This study proposes a comprehensive prediction framework for strawberry quality assessment, addressing the limitations of single-indicator methods through hyperspectral imaging and chemometric analysis. Hyperspectral images of strawberries at different ripening stages were acquired, along with measurements of both external and internal quality parameters. A strawberry quality index (SQI) was constructed through correlation and factor analyses, integrating multiple key indicators to objectively quantify overall fruit quality. The spectral data were preprocessed using Savitzky-Golay (SG) smoothing, multiplicative scatter correction (MSC), standard normal variate (SNV), and their pairwise combination methods to identify the optimal preprocessing strategy. Feature wavelengths were then selected using competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projections algorithm (SPA). Finally, three SQI prediction models, including partial least squares regression (PLSR), support vector regression (SVR), and convolutional neural network (CNN), were developed based on different input variables. The results demonstrated that the CNN model using full-spectrum input achieved the best performance, with an Rp2 of 0.916 and an RMSEP of 0.157 on the test set. Overall, the SQI-based modeling approach offered a more systematic and comprehensive assessment of strawberry quality than traditional single-indicator methods, providing a robust foundation for optimal harvest timing and nondestructive, rapid, and integrated quality evaluation.

The beans of edamame are tightly enclosed by the pod, and this multilayer structure complicates internal light transport, posing challenges for hollow-defect detection. This study systematically explored the optical properties and light transport mechanisms of edamame, and developed a mechanism-guided hollow-defect detection model. Diffuse reflectance and transmittance spectra of the pod, seed coat, and bean were acquired to estimate the absorption and reduced scattering coefficients of different edamame structures using the inverse adding-doubling algorithm. Based on these optical parameters, wavelength-dependent penetration depths were calculated. The pod exhibited the largest calculated penetration depth at 918 nm, reaching approximately 0.97 mm, suggesting relatively lower attenuation and greater potential for subsurface optical information acquisition. Multilayer light-propagation models of normal and hollow regions further revealed distinct structure-dependent energy distribution patterns. Although spectral differences were observed in both the visible and near-infrared regions, the 700–1000 nm range exhibited weaker attenuation and greater sensitivity to internal structural variations than the absorption-dominated visible region. Guided by the optical penetration and multilayer light-transport analysis, an HE-HybridSN model using 700–1000 nm reflectance spectra achieved 93.94

Abstract As wind power penetration in power grids increases, the inherent variability of wind power output poses substantial challenges to secure and stable operation. This study proposes a dual-level co-optimization framework that couples signal decomposition and predictor tuning to enhance short-term wind power forecasting accuracy for dispatch-oriented applications. Specifically, the crested porcupine optimizer (CPO) is first employed to adaptively optimize key variational mode decomposition parameters, enabling more effective decomposition of the original wind power series to alleviate mode mixing and reduce nonstationarity. A hybrid predictor combining a bidirectional temporal convolutional network (BiTCN), a bidirectional gated recurrent unit (BiGRU), and an attention mechanism is then constructed (BiTCN-BiGRU-A), and CPO is further used to optimize its critical hyperparameters. Experiments on four representative months (March, July, October, and December) show that, relative to the BiTCN-BiGRU-A baseline, the Proposed Method achieves average reductions of 60.1% in mean absolute error and 61.6% in root mean square error, indicating consistent improvements under the representative conditions considered. Moreover, a case-study style cross-site assessment on an external wind farm dataset provides preliminary evidence of the effectiveness of the Proposed Method under changed operating conditions, supporting its practical relevance for short-term dispatch and operational decision-making in power grids.

The vertical heterogeneity of rice canopy structure limits the accuracy of inverting leaf physicochemical parameters using traditional radiative transfer models, while LiDAR-based 3D reconstruction remains costly for large-scale applications. To address these challenges, this study proposes a method for constructing 3D rice canopy scenes using "Precision Mode" and "Rapid Mode" strategies. The Precision Mode builds detailed structural models based on measured morphological parameters, validated via the LESS 3D radiative transfer model. To overcome the limitations of obtaining detailed morphology via UAV remote sensing, the Rapid Mode employs machine learning algorithms-specifically Support Vector Machine (SVM), Random Forest (RF), and XGBoost-to map easily accessible parameters (LAI, Above-ground Biomass, Plant Height, and Transplanting Date) to detailed 3D structural parameters. Results indicate that XGBoost achieves the highest accuracy in the Rapid Mode. Furthermore, simulated spectra under both modes showed high consistency with measured spectra, yielding average RMSE values of 0.0104 (R2 = 0.9965) for the Precision Mode and 0.0307 (R2 = 0.9694) for the Rapid Mode. Although the spectral accuracy of the Rapid Mode is slightly lower, its modeling efficiency is significantly enhanced, retaining a strong capability to reproduce spectral response characteristics across growth stages. This approach provides an effective tool for analyzing vertical spectral response mechanisms and offers an efficient data simulation scheme for UAV remote sensing parameter inversion based on 3D radiative transfer models.

