Featured Application The whole-stalk reed-harvesting header designed in this paper can be used for efficient whole-stalk harvesting and real-time baling of reeds while also being suitable for the efficient whole-stalk harvesting of other, similar tall-stalk crops.Abstract The whole-stalk harvesting and baling of reeds (Phragmites australis (Cav.) Trin. ex Steud) require a header with high efficiency and low loss rate. Based on an analysis of reed characteristic parameters, this paper proposes a T-shaped layout header design for whole-stalk reed harvesting. The design employs bilateral transverse conveying chains and a longitudinal clamping conveying chain, coordinated with a baler at its end, to achieve integrated harvesting and baling of whole reed stalks, thereby improving efficiency. By analyzing the relationship between key header parameters, such as the height of the lifting lugs on the transverse conveying device, the speeds of the transverse and longitudinal conveying chains, and the forward speed of the header, and the posture of the reeds, the causes of header loss during reed harvesting with the T-shaped header are identified. On this basis, a set of design criteria for the key parameters of the T-shaped whole-stalk reed header is established. A T-shaped reed whole-stalk harvesting header was designed according to these criteria and tested in harvesting experiments with varying preset values for forward speed and lower transverse conveying chain speed. Experimental data show that under optimally matched parameters, the header achieves a low average loss rate (Lh <= 2.0%) and a high average harvesting efficiency at the rated forward speed (up to 1.0 hm2/h, including baling), verifying the correctness of the theoretical analysis and the design criteria. The research results provide theoretical and experimental support for the design of conveying systems in headers for tall-stalk crops such as reeds.

To address the problems of low manual pickup efficiency and severe soil accumulation in existing shovel-type pickup devices used for segmented potato harvesting, a composite pickup device consisting of a grid-type segmented pickup shovel and a flexible lifting wheel was designed. A potato-soil-machine discrete element simulation model was established using EDEM to optimize structural parameters, including grid spacing, and to investigate the effects of operating parameters, including forward speed, digging depth, and lifting-wheel speed, on pickup performance. The simulation results showed that grid spacing was negatively correlated with the loss rate and positively correlated with the damage rate. An optimized grid spacing of 25 mm reduced the damage rate to 2.21% and the loss rate to 3.70%, while effectively reducing potato jamming. As the forward speed and lifting-wheel speed increased, the loss rate decreased, whereas the damage rate increased. Digging depth had a nonlinear effect on the loss rate, which initially decreased and then increased, reaching its minimum at a digging depth of 100 mm, whereas the damage rate continuously decreased with increasing digging depth. Field tests yielded an average loss rate of 3.88% and an average damage rate of 2.24%, meeting the operational requirements for potato harvesting.

To address the pronounced issue of noise and vibration within the combine harvester cab, this study proposes a hybrid simulation and experimental validation approach that integrates the pivot noise transfer function (NTF) with a finite element method (FEM)-based vibroacoustic coupling analysis. A coupled finite element model combining the cab structure and its internal acoustic cavity was developed, with the excitation path characteristics explicitly defined. The coupled interaction between structural and acoustic modes, along with its influence on noise transmission, was systematically examined. The analysis revealed a significant transmission peak near 18 Hz at critical pivot Point D under specific excitation directions, indicating strong directional sensitivity in the excitation–response relationship. Experimental validation showed that the discrepancy between simulated and measured responses, including the NTFs, remained within 15%, confirming the accuracy and applicability of the proposed method. This research offers a reliable analytical framework and practical reference for noise and vibration reduction in agricultural machinery cab design.

Agricultural machinery operates under complex field conditions involving uneven terrain, crop flow impacts, variable speed and load, dust, moisture, and multi-source structural excitation. These factors make vibration-based fault diagnosis more challenging than that of conventional rotating machinery because weak fault features are often masked by non-stationary background vibration and operating condition disturbances. This review provides a structured synthesis of vibration-based fault diagnosis for agricultural machinery, focusing on tractors, combine harvesters, harvesting machinery, and key components such as bearings, gearboxes, transmission systems, headers, threshing drums, cleaning sieves, vibrating screens, chassis, frames, and cab systems. The review first analyzes vibration sources, fault mechanisms, and signal degradation under field conditions. It then summarizes vibration sensors, data acquisition, preprocessing, time–frequency analysis, feature representation, machine learning, deep learning, transfer learning, and multi-source information fusion. Applications are reviewed from component-level diagnosis to whole-machine monitoring. Key challenges include field data scarcity, variable conditions, sensor reliability, data leakage, model generalization, edge deployment, standardization, and long-term validation. Future research should emphasise high-quality field datasets, physics-informed and explainable models, robust cross-condition diagnosis, multimodal sensing, edge intelligence, digital twins, and predictive maintenance. This review highlights the need to connect vibration mechanisms, diagnostic models, and engineering deployment requirements for reliable agricultural machinery health monitoring. Rather than treating sensors, components, algorithms, and deployment issues as separate topics, this review organizes the literature around field-specific vibration disturbances, validation evidence, deployable diagnostic requirements, and future implementation priorities.

