Transplanting status is a significant indicator for rice cultivation, and is essential for field management, food security and agricultural production. However, traditional characterization cannot detect the transplanting status in a timely and effective manner; manual seedling replanting is labor-intensive, has a high cost and is inefficient. This study proposed a detection method for floating seedlings and missed transplanting. The method employed a self-built improved YOLO, namely HGV-YOLO. We leverage a HorBlock module to achieve the splitting of the morphological features of rice seedlings in different dimensions of the backbone network of YOLOv8n, which enabled the network to further enhance the classification and recognition ability of rice seedlings. Furthermore, Grouped Spatial Convolution (GSConv) replaces convolution, and the VOV-GSCSP replaces the C2f modules, reducing the number of parameters and improving the model’s inference speed. To improve the model’s bounding box precision, the WIoU loss function was also incorporated. Finally, we use the least squares method to predict the center point of the rice seedlings. The experimental results indicate that HGV-YOLO achieves a precision of 93.7%, a recall of 83.1%, and an mAP@0.5 of 91.1%. Compared to YOLOv8n, HGV-YOLO reduces Params by 3.1% and GFLOPs by 1.2%, respectively, while improving mAP@0.5 by 2.3%. Compared to YOLOv3-tinyYOLOv5 and YOLOv6, HGV-YOLO achieves increases in mAP@0.5 of 4.6 %, 3.1%, and 2.8%, respectively. In summary, the HGV-YOLO model exhibits a strong performance and provides valuable insights for advancing the autonomous navigation of rice transplanting robotics.

Aiming at the issue that increasing the forward speed during target spraying of calcium and other agents onto the heart and inner leaf margins of Chinese cabbage plants leads to changes in the effective sprayed area covered per unit time and the pesticide spray volume, a target spray control system based on deep learning was designed. This system aims to investigate the relationship between the forward speed of target spraying and the spray volume and coverage in the scenario of applying calcium agents to the heart of Chinese cabbage plants. Firstly, the structure and working principle of the target spray control system were elaborated. Secondly, the general YOLOv8 model was improved, proposing a lightweight BWT-YOLO model that integrates long-range recognition, sprayer forward speed, and spray response frequency. Utilizing a Jetson Xavier NX controller equipped with the lightweight BWT-YOLO model, a pressure-stabilized execution unit and a target control unit were designed, and the system performance was tested through intermittent spray experiments. During the experiments, the forward speed of the spray platform was gradually increased, and the spray volume of the corresponding nozzle was obtained based on the response frequency of the solenoid valve. The response times of the three main parts of the target spray system were recorded: the average image processing time was 29.50 ms, the decision signal transmission time was 6.40 ms, and the spray process duration was 88.83 ms. Comprehensive analysis indicated that the total response time of the target spray lagged behind the electrical signal by approximately 124.73 ms. By compensating for the solenoid valve response lag time and conducting acquisition experiments, the difference between the actual spray volume after compensation and the required volume was derived. It was determined that under the condition of 7.2 km/h speed, the corresponding difference between actual and required volume was 0.01 L/min, which was the smallest difference and met the operational requirements for target spraying. This study provides a reference for the application of target application robots in spray systems and the selection of operational parameters.

To address the challenges of low precision and limited mechanization in the precision seeding of field-grown Fritillaria, a belt-type precision seeder with metering cells was developed. Based on TRIZ theory, a conical-cylindrical cell with a hemispherical bottom was designed. The belt metering mechanism, driven by an electric motor, operated in coordination with a brush-type seed-retaining plate to achieve precision seeding of Fritillaria. The overall design of the machine was validated using EDEM simulation. Orthogonal experiments were conducted with forward speed, cell diameter, and seed drop height as test factors, and the seeding qualification rate as the evaluation index. The results showed that the optimal parameter combination was a forward speed of 0.88 km/h, a cell diameter of 24 mm, and a seed drop height of 75 mm. Under these conditions, the qualification rate reached 93.16%, meeting the requirements for precision seeding of Fritillaria.

