABSTRACT FROM AUTHOR]

Multisource remote sensing images have rich features and high interpretability and are widely employed in many applications. However, highly unbalanced category distributions and complex backgrounds have created some difficulties in the application of remote sensing image semantic segmentation tasks, such as low accuracy of small target segmentation and inaccurate edge extraction. To solve these problems, in this paper, a feature map segmentation reconstruction module and an attention upsampling module are proposed. In the encoder part, the input feature map is equally segmented, and the segmented feature map is enlarged to effectively improve the small target feature information expression ability in the model. In the decoder part, the key segmentation and location information of shallow features are obtained using the global view. The deep semantic information and shallow spatial location information are fully combined to achieve a more refined upsampling operation. In addition, the attention mechanism of the spatial and channel squeeze and excitation block (scSE) is applied to pay more attention to important features and to suppress irrelevant background and redundant information. To verify the effectiveness of the proposed method, the WHU-OPT-SAR dataset and six state-of-the-art algorithms are utilized in comparative experiments. The experimental results show that our model has demonstrated the best performance and low computational complexity. With only approximately half the floating-point operation count and the number of model parameters of the MCANet model, which is specially designed for the dataset, our model surpasses MCANet by 1.52% and 1.53% in terms of mean intersection over union (mIoU) and F1 score, respectively. In particular, for small object regions such as roads and other categories, compared to the baseline model, the IoU and F1 score of our model are improved by 5.27% and 3.99% and by 5.68% and 5.65%, respectively. These results demonstrate the superior performance of our model in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Monitoring crop phenology is essential for managing field disasters, protecting the environment, and making decisions about agricultural productivity. Because of its high timeliness, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenology estimation. Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The observation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (POLY), machine learning methods can automatically learn features and handle complex data structures, offering greater flexibility and generalization capabilities. Therefore, incorporating two ensemble learning algorithms consisting of support vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, PF-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR data in 2017 covering rice fields in Sevilla region in Spain was used for establishing the observation and prediction equations, and the other year of data in 2018 was used for validating the prediction accuracy of PF methods. Four polarization features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as the observations in modeling. Experimental results reveals that the machine learning-aided methods are superior than the PF-POLY method. The PF-SVR exhibited better performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF-SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RFR and 9.1 for PF-POLY. Moreover, the results suggest that the RVI is generally more sensitive than other features to crop phenology and the performance of polarization features presented consistent among all methods, i.e., RVI＞VV＞VH＞VH/VV. Our findings offer valuable references for real-time crop phenology monitoring with SAR data. [ABSTRACT FROM AUTHOR]

The present study was performed to investigate the effects of dietary supplementation with Lactobacillus plantarum (CGMCC1.557) on egg production and fecal microbiota composition in laying hens. Sixty Hy-Line Brown laying hens (18 weeks old) were randomly divided into two groups. The control group was fed a basal diet only, and the test group was fed basal diet supplemented with a final concentration of 1.0 × 109 CFU/mL during the 10-week experimental period. Egg production and fecal microbiota composition were both assessed in 28-week-old hens using high-throughput sequencing technology. The results showed that, compared with the control group, the test group exhibited increased laying and feed intake rates (p < 0.05). At the genus level, Lactobacillus was more abundant in the test group compared with the control group (p < 0.05). Conversely, Romboutsia was more abundant in the control group compared with the test group (p < 0.05). This study provides us with an insight into the potential use of L. plantarum as a food supplement in the laying hen industry. the study also provides us with a better understanding of the interplay between L. plantarum and the fecal microbiota of laying hens.

In light of the limited research on latent damages during the construction of large-span suspension arches, this study introduced a method to simulate structural damage utilizing random porosity. Initially, based on data from real-world engineering projects, the most susceptible areas within the arch structure were pinpointed. Subsequently, multiple test arch simulation models were constructed. Employing Python, commands for random porosity were implemented within ABAQUS and distinct mesh modules were devised to depict structures under varying degrees of damage. The current investigation delved into the structural responses of these susceptible areas under different damage rates, shedding light on damage progression patterns. Notably, our findings demonstrated that concealed damages on the top plate of the arch foot profoundly influenced structural integrity, whereas damages at the arch hance were comparatively minimal and predominantly manifest at the arch base. The pronounced localized damage at both the arch base and hance initiated and intensified at sectional corners, necessitating enhanced anti-crack measures in these regions. Moreover, depending on the stresses of the arch structure, diverse reinforcement strategies could be employed, optimizing the balance between load-bearing efficiency and cost considerations. [ABSTRACT FROM AUTHOR]