This study aimed to improve the separation efficiency of damp crops by developing a hot-air cleaning fan. First, the aerodynamic performance of two fans with different inlet structures was compared. The results showed a direct correlation between the static pressure and the outlet air velocity. The modified-inlet fan has an outlet average air velocity very close to the original fan of the combine harvester. Hence, the modified- inlet fan model was chosen for further analysis. Subsequent evaluations were conducted to assess the effects of fan speed, inlet air temperature, and volute temperature on fan performance. Fan speed exhibited a negative correlation with outlet air temperature but a positive correlation with outlet air velocity, at both the upper and lower outlets. Furthermore, both the inlet air temperature and the volute temperature were directly related to the air temperature at these outlets. A Response Surface Methodology (RSM) with three factors at three levels was utilized to optimize fan parameters, identifying fan speed and inlet air temperature as the predominant factors affecting outlet air velocity and temperature, respectively.The optimal set of parameters was determined to be a fan speed of 1445 rpm, an inlet temperature of 85 °C, and a volute temperature of 60 °C. Under these conditions, bench test results indicated the average air velocity and temperature are basically consistent with the theoretical optimal value.

Striking the pods on upper layer of main stalk and top part branches should be reduced. ABSTRACT: The reel motion trajectory of the traditional rapeseed combine harvester is unable to adapt to the characteristics of rapeseed plants, leading to high reel rapeseed loss. In this study, the "Ningza 158" rapeseed variety-during the optimum harvest period-was selected as the research object, with a sample size of 25. From early May to mid-July 2024, the correlation between the three-dimensional (3D) structural characteristic parameters of rapeseed plants and the difference in the pod characteristic parameters at different growing positions of a single rapeseed plant was analyzed using statistical methods, which provided an important theoretical basis for improving the adaptability of the reel to the characteristics of rapeseed plants. The results showed that, above the stubble height, the center of gravity height of rapeseed plants was approximately 8/13 of the plant height, and the variation coefficient of the relative position was 4.0%. The variation coefficient of the 3D structural characteristic parameters ranged from 9.3 to 41.9%, indicating that the characteristic was complex and highly variable. Based on a criterion of a correlation coefficient >= 0.8, nine linear regression models among different 3D structural characteristic parameters were established. It was necessary to improve the adaptability of the reel to the change in plant height by coordinating the adjustment of the installation height and the diameter of the reel. Striking the pods on the upper layer of the main stalk and top part branches should be reduced in the reel operation, which would be helpful in diminishing reel rapeseed loss.

