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New energy power plants generate vast amounts of operational data and experience highly variable operating conditions， posing a significant challenge when it comes to diagnosing faults in generator sets. For this reason， a fault diagnosis model based on an improved SVM （support vector machine） algorithm is introduced. Firstly， the concept and principle of SVM for new energy power plants are analyzed. A multivariate SVM classifier is used to optimize the SVM. Secondly， the fault signal extraction method and fault characterization method of photovoltaic power stations and wind farms are studied. Moreover， a fault diagnosis model is proposed. Finally， sample data are obtained from new energy power plants， and an improved SVM fault diagnosis model based on decision level fusion is built. Additionally， the model is trained using fault feature vectors. The results show that the fault diagnosis accuracy for photovoltaic power plants reaches 97.5%， while the fault diagnosis accuracy for wind farms reaches 98.09%， which verify the accuracy of the method.

Demand response is an important way to balance the power supply and demand， and to wake up user-side resources. In view of data trust， privacy protection， transaction efficiency and other issues of current demand response business， a solution based on smart contract on blockchain is proposed. First， according to the invited demand response mechanism， the pain points and difficulties of the current business mechanism are analyzed. Then， the smart contract on blockchain is introduced to construct the technical framework of the invited demand response framework based on smart contract， to design the operation mechanism and the related smart contract functions.， and to analyze the technical advantages of the smart contract on blockchain applied in the demand response business. Finally， the deficiencies of this mechanism are discussed， and the future development direction of demand response business based on blockchain technology is discussed concerning demand response service increase， transaction system improvement， user-side interaction enhancement， and timeliness advancement.

Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF) neural networks with Inverse Distance Weighting (IDW), termed the IDW-RBFNN. This framework initially uses the IDW method to apply preliminary weights based on distance to the data points, which are then used as input for the RBF neural network to form a training dataset. Subsequently, the RBF neural network further trains on these data to refine the interpolation results, achieving more precise spatial data interpolation. We compared the interpolation prediction accuracy of the IDW-RBFNN framework with ordinary Kriging (OK) and RBF methods under three different parameter settings. Ultimately, the IDW-RBFNN demonstrated lower error rates in terms of RMSE and MRE compared to direct RBF interpolation methods when adjusting settings based on different power values, even with a fixed number of data samples. As the sample size decreases, the interpolation accuracy of OK and RBF methods is significantly affected, while the error of IDW-RBFNN remains relatively low. Considering both interpolation accuracy and resource limitations, we recommend using the IDW-RBFNN method (p = 2) with at least 60 samples as the minimum sampling density to ensure high interpolation accuracy under resource constraints. Our method overcomes limitations of existing approaches that use fixed steady-state distance decay parameters, providing an effective tool for soil fertility monitoring in delta regions. [ABSTRACT FROM AUTHOR]

The existing forage harvester header cannot automatically adjust the height and inclination during operation, resulting in uneven stubble height of forage, which, in turn, affects the efficiency of harvesting and the quality of forage regeneration. To address this issue, this study conducted the design and experimentation of a 2-degrees-of-freedom (DOF) profiling header. Firstly, this study designed an adaptive profiling header with 2-DOF adjustment, which was realized by the height adjustment mechanism and the tilt angle adjustment mechanism. The relationship model between the profiling device and the attitude of the header was established so that the header can acquire ground undulation in real time through the angle sensor of the profiling device. In order to verify the rationality of the header design, a co-simulation model of ADAMS and MATLAB/Simulink was built, and the header attitude control system was designed based on the fuzzy PID algorithm. The co-simulation results show that the header height ( H ) is always kept around 150 mm during the forwarding process of the harvester, with a maximum error of 5.8 mm, and the average relative error ( R E H ) and root mean square error ( R M S E H ) were 1.4% and 2.6 mm, respectively, and the maximum error of the tilt angle ( γ ) of the header is 0.53° and the R M S E γ is 0.22°, which indicates that the header profiling mechanism can accurately reflect the undulation of the terrain and the header attitude control system has good robustness. Finally, the test platform was built and tested in a grassland. The test results show that the average height of the header is 149.8 mm, the maximum error is 7.5 mm, and the R E H and R M S E H are 3.4% and 5.3 mm, respectively. The average error of the header inclination is 0.34°, and the maximum error is 0.57°. The test results indicate that the header can realize the adaptive adjustment of height and inclination, and the control system has high precision, stability and reliability, meeting the demand of automatic regulation of header attitude of a forage harvester. [ABSTRACT FROM AUTHOR]

Chinese flowering cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee) is an important leaf vegetable originating from southern China. Its planting area is expanding year by year. Accurately judging its maturity and determining the appropriate harvest time are crucial for production. The open state of Chinese flowering cabbage buds serves as a crucial maturity indicator. To address the challenge of accurately identifying Chinese flowering cabbage buds, we introduced improvements to the feature fusion approach of the YOLOv5 (You Only Look Once version 5) algorithm, resulting in an innovative algorithm with a dynamically adjustable detection head, named FPNDyH-YOLOv5 (Feature Pyramid Network with Dynamic Head-You Only Look Once version 5). Firstly, a P2 detection layer was added to enhance the model's detection ability of small objects. Secondly, the spatial-aware attention mechanism from DyHead (Dynamic Head) for feature fusion was added, enabling the adaptive fusion of semantic information across different scales. Furthermore, a center-region counting method based on the Bytetrack object tracking algorithm was devised for real-time quantification of various categories. The experimental results demonstrate that the improved model achieved a mean average precision (mAP@0.5) of 93.9%, representing a 2.5% improvement compared to the baseline model. The average precision (AP) for buds at different maturity levels was 96.1%, 86.9%, and 98.7%, respectively. When applying the trained model in conjunction with Bytetrack for video detection, the average counting accuracy, relative to manual counting, was 88.5%, with class-specific accuracies of 90.4%, 80.0%, and 95.1%. In conclusion, this method facilitates relatively accurate classification and counting of Chinese flowering cabbage buds in natural environments. [ABSTRACT FROM AUTHOR]

The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP3Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP3Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters. [ABSTRACT FROM AUTHOR]

Visible remote sensing of UAV has advantages of low cost, simple operation, real-time acquisition of remote sensing images and high ground resolution. This paper proposed a method of precise classification of cultivated land based on UAV visible remote sensing image. Taking the visible remote sensing image of some cultivated land in Shiqiao Village, Tieyong Town, Huidong County, Guangdong Province as the research object, the area information, shape information and location information of cultivated land were monitored and extracted. Two kinds of vegetation based on visible wave band were used in the image by object-oriented method. Based on the analysis of index, texture and shape information, the better scheme of extracting cultivated land information by classification was studied. After repeated experiments, the segmentation scale 45 and the merging scale 90 were determined as the segmentation parameters. At the same time, the non-planted cropland and other land objects were separated using band information and texture information. The overall accuracy of the method was 89.23%, and the Kappa coefficient was 0.72. The experimental results showed that although there were some cases of mistaken lifting and mistaken lifting of fine plots in the classification of cultivated land using UAV visible remote sensing data, the overall accuracy remained at a high level, which provided a reference for crop classification of cultivated land, and provided precise data basis for precision agriculture. [ABSTRACT FROM AUTHOR]