Maturity assessment is a key link in the precise grading and sorting of edamames with pod. Traditional maturity assessment of edamames with pod relies on manual visual inspection by agricultural producers, which suffers from high labor intensity, strong subjectivity, and high error rates. This study employed visible-near infrared hyperspectral imaging technology to perform non-destructive, precise detection on edamames with pod samples representing four maturity stages. In this study, linear partial least-squares discriminant analysis (PLS-DA), least-squares support vector machine (LS-SVM), and k-nearest neighbors (KNN) were applied to discriminate the maturity of edamames with pod. In addition, EMSTFormer-SpecNet of maturity assessment model for edamames with pod was proposed in this study, which included a channel attention mechanism, a multi-scale feature extraction module, and a transformer module. The result of this study demonstrates that the EMSTFormer-SpecNet model outperforms machine learning models and common deep learning models in processing one-dimensional hyperspectral reflectance data of edamames in pod with different maturity levels. The classification accuracy rate of the test set reached 97.82

In this study, hyperspectral imaging technology was utilized to monitor the alterations and spatial distribution of soluble solid content (SSC) in Actinidia arguta during postharvest storage. The fruit were exposed to a 24 h anaerobic treatment in a pure N2 atmosphere and then stored at ambient temperature for 10 days. These findings collectively affirm that N2 treatment can effectively decelerate the softening process of Actinidia arguta by impeding firmness loss and SSC progression. After preprocessing, feature band extraction was conducted using competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), and a synergistic iVISSA-CARS algorithm. Partial least squares regression (PLSR) and particle swarm optimization extreme learning machine (PSO-ELM) models were developed for SSC prediction, with the PSO-ELM model yielding the most accurate predictions. In the test set, the CARS-PSO-ELM model for the control group achieved an Rp2 of 0.877, an RMSEP of 0.611, and an RPD of 1.953, while the iVISSA-CARS-PSO-ELM model for the N2 treatment group achieved an Rp2 of 0.904, an RMSEP of 0.554, and an RPD of 2.236. Finally, SSC visualization maps of Actinidia arguta were generated for both the control and treatment groups based on their respective optimal models, providing valuable references for comprehensive quality assessment during subsequent processing, transportation, and commercialization stages.

To facilitate the participation of small- and medium-sized customer-side resources into the electricity market, with the aim of optimizing the allocation of electricity resources, this paper proposes the participation of small- and medium-sized adjustable resources in the electricity market in the form of generation load aggregators. Considering the coupling role of the electric energy market and the peaking auxiliary service market, a joint model of generation load aggregators that participate in the electric energy–peak shaving market is constructed. Comparing the model solution with those of the control group, the peak-to-valley difference of 10.5 MW is much lower than that of the control group, which is 16.54 MW. Compared with the control group, the profit was increased by USD 2.6 thousand, or 6.06%. It can be seen that the model proposed in this paper can reduce the peak and valley pressure as well as the control pressure of the power system from the load side. By participating in the peak shaving market, the transferable loads within the generation load aggregators can give full play to its adjustable characteristics, thereby reducing the cost of electricity consumption and increasing its profit, and providing a certain theoretical basis for the electricity market management to design the rules for adjustable users to enter the electricity market.