[Objective]The precise quantification of rice seeds within individual cavities of seedling trays constitutes a critical operational parameter for optimizing seeding efficiency and fine-tuning the performance of air-vibration precision seeders. Achieving high accuracy in this task directly impacts resource utilization, seedling uniformity, and ultimately crop yield. However, the operational environment presents significant challenges, including complex backgrounds, seed overlap, variations in lighting and seed orientation, and the inherent difficulty of distinguishing individual seeds within dense clusters. These factors often lead to suboptimal performance in existing automated detection systems, manifesting as low detection accuracy and an inability to achieve robust, precise instance segmentation of individual rice seeds. To address these persistent limitations and advance the state-of-the-art in precision seeding monitoring, an integrated framework for rice seed instance segmentation was proposed. The core innovation lies in the synergistic combination of a cross-modal grounding generation (CGG) network with a pretrained model, which is designed to leverage complementary information from visual and textual domains.[Methods]The proposed methodology fundamentally aimed to bridge the gap between visual perception and semantic understanding within the specific context of rice seed detection. The CGG-pretrained model framework achieved this through deep joint alignment of visual features extracted from seedling tray images and textual features derived from contextual knowledge. This cross-modal grounding enabled collaborative learning, where the visual processing stream (handling object localization and pixel-level segmentation) was continuously informed and refined by the semantic understanding stream (interpreting context and relationships). Specifically, the visual backbone network processes input imagery to generate feature maps, while the pretrained language model component, which utilized contextual embeddings, generated semantically rich textual representations. The CGG module acted as the fusion engine, establishing explicit correspondences between specific regions in the image (potential seeds or clusters) and relevant semantic concepts or descriptors provided by the pretrained model. This bidirectional interaction significantly enhanced the model's ability to disambiguate overlapping seeds, resolved occlusions, and accurately delineated individual seed boundaries under challenging conditions. Key technical innovations validated through rigorous ablation studies include: (1) The strategic use of the bootstrapping language-image pre-training (BLIP) model for generating high-quality pseudo-labels from unlabeled or weakly labeled image data, facilitating more effective semi-supervised learning and reducing annotation burden, and (2) the application of bidirectional encoder representations from transformers (BERT)-based word embed to capture deep semantic relationships and contextual nuances within textual descriptors related to seeds and seeding environments.[Results and Discussions]The ablation experiments demonstrated a pronounced synergistic effect when the core improvements were combined, resulting in a segmentation accuracy improvement exceeding 3 percentage points compared to the baseline model that lacking the integration. Comprehensive experimental evaluation demonstrated the superior performance of the proposed CGG model against established benchmarks. Under the standard intersection over union (IoU) threshold of 0.5, the model achieved a mean average precision (mAP) of 90.7% for bounding box detection (denoted as mAP50bb for detection) and an outstanding 91.4% mAP for instance segmentation (denoted as mAP50seg for segmentation). These results represented a statistically significant improvement over leading contemporary models, including region-based convolutional neural network (Mask R-CNN) and Mask2Former, which highlighted the efficacy of the cross-modal grounding approach in accurately localizing and segmenting individual rice seeds. Further validation within realistic seeding trial scenarios, which involved direct comparison with meticulous manual annotations, confirmed the model's practical robustness. The CGG model attained the highest accuracy in two critical operational metrics: (1) Precision in segmenting individual seed instances (single-seed segmentation accuracy), and (2) accuracy in determining the exact seed count per cavity, and it achieved an average accuracy of 88% for per-cavity quantification. Moreover, the model exhibited superior performance in minimizing estimation errors for cavity seed counts, as evidenced by its significantly lower error metrics: a root mean square error (RMSE) of 16.8 seeds, a mean absolute error (MAE) of 13.7 seeds, and a mean absolute percentage error (MAPE) of 2.46%. These error values were markedly lower than those recorded by the comparison models, which underscored the CGG model's enhanced reliability in practical counting tasks. The discussion contextualized these results and attributed the performance gains to the model's ability to leverage semantic context to resolve ambiguities inherent in visual-only approaches, particularly in dense and overlapping seed scenarios common in precision seeding trays.[Conclusions]The developed CGG-pretrained model integration presents a significant advancement in automated monitoring for precision rice seeding. The model successfully addresses the core challenges of low detection accuracy and imprecise instance segmentation for seeds in complex environments. Its high accuracy in both individual seed segmentation and per-cavity seed count quantification, coupled with low error rates, demonstrates strong potential for practical deployment. Importantly, the model enables real-time detection of rice seeds during the image analysis stage, this functionality provides a quantifiable, data-driven basis for making immediate operational decisions, most notably enabling the targeted precision reseeding of empty or under-seeded cavities identified during the seeding process. By ensuring optimal seed placement and density from the outset, the technology contributes directly to improved resource efficiency (reducing seed waste), enhanced seedling uniformity, and potentially higher crop yields. Future work will focus on further optimizing inference speed for higher-throughput seeding lines and exploring generalization to other crop types and seeding mechanisms.