Industrial hemp has significant utilization value, and China is the largest producer of industrial hemp in the world, primarily growing hemp for fiber. Heilongjiang Province is the largest area of hemp fiber cultivation in China. In response to issues such as inconsistent operating standards, low efficiency, and poor harvesting quality of industrial hemp harvesters in China, this study integrates the mechanical properties of the “Hanma 5” hemp stalk and applies cutting platform design theory to analyze and optimize the key components of existing hemp harvesters, aiming to obtain optimal operational parameters. First, by analyzing the motion laws of the cutting blade and the regression equations of shear power consumption and shear force, the relationship between cutting speed and time is established, and the cutting and conveying parameter ranges are derived, providing the basis for subsequent simulation analysis and field validation. Next, dynamic simulation analysis of the key components of the domestic 4GM-2.2 industrial hemp harvester for fiber is conducted using ADAMS and Workbench. The conveyor speed values and corresponding chain drive combinations under different conveyor speed ratios are obtained. Field experiments validated the optimal combination of key operational parameters for the industrial hemp harvester as follows: forward speed of 2.1 m/s, cutting speed of 2.5 m/s, conveyor speed ratio of 2.2, coefficient of variation for the laying angle of 6.88%, coefficient of variation for the laying thickness of 4.11%, and cutting height of 10.4 cm. Under the optimal operational parameter combination, the re-cut rate was 8.4%, with no missed cutting observed. This paper provides technical references for exploring the optimal operational parameters of industrial hemp harvesters for fiber to achieve high-quality harvesting operations.

The multi-factor coupling mechanism of droplet impact dynamics remains unclear due to insufficient analysis of leaf structure–droplet interaction and inadequate integration of simulations and experiments, limiting precision pesticide application. To address this, we developed a droplet impact model using the Volume of Fluid (VOF) method combined with high-speed camera experiments and systematically analyzed the effects of impact velocity, angle, and droplet size on slip behavior via response surface methodology. Methodologically, we innovatively integrated 3D reverse modeling technology to reconstruct soybean leaf microstructures, overcoming the limitations of traditional planar models that ignore topological features. This approach, coupled with the VOF method, enabled precise tracking of droplet spreading, retraction, and slip processes. Scientifically, our study advances beyond previous single-factor analyses by revealing the synergistic mechanisms of impact parameters through response surface methodology, identifying impact angle as the most critical factor (42.3% contribution), followed by velocity (28.7%) and droplet size (19.5%). Model validation demonstrated high consistency between simulation predictions and experimental observations, confirming its reliability. Practically, the optimized parameter combination (90° impact angle, 1.5 m/s velocity, and 300 μm droplet size) reduced slip displacement by over 50% compared to non-optimized conditions, providing a quantitative tool for spray parameter control. This work enhances the understanding of droplet–leaf interaction mechanisms and offers technical guidance for improving pesticide deposition efficiency in agricultural production.

Combining deep learning (DL) and near-infrared (NIR) spectroscopy provides a new direction for revolutionizing agricultural quality assessment methods. Typical DL frameworks often require a large number of samples, and the acquisition of NIR spectral samples is usually constrained by various factors. For this reason, this paper combines NIR spectral feature selection with improved stacked sparse autoencoder (ISSAE) DL to build a classification model suitable for small NIR samples and validate the model's performance with maize seed variety identification. An improved backward-spaced partial least squares method (IBIPLS) is used to select the feature wavelengths. IBIPLS keeps the number of spectral segments traveled within tolerable bounds. The correlation coefficient to the root mean square error of the cross-validation ratio was used to evaluate the merit of the extracted bands to achieve effective feature wavelength selection. ISSAE blends unsupervised feature learning with refined network parameter tuning, using a cross-entropy function as a supervised penalty term. A total of 276 NIR spectral samples from 13 maize varieties were collected, and IBIPLS selected 789 spectral features among 1845 spectral variables. The sparsity parameter and the number of hidden layer nodes were adjusted to obtain the best performance during the training of ISSAE. The model performs best when the sparsity parameter is 0.1 and the number of hidden layer nodes is 130, with a test accuracy of 98.91%. Comparing this model with models such as k-nearest neighbor (KNN), support vector machine (SVM), back propagation (BP) neural network, and 1-D CNN, it performs the best. The above test results show that the method proposed in this paper can achieve accurate and fast classification of maize varieties with good reliability and generalization performance, which provides a new idea for NIR small sample classification using DL.