Identification of hollow edamame with pod remains a challenge due to its similarity in the appearance with normal edamame with pod. In this study, Hyperspectral reflection/transmission imaging (RHSI/THSI) were used to obtain hyperspectral reflection/transmission image. The reflection characteristic images (RIs) and transmission characteristic images (TIs) were extracted by SC (SPA and CARS) combined with image quality assessment coefficient (IQAc). Aiming at the problem of low grayscale in TIs, targeted optimization was carried out. SSD, YOLOv5 and YOLOv8 were constructed based on RIs, TIs and optimized transmission characteristic images (OTIs) for the Identification of hollow edamame. The research results show that all models can efficiently identify hollow edamame, with the recognition accuracy reaching over 95 %. In comparison, THSI performs better than RHSI in discriminating hollow edamame. Among all the models, OTIs-YOLOv8 has the highest Identification precision, with a precision of 99.57 % and a recall of 99.9 %. The results indicate that the optimized transmission characteristic images exhibit more significant advantages than the reflection characteristic images in the discrimination of hollow edamame. The research of this study provides a solid methodological approach and reliable experimental basis for the quality evaluation of edamame and the development of related rapid detection equipment.

To promote the optimal allocation of electricity resources, this paper designs a trading framework for small and medium-sized adjustable electricity users to participate in China's dual-tier electricity market through load aggregators, proposes an inter-provincial transaction correction subsidy coefficient, and analyzes the purchase cost and risk at multiple time and space scales. Combining user demand response and preference behavior, a purchase and sale electricity decision-making model is constructed to achieve revenue maximization and risk control. The experimental results show that the spot purchase strategy proposed in this paper faces relatively low market risks. After applying the subsidy, the daily profit of the load aggregator increases by 145%; the inter-provincial purchase volume increases, and the resource allocation is optimized; the electricity charges of terminal users decrease, with a reduction range of 3.08-7.78 $/MWh. This indicates that the participation of small and medium-sized adjustable electricity users' loads in the dual-tier electricity market through load aggregators expands the trading scope, which is conducive to enhancing the efficiency and sustainable development of the electricity market.

Hollowness is a common defect in edamame with pod that affects its market value and yield. The similar appearance of hollow and normal edamame with pod makes detection and sorting challenging. This study utilized hyperspectral reflectance imaging and transmission imaging to detect hollow edamame with pods. Various classification models were constructed, including partial least squares discriminant analysis, support vector machine, random forest, artificial neural network, and linear discriminant analysis. Six preprocessing methods (SG, MA, MSC, SNV, DT, and WT) and three feature selection methods (SVC-SPA, CARS, and GA) were used to optimize the models. Based on the optimal model, the hollow regions of edamame were visualized, and the proportion of hollow areas was quantified. Based on the optimal threshold for hollow ratio, edamame classification was performed. Results indicate that the SG-SPA-SVM model, derived from hyperspectral transmission data using eight characteristic bands, achieved the best classification performance, attaining a classification accuracy of 98 % at a hollow ratio threshold of 0.48. It offers a scientific basis for quality assessment in related pod and the development of spectrometer applications in production sorting.

With the rapid development of distributed PV, many distributed PV devices are connected to the power grid, which is essential to optimize the scheduling in the power grid containing a high proportion of distributed PV. In this paper, a new day-ahead optimal dispatching model of a power system combined with the high proportion of photovoltaic is established. The impact of time-of-use tariffs on customers and the regulation of electricity by energy storage plants are considered in the model. The main contribution of this paper is that providing a better solution for grids with a high proportion of distributed photovoltaic, reducing carbon emissions and improving photovoltaic consumption. A solution approach i.e., Wild horse optimizer (WHO) is employed to optimal dispatch. The results show that compared with the Particle swarm algorithm, using the Wild horse optimizer has saved 16% costs, reduced 28% in thermal power carbon emissions, and increased 24% in distributed PV utilization rates. Wild horse optimizer is a better optimal approach which can reduce distributed PV abandonment rates and decrease costs when applied to the optimal scheduling in the grid with high percentage of distributed photovoltaic.

Abstract This review presents a state-of-the-art literature review of automatic generation control (AGC) control strategies for power systems containing renewable energy sources. The incorporation of renewable energy into the power system has a large impact on the stability, reliability, economy and security of the power system. To mitigate these effects, it is important to choose a suitable control strategy for AGC. However, there is a limited amount of literature available on the review of AGC in renewable energy power systems, so a review of AGC control strategies for renewable energy-containing power systems is necessary. The investigation of this paper focuses on all kinds of different AGC control strategies for renewable energy-containing power systems, such as proportional integral derivative control, fuzzy control, artificial neural network control, etc, and compares and considers these different control methods, while this paper summarises the power system models with/without renewable energy. In addition, this paper summarises and discusses the application of intelligent optimization algorithms and energy storage systems to control strategies. The problems and future research directions of the current research on power systems with renewable energy sources are also discussed.