To address the high efficiency and precision seeding requirements for rice factory seedling cultivation, a whole tray air-suction dual-station high efficiency seedling cultivation and seeding assembly line was designed. Based on the whole-tray air-suction principle, the study focused on the design of a dual-suction seed tray dual-station precision hole-to-hole seeding device, a transverse seedling tray conveying device, and an automatic seed replenishment device. Nan Jing 46, Nan Jing 5055, and Koshihikari rice seeds were used for seeding performance experiments on the assembly line. The impact of workstation configuration and production efficiency on the empty hole rate, reseeding rate, damage rate, and seeding precision was analyzed. Experimental results showed that the dual-station precision seeding method significantly improved seeding efficiency. When the seeding efficiency reached 2000 trays per hour, the seeding rate of 1-3 seeds per hole was 90.1%, while the damage rate, empty hole rate and reseeding rate were 0.38%, 0.84%, 9.06% respectively. This study shows that combining the whole tray air-suction principle with transverse seedling tray conveying device, dual-station precision hole seeding device, and automatic seed addition device can significantly enhance the seeding efficiency while ensuring the seeding accuracy, achieving a seeding efficiency of over 2000 trays per hour. This study provides an important reference for further improving the seeding accuracy and efficiency of precision rice seedling cultivation and seeding assembly line.

To prevent blockage in axial flow threshing and separation devices caused by varying material moisture and feeding rates while simplifying monitoring and diagnostic system, a test bench was used to collect vibration signals from four monitoring points of devices, analyse blockage tendencies under different conditions. Signals were denoised and reconstructed with the Slime Mould Algorithm and Variational Mode Decomposition, and segmented with overlapping moving time windows. Time, frequency, and time-frequency domain features were extracted to assess device operating status and sensitivity of signal changes at different monitoring points. Findings revealed that the duration of a slight blockage tendency was long under normal moisture content and small increments of feeding rate. With high moisture content and large increments of feeding rate, the duration of slight blockage tendency will decrease and quickly enter a severe blockage tendency state, with continued feeding resulting in immediate blockage. The monitoring point directly below the concave grate exhibited the most sensitive signal changes, with the largest waveform variations and standard deviation deviations. Feature dimensionality reduction was performed using Relief-F algorithm, and Bayesian-optimised machine learning models were trained for state identification. The diagnostic model of a monitoring point directly below the concave grate demonstrated high diagnostic accuracy, recall, and reliability, indicative of an effective monitoring point. The Bayesian-optimised Support Vector Machine model achieved the best performance, with 85.1 % and 93.6 % accuracy under different conditions and rapid prediction speeds (53000 and 40000 obs s(-1)). This met the requirements for a simplified, accurate, and fast online monitoring system.

Soil blockage in potato-soil separation devices during operation significantly compromises harvesting efficiency, necessitating real-time monitoring solutions. This study developed a multi-sensor data acquisition system to capture strain signals from the comb teeth, vibration signals from the differential-speed roller and rod screen bearings, speed signals from the differential-speed roller and rod screen under different working conditions. Then, a genetic algorithm was used to optimize the Gauss kernel parameters, and a support vector machine model for identifying soil blockage was established based on the extracted features. The results show that the device is a risk of blockage if one or more of the following conditions occur: (1) the filtered peak strain of comb teeth 5and 6 exceeds 0.3x10-4; (2) the amplitude of meshing frequency between rod screen and rubber wheel is reduced to 0.01; (3) the peak-to-peak value of soil skateboard vibration signal is lower than 70 % of the normal value; (4) the rod screen and the differential-speed roller speed are lower than 80 % of the normal value. The model with optimal kernel parameters exhibited high accuracy, with 96.7 % precision, 94.2 % recall rate and 95.4 % F1-score for the test set. This study establishes a theoretical framework for intelligent blockage monitoring in potato harvesters, with practical implications for improving harvesting efficiency.

The rotating speed of the threshing cylinder was hardly constant in practice due to transmission error and cylinder speed would change with feed quantity to ensure the grain threshing effect. This paper proposed a novel unbalance detection method for non-stationary rotation. Variational mode extraction (VME) was introduced to extract unbalanced vibration signal and the unbalance feature index (UFI) was constructed as a criterion of weighting factor selection. Moreover, the unbalance phase calculation method and phase reference in non-stationary were proposed, which could eliminate the influence of speed change. The advantages of VME and UFI were verified by simulation, and the effectiveness of proposed method was proved by experiment on multi-cylinder test rig and combine harvest. This method could realize the reconstruction of unbalanced vibration signal in time-varying speed with low computational cost, break the strict requirement of constant speed of the existing balancing method.