As an innovative plant protection method in precision agriculture, electrostatic spray technology can increase the droplet coverage area by over 30% coMpared to conventional spraying. This technology not only achieves higher droplet deposition density and coverage but also enables water and pesticide savings while reducing environmental pollution. This study, combining theoretical analysis with experimental validation, reveals the critical role of electrode material selection in induction-based electrostatic spray systems. Theoretical analysis indicates that the Fermi level and work function of electrode materials fundamentally determine charge transfer efficiency, while corrosion resistance emerges as a key parameter affecting system durability. To elucidate the effects of different electrode materials on droplet charging, a coMparative study was conducted on nickel, copper, and brass electrodes in both pristine and moderately corroded states based on the corrosion classification standard, using a targeted mesh-based charge-to-mass measurement device. The results demonstrated that the nickel electrode achieved a peak charge-to-mass ratio of 1.92 mC/kg at 10 kV, which was 8.5% and 11.6% higher than copper (1.77 mC/kg) and brass (1.72 mC/kg), respectively. After corrosion, nickel exhibited the smallest reduction in the charge-to-mass ratio (19.2%), significantly outperforming copper (40.2%) and brass (21.6%). Droplet size analysis using a Malvern Panalytical Spraytec spray particle analyzer (measurement range: 0.1–2000 µm) further confirmed the atomization advantages of nickel electrodes. The volume median diameter (Dv50) of droplets produced by nickel was 4.2–8 μm and 6.8–12.3 um smaller than those from copper and brass electrodes, respectively. After corrosion, nickel showed a smaller increase in droplet size spectrum inhomogeneity (24.5%), which was lower than copper (30.4%) and brass (25.8%), indicating superior droplet uniformity. By establishing a multi-factor predictive model for spray droplet size after electrode corrosion, this study quantifies the correlation between electrode characteristics and spray performance metrics. It provides a theoretical basis for designing weather-resistant electrostatic spray systems suitable for agricultural pesticide application scenarios involving prolonged exposure to corrosive chemicals. This work offers significant technical support for sustainable crop protection strategies.

To address the technical problems of broad droplet size spectrum, insufficient atomization uniformity, and spray drift in plant protection unmanned aerial vehicle (UAV) applications, this study developed a novel two-stage aerial electrostatic spraying device based on the coupled mechanisms of hydraulic atomization and electrostatic induction, and, through the integration of three-dimensional numerical simulation and additive manufacturing technology, a new two-stage inductive charging device was designed on the basis of the traditional hydrodynamic nozzle structure, and a synergistic optimization study of the charging effect and atomization characteristics was carried out systematically. With the help of a charge ratio detection system and Malvern laser particle sizer, spray pressure (0.25–0.35 MPa), charging voltage (0–16 kV), and spray height (100–1000 mm) were selected as the key parameters, and the interaction mechanism of each parameter on the droplet charge ratio (C/m) and the particle size distribution (Dv50) was analyzed through the Box–Behnken response surface experimental design. The experimental data showed that when the charge voltage was increased to 12 kV, the droplet charge-to-mass ratio reached a peak value of 1.62 mC/kg (p < 0.01), which was 83.6% higher than that of the base condition; the concentration of the particle size distribution of the charged droplets was significantly improved; charged droplets exhibited a 23.6% reduction in Dv50 (p < 0.05) within the 0–200 mm core atomization zone below the nozzle, with the coefficient of variation of volume median diameter decreasing from 28.4% to 16.7%. This study confirms that the two-stage induction structure can effectively break through the charge saturation threshold of traditional electrostatic spraying, which provides a theoretical basis and technical support for the optimal design of electrostatic spraying systems for plant protection UAVs. This technology holds broad application prospects in agricultural settings such as orchards and farmlands. It can significantly enhance the targeted deposition efficiency of pesticides, reducing drift losses and chemical usage, thereby enabling agricultural enterprises to achieve practical economic benefits, including reduced operational costs, improved pest control efficacy, and minimized environmental pollution, while generating environmental benefits.