Greenhouse vegetables have become increasingly important in global crop production due to their ability to be cultivated out of season and ensure a year-round supply of vegetables. With the rapid advancement of “phenomics”, accurately measuring the phenotypic information of greenhouse vegetables is crucial for enhancing both their yield and quality. Over the past two decades, various technologies have been developed for phenotypic detection of fruits, vegetables, and other crops, based on the interaction between electromagnetic waves and matter. While some articles have investigated these applications, there is a lack of a systematic review specifically focused on the phenotypic detection of greenhouse vegetables. In this review, RGB imaging, Multispectral/Hyperspectral imaging, Chlorophyll fluorescence imaging, Thermal imaging, Raman imaging, X-ray imaging, Magnetic resonance imaging, and Terahertz imaging are collectively referred to as spectrum imaging technologies. We provide a comprehensive review of the origins, research progress over the past twenty years, and current challenges of spectrum imaging in the field of greenhouse vegetable research. It focuses on identifying the most suitable spectrum imaging technologies for detecting four categories of phenotypic traits: biochemical, physiological, morphological, and yield-related traits. Additionally, we highlight the issues that need optimization in the practical application of these technologies and the bottlenecks faced in different trait studies. Finally, based on existing research, we propose several potential solutions and future research directions to maximize the utility of spectrum imaging technologies in the phenotypic detection of greenhouse vegetables.

Wind energy is a clean and renewable source that has the potential to alleviate the global fossil fuel crisis and environmental pollution by generating electricity. However, accurately predicting wind energy output remains challenging due to its inherent uncertainty. To enhance the accuracy of wind power prediction, a short-term wind power forecasting method for power systems, MC-VMD-CNN-BiLSTM, is proposed, which considers error rolling correction. The method begins with feature selection and outlier handling using the quadrature method. Then, wind power data is decomposed into multiple sub-sequences using the Variational Mode Decomposition (VMD) technique to reduce the raw volatility of wind power. Then, a Convolutional Neural Network (CNN) followed by a Bidirectional Long Short-Term Memory (BiLSTM) model is used for wind power prediction. Finally, the proposed method utilizes the Monte Carlo method for rolling error correction by using known errors from previous time frames to correct subsequent predictions. The MC-VMD-CNN-BiLSTM proposed in this paper considering error rolling correction is compared with ELM, SVM, PSO-BP and ARIMA models through an example analysis of the data of a city, and the proposed model in this paper reduces 61.78%, 50.35%, 62.30% and 73.05% in the NRMSE index in the spring as an example, respectively. The results show that the prediction model proposed in this paper has higher prediction accuracy compared with the traditional prediction model.

针对蓝莓果蝇虫害分类识别存在效率低、准确度差等问题,采用深度学习方法对采集的蓝莓高光谱图像进行数据处理与分析,以实现蓝莓果蝇虫害的无损检测.首先蓝莓高光谱图像采用PCA进行降维,优选数据集PC2与PC3并进行拼接得到最佳数据集PC23,对数据集中图像进行旋转90°、旋转180°、模糊、高亮、低亮、镜像和高斯噪声共7种增强操作,使各数据集容量扩增为原始容量的18倍.然后采用VGG16、InceptionV3与ResNet50深度学习模型对蓝莓果蝇虫害图像进行检测,均取得了较高的识别准确率.其中ResNet50模型效率最高,且ResNet50模型的准确率最高,达到92.92％,损失率最低,仅有3.08％,因此ResNet50模型在蓝莓果蝇虫害无损检测方面整体识别效果最佳.为了进一步提高蓝莓果蝇虫害无损检测性能,从ECA注意力模块、Focal Loss损失函数与Mish激活函数3方面对ResNet50模型进行了改进,构建了改进的im-ResNet50模型.得出im-ResNet50模型识别准确率达95.69％,损失率为1.52％.试验结果表明,im-ResNet50模型有效提升了蓝莓果蝇虫害识别能力.采用Grad-CAM分析了 im-ResNet50模型可解释性,能够快速、准确地无损检测蓝莓果蝇虫害.

