Cleaning is one of the most important steps in the harvesting process, and the prolonged and high-load operation of the vibrating sieve can decrease its reliability. To uncover the structural flaws of the cleaning sieve in the crawler combine harvester and establish a foundation for quality inspection, this paper proposes a method for durability testing and analysis using vibration and strain signals. Via the modal analysis of the cleaning sieve, the most susceptible areas for fault signals are identified. Subsequently, a specialized test rig exclusively designed for the examination of the durability of the cleaning sieve is constructed. After following 96 h of uninterrupted operation, the vibration plate of the cleaning sieve sustains damage, resulting in atypical noise. A signal analysis reveals that the primary vibration signal of the cleaning sieve primarily consists of a fundamental frequency of 5 Hz, corresponding to the driving speed, as well as a frequency doubling signal of 50 Hz. After the occurrence of damage, the peak amplitude of the received vibration signal increases by over 86.3%. Furthermore, the strain gauge sensor situated on the support plate of the rear sieve detects anomalous signals with frequencies exceeding 300 Hz, which are accompanied by a considerable rise in the power spectral density. This research has significant importance for enhancing the service life of the cleaning sieve and optimizing the overall machine efficiency.

In order to meet the requirements of multi-crop harvesting, reduce the loss of harvesting, and improve the quality of harvesting, the device and method of matching and adjusting the rotation speed and forward speed of the pulling reel were designed. On the premise of satisfying the matching regulation, the parameters of the rotary wheel and the cutting table were adjusted so that the rotational speed ratio λ of the harvest crop was in the suitable range of the rotational speed ratio of the crop. The speed ratio was designed as a suitable interval of different crops by experiments and experience, and, in order to meet the requirements, it was designed to optimize the wheel speed. The speed-matching was designed, and through the experiments on the wheel-speed-adjustment error, which was less than 2%, it was designed to meet the design requirements. Crop-harvesting experiments were carried out under rotational speed-matching and a random speed, and the experimental results showed that the loss rate under rotational speed-matching was significantly lower than that under random speeds; the tests showed that the wheel speed designed with speed-matching can effectively reduce the loss rate.

A rice impurity detection algorithm model, DE-YOLO, based on YOLOX-s improvement is proposed to address the issues of small crop target recognition and the similarity of impurities in rice impurity detection. This model achieves correct recognition, classification, and detection of rice target crops with similar colors in complex environments. Firstly, changing the CBS module to the DBS module in the entire network model and replacing the standard convolution with Depthwise Separable Convolution (DSConv) can effectively reduce the number of parameters and the computational complexity, making the model lightweight. The ECANet module is introduced into the backbone feature extraction network, utilizing the weighted selection feature to cluster the network in the region of interest, enhancing attention to rice impurities and broken grains, and compensating for the reduced accuracy caused by model light weighting. The loss problem of class imbalance is optimized using the Focal Loss function. The experimental results demonstrate that the DE-YOLO model has an average accuracy (mAP) of 97.55% for detecting rice impurity crushing targets, which is 2.9% higher than the average accuracy of the original YOLOX algorithm. The recall rate (R) is 94.46%, the F1 value is 0.96, the parameter count is reduced by 48.89%, and the GFLOPS is reduced by 46.33%. This lightweight model can effectively detect rice impurity/broken targets and provide technical support for monitoring the rice impurity/ broken rate.

To improve the seeding qualification rate and stability of the air-suction vibrating disc type seed meter on the rice seedling raising line, in this paper, an improved wireless sensor network node layout optimization algorithm was proposed, and the operation monitoring system of the seed meter was designed using the Internet of Things and configuration software. In the system, the upper computer software adopted the Kingview software, the lower computer took the STM32F429IGT and CC2530 as the core controllers, and ZigBee was selected for data transmission to build the wireless sensor network. The acquisition of field status information and the sending of control instructions were realized through the sensor nodes constructed by the CC2530 core controller. The data was sent to the coordinator node in real-time through the wireless sensor network. The coordinator node realized the bidirectional transmission of data with Kingview and the upper computer control instructions forwarding using the ASCII protocol The host computer monitoring and management software was developed based on configuration software to realize real-time data monitoring, access database storage, fault alarm, control command sending and other functions. The experimental results showed that the detection accuracy of the system for the seeding amount and missed seeding amount was 94.3% and 95.6%, respectively, which could realize the monitoring of the primary working status of the seed meter. The system realized effective data transmission and data remote wireless transmission function, which provided sufficient theoretical and data support for the performance optimization of the seed meter, and laid a good foundation for the visualization and intelligence of information data.

The objective is to address the issue of excessive vibration in the cab of the combine harvester. This study addresses excessive cab vibration in the Linhai 4LZ-7.0 combine harvester by analyzing vibration signals under two working conditions using the Short-Time Fourier Transform. The results identified the vibrating screen and grass crusher as primary resonance sources, with maximum vibration along the X-axis. Simulation revealed that their first-order modal frequencies coincided with external excitation frequencies, causing resonance transmission to the cab. To resolve this, the driving pulleys of both components were redesigned and replaced. Post-modification testing showed a 90% reduction in the cab vibration level index from 1215 to 112, a 26% decrease in root mean square values, and the elimination of resonance peaks in frequency spectra. By modifying excitation frequencies to avoid structural resonance, cab vibration was effectively mitigated, significantly improving operational comfort. This paper is the first to pinpoint the primary resonance source and avert harvester resonance by altering its external excitation, delivering an effective, low-cost engineering fix for agricultural-machinery manufacturers; the abstract has been updated accordingly.

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.