Accurate, rapid, and online determination of mango dry matter content (DMC) holds great significance for the mango industry. The integration of near-infrared spectroscopy and deep learning theory offers an opportunity to enhance determination accuracy. In this paper, we propose a spectral data augmentation method based on the sparse autoencoder and establish a one-dimensional convolutional model to predict mango DMC. The test results indicate that the model performs optimally when trained on a training set comprising 80 % of the augmented data. The root mean square error (RMSE) of the test set was 0.4073, and the coefficient of determination (R2) was 0.9782. The prediction accuracy of our model surpasses that of models such as Gaussian Process Regression, Support Vector Machines, and Partial Least Squares Regression. This study can assist in fruit quality inspection, processing optimization, variety selection, and breeding, as well as storage and preservation, and has a wide range of application potential and value. It also provides novel insights into data augmentation techniques for near-infrared spectral regression modeling.

IntroductionAccurate application of pesticides at the seedling stage is the key to effective control of Chinese cabbage pests and diseases, which necessitates rapid and accurate detection of the seedlings. However, the similarity between the characteristics of Chinese cabbage seedlings and some weeds is a great challenge for accurate detection.MethodsThis study introduces an enhanced detection method for Chinese cabbage seedlings, employing a modified version of YOLO11n, termed YOLO11-CGB. The YOLO11n framework has been augmented by integrating a Convolutional Attention Module (CBAM) into its backbone network. This module focuses on the distinctive features of Chinese cabbage seedlings. Additionally, a simplified Bidirectional Feature Pyramid Network (BiFPN) is incorporated into the neck network to bolster feature fusion efficiency. This synergy between CBAM and BiFPN markedly elevates the model’s accuracy in identifying Chinese cabbage seedlings, particularly for distant subjects in wide-angle imagery. To mitigate the increased computational load from these enhancements, the network's convolution module has been replaced with a more efficient GhostConv. This change, in conjunction with the simplified neck network, effectively reduces the model's size and computational requirements. The model’s outputs are visualized using a heat map, and an Average Temperature Weight (ATW) metric is introduced to quantify the heat map’s effectiveness.Results and discussionComparative analysis reveals that YOLO11-CGB outperforms established object detection models like Faster R-CNN, YOLOv4, YOLOv5, YOLOv8 and the original YOLO11 in detecting Chinese cabbage seedlings across varied heights, angles, and complex settings. The model achieves precision, recall, and mean Average Precision of 94.7%, 93.0%, and 97.0%, respectively, significantly reducing false negatives and false positives. With a file size of 3.2 MB, 4.1 GFLOPs, and a frame rate of 143 FPS, YOLO11-CGB model is designed to meet the operational demands of edge devices, offering a robust solution for precision spraying technology in agriculture.

To optimize the flow stability and improve application accuracy of the PWM intermittent variable-rate spraying system, which suffers from insufficient flow stability and response delays during changes in travel speed, this study proposes an intelligent control method based on an improved Adaptive Neural Fuzzy Inference System (ANFIS). Flow characteristic data of the solenoid valve were collected under four pressure conditions (0.2–0.5 MPa), drive frequencies (5–20 Hz), and duty cycles (10–90%) using an indoor test system. An ANFIS controller architecture was constructed with target flow rate and actual travel speed as input variables and PWM frequency-duty cycle combinations as output variables. This controller enhances the traditional single-output mode of ANFIS by achieving multi-output collaborative optimization through shared premise parameters, thereby strengthening the system’s nonlinear modeling and control capabilities. To validate the system’s practical performance, a field simulation test platform based on a spraying robot was constructed. By analyzing preset prescription map information, the system achieved precise variable-rate spraying operations during movement. Test results demonstrate that the steady-state error remains within 5.03% under various speed-varying conditions. This research provides a high-precision intelligent control solution for variable-rate spraying systems, holding significant implications for reducing pesticide application rates and advancing precision agriculture.

地方高校研究生培养模式是一直以来不断探索的问题,结合近年来的实践培养经验,在对目前地方高校研究生培养现状分析的基础上,通过调查问卷的方式指明了科技竞赛对研究生培养作用,阐明了科技竞赛是提高学生创新实践能力的有效途径,最后提出以科技竞赛为载体,构建地方高校研究生培养模式,提升研究生的综合素质和适应新时代发展要求的能力.

为了满足国家对研究生科研创新能力的需求,针对地方高校研究生科研创新能力培养的相关问题开展了问卷调查,在调查结果的基础上总结问题,结合多年实际经验,以黑龙江八一农垦大学农业工程学科为例,探索了地方高校研究生科研创新能力培养模式,以期为新工科背景下研究生协同创新培养提供借鉴.