In order to solve the problem of poor grid connected consumption capacity of renewable energy in power system, a multi-source coordinated optimal scheduling method of wind-PV-hydro-thermal-nuclear-storage is proposed in this paper. The optimal scheduling method comprehensively considers the constraints of system power balance, capacity and climbing rate of various power generation modes, establishes the objective function based on the system power purchase cost and renewable energy consumption, obtains the weight relationship between the objective functions by analytic hierarchy process. An adaptive improved genetic algorithm is proposed, and the objective function is solved and the results are compared by this algorithm and three algorithms such as adaptive improved particle swarm algorithm, respectively. The results show that the improved genetic algorithm can eliminate the local optimum phenomenon by normalizing the crossover and combining variances, and is more suitable for the multi-source coordinated optimization scheduling model compared with the other algorithms. After the multi-source coordination and optimal dispatch, the system renewable energy disposal is reduced from 657MWh to 208MWh, and the power purchase cost of the system load is reduced from ${\$}$ 30.077 to ${\$}$ 30.052M. It can be seen that the selected optimization method can enhance the system’s renewable energy consumption capacity while reducing the system’s power purchase cost, which reflects the effectiveness of the optimization method used in this paper. By changing the scaling parameters of the hierarchical analysis method, the scheduling scheme can be flexibly selected for different regions, reflecting the universal applicability of the optimization method used in this paper. To provide an efficient and accurate solution for multi-energy dispatching problems in power systems.

Introduction: In recent years, with the rapid development of renewable energy generation, the stability of the power grid has been greatly reduced. In response to this problem, integrating the user side transferable load into the power market has become the key to the development of future power grid. At present, large transferable loads have entered the electricity market in some pilot areas of China, but the relevant research on small and medium-sized transferable users entering the electricity market is still few.Methods: This paper proposes the concept of generation load aggregators. A two-stage generation load aggregator robust optimization model is developed to obtain the scheduling scheme with the lowest operating cost under the worst scenario. The model consists of distributed renewable power, transferable load, self-provided power, energy storage, etc. Uncertainties of renewable energy and load are introduced in the model. By using the column constraint generation algorithm and strong pairwise theory, the original problem is decomposed into the main problem and sub-problems to be solved alternately, so as to obtain the scheduling scheme with the lowest operating cost in the worst scenario under different conservatism.Results: The solved results are compared with those without the generation load aggregator, illustrating the role of the generation load aggregator in relieving peak and valley pressure on the grid from the load side, reducing the cost of electricity for loads, and promoting the consumption of renewable energy. The comparison with the deterministic optimization algorithm shows a significant decrease in the total cost and validates the performance of the selected solution algorithm. The boundary conditions for the use of energy storage by generation load aggregators for peak and valley reduction under the time-sharing tariff mechanism are also derived.Discussion: This study can provide reference for the investors of generation load aggregators when planning whether to install energy storage or the scale of energy storage, and also help the power market management department to design a reasonable incentive mechanism.

This review presents a state-of-the-art literature review of automatic generation control (AGC) control strategies for power systems containing new energy sources. The incorporation of new energy into the power system causes a large impact on the stability, reliability, economy and security of the power system. In order to mitigate these effects, it is important to choose a suitable control strategy for AGC. However, there is no review of AGC control strategies specifically for new energy-containing power systems, so a review of AGC control strategies for new energy-containing power systems is necessary. The investigation of this paper focuses on all kinds of different AGC control strategies for new energy-containing power systems, such as PID control, fuzzy control, artificial neural network control, etc., and compares and considers these different control methods, while this paper summarises the power system models with/without new energy. In addition, this paper summarises and discusses the application of intelligent optimisation algorithms and energy storage systems to control strategies. The problems and future research directions of the current research on power systems with new energy sources are also discussed.

This review presents a state-of-the-art literature review of automatic generation control (AGC) control strategies for power systems containing new energy sources. The incorporation of new energy into the power system has a large impact on the stability, reliability, economy and security of the power system. In order to mitigate these effects, it is important to choose a suitable control strategy for AGC. However, there is no review of AGC control strategies specifically for new energy-containing power systems, so a review of AGC control strategies for new energy-containing power systems is necessary. The investigation of this paper focuses on all kinds of different AGC control strategies for new energy-containing power systems, such as PID control, fuzzy control, artificial neural network control, etc., and compares and considers these different control methods, while this paper summarises the power system models with/without new energy. In addition, this paper summarises and discusses the application of intelligent optimization algorithms and energy storage systems to control strategies. The problems and future research directions of the current research on power systems with new energy sources are also discussed.