Imbalance-induced vibration of the threshing drum is a common problem during rice harvesting, especially when rice straw becomes entangled with the threshing components. In this study, a permanent magnet self-locking electromagnetic balancing device was designed for a combine harvester threshing drum. Field vibration tests were first carried out to identify the main rotation-related vibration components of the drum. The measured lateral response showed a clear peak at about 45.166 Hz, which was close to the third harmonic of the drum rotational frequency. Additional peaks were also observed near 60 Hz and 90 Hz. Based on these field observations, a two-disc balancing device was developed. The counterweight discs were locked by the magnetic attraction between permanent magnets and ferromagnetic plates when no adjustment was required, and they were repositioned by electromagnetic excitation during compensation. Finite element analysis showed that a 1 mm air gap provided a suitable compromise between magnetic locking and electromagnetic actuation. At this gap, the axial forces on the left and middle ferromagnetic plates were −37.033 N and 37.073 N, respectively. In the bench test, the calculated adjustment angles of the two counterweight discs were 6.938° and −6.411°. Under the prescribed bench-test condition, the vibration amplitude decreased from 55.41 m·s⁻² to 20.00 m·s⁻² after compensation, corresponding to an observed reduction of 63.9%. The results demonstrate the feasibility of the proposed balancing device under the tested bench condition and provide a basis for further repeatability and field-performance evaluation.

Bolted connections in combine-harvester vibrating screens are affected by reciprocating screen motion, frame vibration, and intermittent impact, making weak local vibration components difficult to distinguish from background fluctuations. This study used particle swarm optimization to adaptively select the wavelet basis, decomposition level, and thresholding rule for vibration signal denoising. Triaxial acceleration signals were collected under tightened- and loosened-bolt conditions. Gaussian white noise with input signal-to-noise ratios from 0 to 14 dB was added to evaluate parameter selection under controlled noise levels. The selected wavelet basis and decomposition level varied with the input signal-to-noise ratio. Three or four decomposition levels were selected under stronger noise, whereas two levels were generally sufficient at higher signal-to-noise ratios. Soft thresholding was selected in all tested cases. The optimized method reduced random fluctuations while retaining the main waveform trend in simulated noisy signals. For measured loosened-bolt signals, it reduced background fluctuation and retained local waveform changes.

Industrial chili peppers are specialized varieties primarily used for the extraction of capsaicinoids and paprika red. Their post-harvest processing level directly affects product quality and industrial economic benefits. Most existing studies have focused on a single unit operation or on edible chili peppers, and a systematic review of the entire post-harvest processing chain for industrial chili peppers is still lacking. Taking the standardized post-harvest processing workflow of industrial chili peppers as its core theme, this paper systematically reviews the current research approaches and application status of industrial chili pepper post-harvest processing technologies across six core unit operations, namely cleaning and impurity removal, grading and sorting, drying, stem and seed removal, crushing and grinding, and extraction of bioactive compounds. The analysis indicates that the field currently faces four common challenges: the lack of standardized processing parameters for classified processing, relatively low drying energy efficiency, insufficient online sensing and intelligent collaborative control, and a scarcity of industrial-scale validation for emerging technologies. This paper further constructs a technical route and technology evaluation framework for the entire post-harvest processing chain of industrial chili peppers, clarifies the applicable boundaries and scale suitability of different processing technologies, and provides a theoretical basis for industrial technological upgrading and process selection.

Garlic, a significant global specialty economic crop, is currently facing severe challenges from labor shortages and escalating production costs. Achieving full-process mechanized production is the core approach to ensuring sustainable industrial development and enhancing international competitiveness. This paper systematically reviews the research progress and application status of mechanized equipment throughout the entire crop cycle of garlic production, including seeding, field management, harvesting, and post-harvest processing and sorting. The study reveals that garlic equipment is undergoing a profound transformation from traditional mechanization to “opto-electro-mechanical integration” and intelligence. In the seeding phase, breakthroughs have been made in pneumatic precision seed-metering and machine vision-based clove bud orientation technologies, significantly improving the quality of upright planting. In field management, precise variable-rate application and targeted weeding have been preliminary realized through plant protection Unmanned Aerial Vehicle (UAV) downwash airflow field simulation (CFD) and deep learning-based image segmentation. In the harvesting phase, relying on 3D Discrete Element Method (3D-DEM) soil-cutting simulation and adaptive profile root-trimming technology, the industry is accelerating the transition from inefficient segmented harvesting to low-damage combined harvesting. In the post-harvest phase, hyperspectral imaging (HSI) and multi-label convolutional neural networks (CNNs) have been utilized to achieve high-speed non-destructive detection of internal and external quality. However, industry still faces critical bottlenecks such as the insufficient integration of machinery and agronomy, poor robustness of intelligent perception algorithms in complex environments, and high damage rates of core soil-engaging components. Future research should focus on lightweight algorithm deployment, digital twin-driven virtual prototyping, and the construction of regional standardized machinery–agronomy systems, aiming to build an efficient and universal intelligent production closed-loop for garlic.

Mechanized harvesting of subsurface crops has long been confronted with the critical engineering dilemmas of high damage rates and high impurity rates. Traditional taxonomic classification methods based on botanical families and genera fail to provide effective guidance for the engineering research and development of harvesting machinery. From an engineering perspective, this paper proposes a novel classification logic that categorizes subsurface crops into three major types based on their soil burial depth and physical distribution characteristics: shallow-soil clustered growth type (0–20 cm), mid-soil scattered growth type (20–40 cm), and deep-soil vertically rooted type (>40 cm). The harvesting bottlenecks of representative crops within these strata, including potato, onion, peanut, sweet potato, cassava, and yam, are systematically elucidated. Furthermore, this review provides an in-depth analysis of the current state of frontier core technologies, such as bionic drag reduction excavation, flexible multi-stage separation, microscopic discrete element method (DEM) simulation, kinematic optimization, and AI-based visual perception. This paper aims to reveal the common bottlenecks in subsurface crop harvesting and prospect future developmental trends centered on the deep integration of machinery and agronomy as well as intelligent perception and adaptation, thereby providing a solid theoretical foundation and engineering reference for the innovation of global agricultural machinery.

Agricultural threshing mixtures are moist, heterogeneous, and dynamically disturbed, conditions that reduce the selectivity of cleaning systems based solely on size, density, and aerodynamic response. Electrostatic separation introduces an additional property dimension, but its application to raw paddy rice remains constrained by moisture-sensitive charging, irregular particle motion, wall deposition, and variable feed conditions. This critical review synthesizes rice-specific studies on dielectric and material-state sensing with evidence concerning particle charging, electro-aerodynamic transport, multiphysics modelling, and process control. The available literature shows that dielectric measurements can characterize moisture- and density-dependent material states, but they do not directly quantify charge retention or electrostatic separability. Rice-specific evidence remains concentrated in dielectric characterization and machinery operating conditions, whereas electrostatic processing studies mainly concern processed rice-derived materials or analogous particle systems. Direct evidence from raw paddy threshing mixtures under continuous and field-relevant operating conditions remains limited. Based on the evidence, a four-zone framework is proposed comprising feed dispersion, charge acquisition, electro-aerodynamic deflection, and outlet collection. The framework identifies the measurements, model parameters, and validation steps required to relate dielectric state, charge decay, particle trajectories, and control actions, thereby providing a research basis for the development and rice-specific validation of electrostatic cleaning systems.