Magnesium，aluminum，titanium and their alloys have been widely used in machinery，aerospace，biomedicine and other fields，owing to the advantages of high specific strength，rich resources and good processing performance.However，the disadvantage of poor wear resistance limits their applications in various fields severely.In this paper，starting from the formation principle and preparation method of the micro-arc oxidation composite coatings，the latest research statuses of friction and wear properties of coatings prepared by micro-arc oxidation composite technology on the surface of lightweight alloy materials such as magnesium alloy，aluminum alloy，titanium alloy，etc.were systematically reviewed.In addition，the current existing problems and future development direction of the micro-arc oxidation technology were pointed out，which could provide a certain reference for the follow-up research.

农业机器人的研发与应用是如今智慧农业发展的重要趋势,对靶施药机器人是农业机器人领域的重要分支.本研究总结了国内外对靶施药机器人的研究现状与进展,介绍了对靶施药机器人的工作原理与主要技术点.分析了对靶施药机器人关键技术的研究现状,包括病虫害检测技术、对靶施药技术以及自主行走和控制技术,并阐明了这些关键技术在国内外的研究进展和挑战.指出了目前国内外对靶施药机器人大多可以做到节省农药、提高农药利用率的效果,但其研发状态更多处于实验室阶段,尚不能完全胜任实际生产任务.本研究可为对靶施药机器人研发的推进和智能化农业的发展提供参考和思路.

采摘机械的末端执行器作为果蔬收获类机器人的重要组成部分,其采摘机械手的设计是收获机器人研发的重要环节.为此,分析了当前采摘机械手在国内外的发展现状,结合番茄果实的生物学特征,以保护番茄果实不受损伤为设计目标,利用NX12.0三维制图软件设计出了一种将作用力施加于番茄桔梗生长节点的番茄采摘机械手.同时,通过三维软件的仿真模块对机械手的关键零件进行有限元分析,进而对整体机构进行模拟仿真,仿真结果验证采摘机械手的设计合理性,旨在为下一步番茄采摘机械手的研发奠定基础.

Aerobic compost has been commonly used to efficiently dispose of resources recycling and environmental protection in modern agriculture. Among them, aeration can be one of the most important environmental factors to affect composting fermentation. It is necessary for a feasible network model to accurately control the oxygen supply of aeration. This study aims to improve the aeration efficiency and prediction accuracy of aerobic composting aeration. In-depth learning was selected to train a network model, in order to predict the oxygen supply of aeration during aerobic composting fermentation in this experiment. Raw materials were taken as cow dung, cow dung biogas residue, chicken manure, and corn straw in the Haihua Biogas Plant in Miyun District, Beijing, China. The corn straw was crushed by 1-2 cm in grain size. The cow dung,cow dung biogas residue, and chicken manure were uniformly mixed with the crushed corn straw for composting and fermentation. The sensor was used in the composting fermentation tank to collect the parameter data during aerobic fermentation. 268 groups of data were selected as the sample data, 218 groups of data were randomly selected as the input data,and 50 groups of data were selected as the test data. Clonal genetic algorithm（CGA） was used to predict the standard back propagation（BP） neural network model for the aeration oxygen supply, whereas, the 6-14-1 three-layer network structure was used as the basic structure of the prediction model. The input parameters were the temperature, humidity, oxygen concentration,room temperature, pH value, and electrical conductivity（EC）. The mean square error（MSE） of the number of hidden layer nodes was determined to be 14 after training and calculation. The output data was aeration. This article establishes BP neural network model for predicting aeration oxygen supply. Then the genetic algorithm（GA） and clonal selection algorithm were used to improve the prediction accuracy of the model. The experiment shows that the CGA-BP neural network model has the best prediction effect on aeration oxygen supply. 1) The CGA-BP neural network model accelerated the obtaining of the optimal solution, with an efficiency improvement of 75.36% and 51.30% compared to the BP model and GA-BP model,respectively. 2) In the prediction model of aeration oxygen supply, the CGA-BP model had a more accurate prediction effect,with a prediction accuracy of 99.65%. The prediction accuracy of aeration oxygen supply was 96.99% and the prediction accuracy of the GA-BP neural network model reached 99.26%. A comparison was made to evaluate the errors of BP, GA-BP and CGA-BP neural models. The model evaluation showed that the best performance was found in the CGA-BP neural network model with the smallest error, as shown by the mean absolute error（MAE）, mean absolute percentage error（MAPE）and mean square error（MSE）. 3) The improved CGA-BP neural network model can be predicted the aeration oxygen supply of aerobic composting, increasing the aeration control efficiency by 3.22%. The improved model can be expected to accurately predict the aeration oxygen supply of aerobic compost. The finding can provide a strong reference for accurate data for the next aeration.