The control performance of the voltage source inverter will be reduced when it operates under complex microgrid (MG) conditions. Traditionally, problems such as high controller order and complex design may occur when robust control and secondary regulation are adopted for the inverters. Meanwhile, when the conventional adaptive control is adopted, the adaptive gain may be too high, resulting in increased sensitivity of the system to external disturbances. To address the above problems, a novel Narendra‐based adaptive anti‐disturbance control strategy for microgrid voltage source inverter is proposed in this paper. Firstly, a Narendra adaptive‐based controller is constructed, which enables the controller to adaptively adjust the system parameters to change the inverter output voltage quality. Secondly, the extended state observer (ESO) is introduced to observe and suppress the total disturbance of the system, which can make the system less sensitive and ensure the stability of the system operation. Thirdly, according to the Lyapunov stability theory, the Lyapunov function is chosen to prove the global asymptotic stability of the system, and an adaptive law is designed. Finally, the comparative experiment shows that the proposed control strategy based on Narendra adaptive anti‐disturbance can maintain excellent voltage control performance in complex working conditions, and then ensure that the inverter's output voltage always meets power quality standard in complex working conditions. [ABSTRACT FROM AUTHOR]

Quorum quenching (QQ) is a novel anti-biofouling strategy for membrane bioreactors (MBRs) used in wastewater treatment. However, actual operation of QQ-MBR systems for wastewater treatment needs to be systematically studied to evaluate the comprehensive effects of QQ on wastewater treatment engineering applications. In this study, a novel QQ strain, Acinetobacter pittii HITSZ001, was encapsulated and applied to a MBR system to evaluate the effects of this organism on real wastewater treatment. To verify the effectiveness of immobilized QQ beads in the MBR system, we examined the MBR effluent quality and sludge characteristics. We also measured the extracellular polymeric substances (EPS) and soluble microbial products (SMP) in the system to determine the effects of the organism on membrane biofouling inhibition. Additionally, changes in microbial communities in the system were analyzed by high-throughput sequencing. The results indicated that Acinetobacter pittii HITSZ001 is a promising strain for biofouling reduction in MBRs treating real wastewater, and that immobilization does not affect the biofouling control potential of QQ bacteria. [ABSTRACT FROM AUTHOR]

The reasonable power quality assessment model of microgrid is significant to the planning and management for a microgrid. In the power quality assessment, how to extract and integrate the implicative information in different evaluation indicators, to realize the comprehensive power quality assessment, are needs to be further studied. To solve the above problems, a microgrid power quality assessment model based on multivariate Gaussian distribution and local sensitivity analysis is proposed in this paper. Firstly, the probability density function is used to integrate the individual assessment indicators, and the correlation between the assessment indicators is reflected by the covariance matrix, and then the above characteristics of evaluation indicators are quantized to be as the indicator weights by using the local sensitivity. Comparing with the traditional power quality assessment method, the proposed evaluation model is more suitable for microgrid. Finally, an example is developed to verify that the overall situation of microgrid power quality can be reasonably reflected by the proposed evaluation model. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) needs to be connected to the microgrid (MG) through an inverter. The power quality of MG is impacted due to the characteristics of DGs and access to many types of loads. Traditionally, robust control or secondary regulation is used in MG inverters to solve power quality problems. However, there are issues in which the controller order is too high or the design is too complicated. A novel adaptive control strategy based on Narendra theory for voltage source inverter in MGs is proposed in this paper to solve the above problems. This strategy improves the control performance by designing an adaptive law. The MG inverter can adjust the parameters adaptively to change the output voltage quality under complex working conditions. The example shows that the inverter with the proposed adaptive control strategy can maintain good voltage control performance under complex conditions of MG, thus ensure the power quality in the MG. [ABSTRACT FROM AUTHOR]

The interferometric synthetic aperture radar (InSAR) technique based on time–frequency (TF) analysis has great potential for mapping the forest canopy height model (CHM) at regional and global scales, as it benefits from the additional InSAR observations provided by the sublook decomposition. Meanwhile, due to the wider swath and higher spatial resolution of single-polarization data, InSAR has a higher observation efficiency in comparison with PolInSAR. However, the accuracy of the CHM inversion obtained by the TF-InSAR method is attenuated by its inaccurate coherent scattering modeling and uncertain parameter calculation. Hence, a new approach for CHM estimation based on single-baseline InSAR data and sublook decomposition is proposed in this study. With its derivation of the coherent scattering modeling based on the scattering matrix of sublook observations, a time–frequency based random volume over ground (TF-RVoG) model is proposed to describe the relationship between the sublook coherence and the forest biophysical parameters. Then, a modified three-stage method based on the TF-RVoG model is used for CHM retrieval. Finally, the two-dimensional (2-D) ambiguous error of pure volume coherence caused by residual ground scattering and temporal decorrelation is alleviated in the complex unit circle. The performance of the proposed method was tested with airborne L-band E-SAR data at the Krycklan test site in Northern Sweden. Results show that the modified three-stage method provides a root-mean-square error (RMSE) of 5.61 m using InSAR and 14.3% improvement over the PolInSAR technique with respect to the classical three-stage inversion result. An inversion accuracy of RMSE = 2.54 m is obtained when the spatial heterogeneity of CHM is considered using the proposed method, demonstrating a noticeable improvement of 32.8% compared with results from the existing method which introduces the fixed temporal decorrelation factor. [ABSTRACT FROM AUTHOR]