The goal of this study is to help solve the problem of large vibrations generated by the motion of the cutter and pendulum ring system during the operation of a combined harvester, which in turn leads to significant vibrations in the cab. The kinematic equations of the cutter and pendulum ring system and the simplification of these complex equations into a two-degree-of-freedom system model are established in this study. On this basis, we apply the cuckoo search algorithm to obtain the optimal parameter combination of the cutter system, which can effectively reduce the vibration. The SIMULINK simulation model was constructed to obtain the simulation results of the vibration acceleration of the cutter system under four working conditions. The results prove that the simulation results for Case 1 are similar to the experimental measurements under the original parameters of the harvester. The vibration acceleration amplitude of the cutter is -240$ m/s^2 to 220 m/s^2, and the vibration acceleration amplitude of the pendulum ring is -13 m/s^2 to 15 m/s^2, which verifies the correctness of the simplified model and provides a basis for subsequent vibration damping of the cutter system. The experimental study shows that through parameter optimisation and adjustment, the cutter vibration decreased by 8.4% and the cab vibration decreased by 12.9%.

It is important to adjust the diameter of the hydraulic variable-diameter threshing drum adaptively according to the change of feeding rate for the combine harvester. To solve the problem that the drum diameter cannot be adaptively controlled, the variable universe fuzzy PID (VUFPID) controller with adaptive contracting-expanding factor was developed and its field performance was verified. The VUFPID controller with adaptive contracting-expanding factor and the fuzzy PID controller were established using MATLAB, and the simulation was compared and analysed. The simulation results showed that the VUFPID controller with adaptive contracting-expanding factor had better control characteristics. Field experiment results showed that the adaptive control system can adjust the drum diameter to change the threshing gap in real time according to the change of feeding rate. When the adaptive control system was turned on, the average grain entrainment loss rate was reduced by 8.72 % compared with that without the adaptive control system.

To address the poor real-time detection of potato damage during harvesting and the high computational complexity of the model, this paper proposed a lightweight detection algorithm based on the YOLOv8 framework—Light-YOLOv8. The algorithm achieved lightweight detection by integrating EfficientNet-B0 composite scaling strategy to optimize model parameters and integrating the MBConv network module to reduce the backbone network’s weight. By combining the lightweight network Slim-neck with the CARAFE upsampling operator, the SNC neck network was designed and constructed, to reduce the weight and enhance its ability to process detailed information. Additionally, Light-YOLOv8 employed the PReLU activation function to optimize network performance. Experimental results demonstrated that Light-YOLOv8 achieved a mean average precision (mAP@50–95) of 95.23

To reduce the drag encountered by the potato harvester during excavation in wet soil, a bionic digging shovel was designed based on the vole's front middle toe. By using MATLAB software to extract and fit the information of the contour curve of the vole's front middle toe, the bionic digging shovel model was designed and established. A comparative experiment was conducted on the vole-imitating digging shovel, Gryllotalpa-imitating digging shovel, and common digging shovel in EDEM software, to verify that the vole-imitating digging shovel has drag reduction capability. Through single- and multi-factor simulation analysis, the structural and operational parameters of the vole-imitating digging shovel were optimized. The excavation drag of three types of shovels was compared through field experiments, and the correctness of the simulation results was verified. The results show that the optimal parameter combinations of the vole-imitating digging shovel, i.e., a single shovel width of 135 mm, an entry angle of 20°, and a forward speed of 0.5 m/s. Under the same working conditions, the vole-imitating digging shovel has a better drag reduction effect and a higher soil breakage efficiency than the other two shovels.

The cleaning sieve box is a key component to achieve the cleaning of a combine harvester, and its service life directly affects the reliability of the entire machine. Aiming at the high quality of the cleaning sieve box and the cyclic loading during operation (which can easily cause fatigue damage and affect its service life), a fatigue durability analysis method for the cleaning sieve box based on a test bench is proposed. First, the deformation and stress distribution of the sieve box are analyzed through modal and dynamic simulation to identify the hotspots of fatigue damage in the sieve box. On this basis, a sieve box test bench was designed and built to collect load signals of the sieve box. The layout of sensor measuring points was optimized based on simulation results and the force characteristics of the sieve box was analyzed through signal processing. Then based on the load signals from multiple measuring points, a fatigue load spectrum was developed using nCode software, and the fatigue life of the sieve box was predicted using Miner's fatigue damage theory. The results indicated that there are multiple stress concentration areas in the connection area between the shaking plate, fish scale screen and the side wall of the screen frame of the sieve box, which are the fatigue damage risk areas. The stress value in the connection area of the tail screen is the highest. Overall, the fatigue life of the front half of the sieve box is generally higher than that of the rear half. The connection area between the side walls of the screen frame and the tail screen is the weak fatigue durability area, with fatigue lives of 5.829 × 106 and 5.591 × 106 cycles, respectively. This study provides a certain basis for the design and optimization of the cleaning sieve box structure.