针对传统深松作业质量检测方法耗时长、精度低的问题,本研究基于传感器技术设计了一种高精度、低成本的深松整地作业深度检测系统,该检测系统可依据深松机具悬挂系统的结构,结合倾角传感器、超声波传感器和深松机具运动过程中产生的几何位移变化规律测量耕深距离.研究结果表明:深松机悬挂结构在作业过程中的角度及高度变化规律符合实际作业情况,基于姿态解算与卡尔曼滤波推导出的耕深测量计算公式可用于深松作业中的耕深距离测量.

At present, near-infrared spectroscopy (NIRS) technology, can realize the rapid and non-destructive detection of seed vigor, but the vigor grade is generally less than 3, and the accuracy is not high. The contradiction between the increase of vigor level and model precision urgently needs to be solved in the near-infrared spectrum detection of seed vigor. Five kinds of seed samples were obtained by the artificial aging method, and the corresponding spectral data were collected to establish the BP prediction model. In order to improve the accuracy and robustness of the model, an algorithm of coupled Mean Impact Value-Successive Projection Algorithm (MIVopt-SPA(sa)) is presented. Aiming at the problem of determining the number of feature variables extracted by the Successive Projection Algorithm(SPA) , the algorithm sets the number range of feature wavelengths and selects the best in this range to realize adaptive SPA(SPA(sa)). Aiming at the problem that SPA algorithm takes a too long time, MIV algorithm is used to reduce the dimension of SPA algorithm. Although the MIV method can sort the wavelength influence values, it lacks the threshold value for selecting wavelength influence. Therefore, the relative distance ratio is introduced to optimize the MIV algorithm to effectively segment the characteristic wavelength range. The full spectrum with 1 845 wavelengths is extracted by the MIVopt-SPA(sa) algorithm, and 37 characteristic wavelengths are extracted, which are mainly distributed near the 7 main absorption peaks of near-infrared spectrum of maize seeds. The results show that the algorithm can effectively extract the characteristic wavelength, which is consistent with the NIR absorption characteristics of maize seed biochemical substances. In order to verify the effect of the algorithm on the performance of the model, the full spectrum BP model, SPA(sa)-BP model, MIV-BP model, MIVopt-SPA(sa)-BP model and competitive adaptive reweighting CARS-BP model were established to classify the five grades of maize seed vigor. The average prediction accuracy of the MIVopt-SPA(sa)-BP model is 99. 1% , which is higher than other models; the average prediction time is 14. 382 s, which is lower than that of the MIV-BP model (24. 523) ,CAR-BP (97. 226) and SPA(sa)-BP model (101. 224 s) , but higher than that of full-spectrum model (0. 253 1); The best performance cross-entropy is 0. 007 892, which is far lower than other 4 models. The experimental results show that the MIVopt-SPA(sa), algorithm can effectively improve the accuracy of the near-infrared detection model of maize seed vigor, realize multi-level, accurate and nondestructive detection of seed vigor, and provide a reference for optimizing the optimisation seed vigor detection model.

Many problems in the process of postgraduate training were studied,such as weak innovation consciousness,limited innovation thinking,lack of curriculum content and insufficient comprehensive practice training through analyzing the connotation of TRIZ theory. The innovative thinking of postgraduate was developed effectively and innovation consciousness of postgraduate was optimized through using innovative thinking training tools in TRIZ innovation theory. The innovation ability of postgraduate was strengthened through using invention problem analysis tool and invention problem solving tool in TRIZ innovation theory. The level of teachers’ teaching and scientific researching are steadily improved through the systematic training of TRIZ theory. At the same time,the content of innovative courses is enriched. The comprehensive quality of postgraduate innovation was improved,to achieve the goal of cultivating innovative talents.