The use of multi-mode remote sensing data for biomass prediction is of potential value to aid planting management and yield maximization. In this study, an advanced biomass estimation approach for sugarcane fields is proposed based on multi-source remote sensing data. Since feature interpretability in agricultural data mining is significant, a feature extraction method of deep archetypal analysis (DAA) that has good model interpretability is introduced and aided by principal component analysis (PCA) for feature mining from the multi-mode multispectral and light detection and ranging (LiDAR) remote sensing data pertaining to sugarcane. In addition, an integrated regression model integrating random forest regression, support vector regression, K-nearest neighbor regression and deep network regression is developed after feature extraction by DAA to precisely predict biomass of sugarcane. In this study, the biomass prediction performance achieved using the proposed integrated learning approach is found to be predominantly better than that achieved by using conventional linear methods in all the time periods of plant growth. Of more significance, according to model interpretability of DAA, only a small set of informative features maintaining their physical meanings (four informative spectral indices and four key LiDAR metrics) can be extracted which eliminates the redundancy of multi-mode data and plays a vital role in accurate biomass prediction. Therefore, the findings in this study provide hands-on experience to planters with indications of the key or informative spectral or LiDAR metrics relevant to the biomass to adjust the corresponding planting management design.

Rapid and accurate moisture detection is critical for alfalfa quality control, yet conventional methods are slow, and non-destructive techniques are challenged by different product forms. This study leveraged Short-Wave Infrared Hyperspectral Imaging (SWIR-HSI) to acquire spatially representative spectra, aiming to develop and validate robust, form-specific moisture prediction models for compressed and powdered alfalfa. For compressed alfalfa, a full-spectrum Support Vector Regression (SVR) model demonstrated stable and good performance (mean Prediction Coefficient of Determination ${\mathrm{R}}_{\mathrm{P}}^2$ = 0.880, Ratio of Performance to Deviation RPD = 2.93). In contrast, powdered alfalfa achieved superior accuracy (mean ${\mathrm{R}}_{\mathrm{P}}^2$ = 0.953, RPD = 5.29) using an optimized pipeline of Savitzky–Golay’s first derivative, Successive Projections Algorithm (SPA) for feature selection, and an SVR model. A key finding is that the optimal model for powdered alfalfa frequently converged to an ultra-sparse, single-band solution near water absorption shoulders (~970/1450 nm), highlighting significant potential for developing low-cost, filter-based agricultural sensors. While this minimalist model showed excellent average accuracy, rigorous repeated evaluations also revealed non-negligible performance variability across different data splits—a crucial consideration for practical deployment. Our findings underscore that tailoring models to specific product forms and explicitly quantifying their robustness is essential for reliable NIR sensing in agriculture and provides concrete wavelength targets for sensor development.

In this study, the wear characteristics of the die were tested and analyzed through compaction tests, and the distribution of wear depth along the direction toward the extrusion outlet was obtained. A discrete element method (DEM) model of the die’s wear process was established. The results show that the severe wear area is located near the stop position of the compression rod, forming a plow-shaped wear area along the extrusion direction, accompanied by fatigue peeling. The wear depth gradually decreases towards the extrusion outlet. The DEM model partially reveals the occurrence of the wear phenomenon, but the particle motion speed deviates from the actual situation. The maximum compression force value range during the DEM compression stage is within the actual maximum compression force value range, and the relative error range of the average maximum compression force is less than 2%. By verifying the formula to calibrate the model, the calibrated model is compared with the actual mold wear, and the predicted value is close to the actual test result. The DEM can be used to explore the wear mechanism and predict the die’s wear failure process, laying the foundation for optimizing die wear resistance design.

In order to reduce the exhaust noise, which is the main source of gasoline engine noise, this paper uses on orthogonal test to optimize the parameters of the main structure of split-stream rushing muffler (the perforation rate of the perforated pipe, perforation aperture of the perforated pipe, perforation rate of the perforated plate). It also comprehensively evaluates its acoustic and aerodynamic performances to determine the optimal structural model. The acoustic performance and aerodynamic performance of the split-stream rushing muffler and the original muffler are compared and analyzed in the simulation test. The results show that the average Transmission Loss of the split-stream rushing muffler is greater than that of the original muffler, and its Pressure Loss is also lower than that of the original muffler. Through the insertion loss experiment, it was found that the average insertion loss of the main muffling frequency band is 19.14 dB, which is higher than that of the original muffler, and the peak frequency of the insertion loss of the split-stream rushing muffler corresponds well with the peak frequency of the engine exhaust noise. The Pressure Loss of the split-stream rushing muffler was tested on the experiment bench, and the relative error between the experiment data and the simulation data is within 8%. In summary, the split-stream rushing muffler can effectively reduce the exhaust noise problem of gasoline engines.

The split-stream rushing exhaust muffler is a design that improves acoustic attenuation performance and reduces exhaust resistance by lowering the internal airflow velocity. In the past, researchers often applied a rigid treatment to the muffler wall when studying this new muffler and neglected the acoustic–structure coupling effect. As a result, differences existed between the calculated results and practical situations. This research employed COMSOL 6.2 software to perform finite element calculations on the acoustic performance of this muffler under acoustic–structural coupling. It analyzed the causes of transmission loss discrepancies with and without the acoustic–structural coupling effect, and the findings were validated experimentally. Building upon this foundation, further optimization and refinement of the muffler’s structural parameters were conducted. The results demonstrated that the transmission loss curve under the acoustic–structure coupling effect followed a similar trend to that observed without the acoustic–structure coupling effect. However, the transmission loss curve changed owing to the influence of the acoustic–structure coupling effect on sound pressure, which resulted in a 6.24% decrease in the average transmission loss. The transmission loss curve accounting for the acoustic–structure coupling effect aligned more closely with the test results than the curve that did not account for the coupling effect. Furthermore, this study delved into the influences of the wall thickness, inner tube diameter, and inner tube length on the muffler’s acoustic performance under acoustic–structural coupling. Subsequently, the muffler was optimized based on the findings.

Diesel engines employed in non-road machinery are significant contributors to nanoparticulate matters. This paper presents a novel device based on the principle of split-stream rushing to mitigate particulate matter emissions from these engines. By organizing and intensifying the airflow movement of the jet in the rushing region, the probability of collisions between nanoparticles is enhanced. This accelerates the growth and coagulation of nanoparticles, reducing the number density of fine particulate matter. This, in turn, facilitates the capture or sedimentation of particulate matter in the diesel engine exhaust aftertreatment system. The coagulation kernel function tailored for diesel engine exhaust nanoparticles is developed. Then, the particle balance equation is solved to investigate the evolution and coagulation characteristics. Afterwards, three-dimensional numerical simulations are performed to study the flow field characteristics of the split-stream rushing device and the particle evolution within it. The results show that the device achieves a maximum coagulation efficiency of 59.73%, increasing the average particle diameter from 96 nm to 121 nm. The particle number density uniformity index exceeded 0.93 in most flow regions, highlighting the effectiveness of the device in ensuring consistent particle distribution.