Traditional cereal combine harvesters cannot adjust the threshing gap according to the change of feeding rate, leading to large threshing losses. Previous research has developed a hydraulic variable-diameter threshing drum, which adjusts the threshing gap by changing the drum diameter. However, the oil pressure within and without the rod cavity of the hollow hydraulic cylinder for the threshing drum is affected by both the feeding rate and threshing gap in these systems. The correlation model between the independent variables (feeding rate and threshing gap) and the response values (threshing performance and oil pressure) has not yet been studied. Therefore, in this study, the relationship between the independent variables and the response values is studied by using the test method of central composite rotatable design (CCRD). The results showed that under the optimal threshing gap corresponding to different feeding rates, the threshold of oil pressure change within rod cavity was approximately 0.64-0.78 MPa, with a variation of <0.14 MPa. Therefore, an adaptive monitoring method based on constant oil pressure is proposed, which could keep the oil pressure within the rod cavity in the threshold range by adjusting the threshing gap. This minimised the entrainment loss rate under different feeding rates.

In order to solve the problem encountered by traditional potato–stem separation devices, that is, they cannot meet the requirements when installed in small-scale harvesters, a new type of vertical differential roller potato–stem separation device was developed. The device features a compact structure and simultaneously possesses both separating and conveying functions. Through the analysis of the separation force between potato and stem, the structure and parameters of the separation device were determined. The simulation and the field test of the potato–stem separation process were carried out with the vertical differential roller speed, the vertical differential roller gap width and the conveyor chain speed as the influencing factors. The simulation test analysed the influence law of different working parameters on the performance of potato–stem separation. The field test revealed the order of the effects of various factors on the impurity rate and skin-breaking rate, concluding that the optimal combination of operational parameters was a vertical differential roller rotational speed of 6 s−1, a vertical differential roller gap width of 7 mm, and a conveyor chain speed of 1.4 m·s−1. This experiment fills the research gap in the study of potato–stem separation devices suitable for small-scale potato harvesters and promotes the development of compact potato harvesters.

ABSTRACT The combine harvester is a widely used piece of agricultural equipment in modern agriculture, and the seed loss rate is one of the important indexes used to measure its operational performance, so the monitoring of the seed loss rate is crucial for adjusting the operational parameters of combine harvesters and improving the quality of grain harvesting. Aiming at the problems of the slow response speed and low monitoring accuracy of the existing domestic seed loss rate monitoring models, this paper proposed a rice seed loss rate monitoring method based on the whale optimization algorithm-back propagation neural network (WOA-BP). The loss rate monitoring device consisted of a piezoelectric ceramic sensor module, charge amplification circuit, band-pass filter circuit, analog-to-digital (AD) converter, main control unit, etc. The WOA-BP algorithm, which has a high accuracy and fast response speed, was used to classify and count the signals to realise seed loss rate monitoring. The indoor test results showed that the relative errors of the monitoring results are less than 8.5% under the condition of a conveying speed of 1.3-2.1m/s, and the relative errors showed an increasing trend as the proportion of straw increased.

Multi-blade centrifugal fans are regarded as an important direction for agricultural cleaning fans due to the advantages of compact structure, low noise and high efficiency. In view of the insufficient aerodynamic performance of traditional centrifugal fans in combine harvester cleaning devices caused by straight-blade structures, as well as the lack of design theory for multi-blade centrifugal fans, this study focused on a multiblade centrifugal fan with wing-shaped blades. Using a combination of CFD numerical simulation and bench tests, the influence of the volute width-to-diameter ratio on fan performance and the internal flow field was investigated. The results showed that at a rated speed of 1000 r/min, the optimal volute width-to-diameter ratio was 1.3. Compared with the prototype fan, the optimized fan achieved a 7.13% increase in efficiency, a 6.53% increase in air volume, and a 16.63% improvement in air distribution uniformity. In addition, the internal flow of the optimized fan was improved, with reduced turbulence intensity in the tongue region of the volute. Furthermore, a volute width-to-diameter ratio-speed-air volume function model was established using MATLAB, providing a theoretical basis for the selection and design of high-performance multi-blade centrifugal fans.