Accurate and effective monitoring of environmental parameters in tea seedling greenhouses is an important basis for regulating the seedling environment, which is crucial for improving the seedling growth quality. This study proposes a tea seedling growth simulation (TSGS) model based on deep learning. The Internet of Things system was used to measure environmental change during the whole seedling process. The correlation between the environmental parameters and the biomass growth of tea seedlings in various varieties was analyzed. A CNN-LSTM network was proposed to build the TSGS model of light, temperature, water, gas, mineral nutrition, and growth biomass. The results showed that: (1) the average correlation coefficients of air temperature, soil temperature, and soil moisture with the biomass growth of tea seedlings were 0.78, 0.84, and −0.63, respectively, which were three important parameters for establishing the TSGS model. (2) For evaluating the TSGS model of a single variety, the accuracy of ZM's TSGS based on the CNN-LSTM network was the highest (Rp2 = 0.98, RMSEP = 0.14). (3) For evaluating the TSGS model of multiple varieties, the accuracy of TSGS based on the CNN-LSTM network was the highest (Rp2 = 0.96, RMSEP = 0.17). This study provided effective technical parameters for intelligent control of tea-cutting growth and a new method for rapid breeding. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is critical for the respiration, transpiration, and photosynthesis of crops. Color indices (CIs) and vegetation indices (VIs) extracted from unmanned aerial vehicle (UAV) imagery have been widely applied to the monitoring of the crop LAI. However, when the coverage of the crop canopy is large and only spectral data are used to monitor the LAI of the crop, the LAI tends to be underestimated. The canopy height model (CHM) data obtained from UAV-based point clouds can represent the height and canopy structure of the plant. However, few studies have been conducted on the use of the CHM data in the LAI modelling. Thus, in this study, the feasibility of combining the CHM data and CIs and VIs, respectively, to establish LAI fitting models for winter wheat in four growth stages was investigated, and the impact of image resolution on the extraction of remote sensing variables (the CHM data, CIs, and VIs) and on the accuracy of the LAI models was evaluated. Experiments for acquiring remote sensing images of wheat canopies during the four growth stages from the RGB and multispectral sensors carried by a UAV were carried out. The partial least squares regression (PLSR), random forest regression (RFR), and support vector machine regression (SVR) were used to develop the LAI fitting models. Results showed that the accuracy of the wheat LAI models can be improved in the entire growth stages by the use of the additional CHM data with the increment of 0.020–0.268 in R2 for three regression methods. In addition, the improvement from the Cis-based models was more noticeable than the Vis-based ones. Furthermore, the higher the spatial resolution of the CHM data, the better the improvement made by the use of the additional CHM data. This result provides valuable insights and references for UAV-based LAI monitoring. [ABSTRACT FROM AUTHOR]

The timely and accurate detection of wheat lodging at a large scale is necessary for loss assessments in agricultural insurance claims. Most existing deep-learning-based methods of wheat lodging detection use data from unmanned aerial vehicles, rendering monitoring wheat lodging at a large scale difficult. Meanwhile, the edge feature is not accurately extracted. In this study, a semantic segmentation network model called the pyramid transposed convolution network (PTCNet) was proposed for large-scale wheat lodging extraction and detection using GaoFen-2 satellite images with high spatial resolutions. Multi-scale high-level features were combined with low-level features to improve the segmentation's accuracy and to enhance the extraction sensitivity of wheat lodging areas in the proposed model. In addition, four types of vegetation indices and three types of edge features were added into the network and compared to the increment in the segmentation's accuracy. The F1 score and the intersection over union of wheat lodging extraction reached 85.31% and 74.38% by PTCNet, respectively, outperforming other compared benchmarks, i.e., SegNet, PSPNet, FPN, and DeepLabv3+ networks. PTCNet can achieve accurate and large-scale extraction of wheat lodging, which is significant in the fields of loss assessment and agricultural insurance claims. [ABSTRACT FROM AUTHOR]