The digging depth is an important factor affecting the mechanized garlic harvesting quality. At present, the digging depth of the garlic combine harvester (GCH) is adjusted manually, which leads to disadvantages such as slow response, poor accuracy, and being very dependent on the operator’s experience. To solve this problem, this paper proposes a machine vision-based automatic digging depth control system for the original garlic digging device. The system uses the improved YOLOv5 algorithm to calculate the length of the garlic root at the front end of the clamping conveyor chain in real-time, and the calculation result is sent back to the system as feedback. Then, the STM32 microcontroller is used to control the digging depth by expanding and contracting the electric putter of the garlic digging device. The experimental results of the presented control system show that the detection time of the system is 30.4 ms, the average accuracy of detection is 99.1%, and the space occupied by the model deployment is 11.4 MB, which suits the design of the real-time detection of the system. Moreover, the length of the excavated garlic roots is shorter than that of the system before modification, which represents a lower energy consumption of the system and a lower rate of impurities in harvesting, and the modified system is automatically controlled, reducing the operator’s workload.

In order to improve the operational performance of the self-developed 4HLB-4 half-feed four-row peanut combine harvester, single-factor and multifactor field tests were conducted on key operational parameters affecting the quality of harvesting. The response surface methodology was used to study the effects of the forward speed, clamping height, peanut picking roller rotational speed, and cleaning sieve vibrational frequency on the loss rate and impurity rate. A response surface regression model of the loss rate and impurity rate was constructed. A multi-objective integrated optimization was carried out for each factor. The results showed that, in terms of the order of significance, the influence of each factor on the impurity rate was as follows: cleaning sieve vibrational frequency > clamping height > forward speed > peanut picking roller rotational speed. The order of significance of the influence of each factor on the loss rate was as follows: peanut picking roller rotational speed > forward speed > clamping height > cleaning sieve vibrational frequency. The optimal combination of parameters was a forward speed of 0.85 m/s, clamping height of 190 mm, peanut picking roller rotational speed of 550 rpm, and cleaning sieve vibrational frequency of 590 cpm. Under these conditions, the impurity rate of 2.62% and the loss rate of 2.05% were obtained, which effectively reduced the impurity rate and loss rate and met the quality requirements of Chinese peanut mechanized harvesting operations. The results of this study can provide a basis for the improvement and optimization of the 4HLB-4 half-feed four-row peanut combine harvester and the optimization of its operating parameters.

Introduction: Root cutting is an important process in garlic field harvesting but is the weakest link in the full mechanization of garlic production. To improve the current situation of technological backwardness and poor operational quality of mechanized garlic root-cutting in the main garlic-producing regions of China, this study combined the physical characteristics and agronomic requirements of garlic plants, and proposed an innovative floating root-cutting technology for garlic combine harvesters that enables the top alignment of bulb, adaptive profiling floating of cutter, and embedded cutting of roots.Methods: Through the kinematic analysis of the floating cutting process, the coordinate equations of the initial contact point of the bulb, the mathematical model of the floating displacement of the cutting component. Using computer simulation techniques, the dynamic simulation study of the floating cutting process was carried out in the rigid-flexible coupling numerical simulation model of root-cutting mechanism and garlic plant. The influence law of garlic conveying speed, extension spring preload force and stiffness on the floating displacement of the cutting component and the angular velocity of swing arm reset and its formation causes were analyzed by a single-factor simulation test. The key operating parameters of the root-cutting mechanism were optimized through the computerized virtual orthogonal test and fuzzy comprehensive evaluation.Results and discussion: The significance of the factors affecting the floating cutting performance decreased in the following order: extension spring preload force, garlic conveying speed and extension spring stiffness. The optimal parameter combination of the root cutting mechanism obtained from the optimization were as follow: extension spring preload force was 16 N, garlic conveying speed was 0.8 m/s, and extension spring stiffness was 215 N/m. Tests conducted with the optimal parameter combination yielded a root excision rate of 92.72%, which meets the requirements of Chinese garlic field harvesting quality. This study provides computer simulation optimization methods for the optimal design of the root-cutting mechanism, and also provides technical and equipment support for the full mechanization of garlic production in China.

Driven by agricultural labor shortages and rising quality requirements, ginger harvesting increasingly demands high-throughput, low-damage operations and a reliable supply chain. This review summarizes harvesting modes and harvester types used in ginger production, with emphasis on critical process modules: digging and lifting, soil disintegration and cleaning, vine cutting and anti-tangling, gentle conveying, and collection. We compare major technical routes in terms of field capacity, control of soil and foreign materials, damage mitigation, and reliability under continuous operation, and identify the conditions under which each route performs best. Drawing on advances in harvesting systems for other root and bulb crops, we outline transferable approaches for intelligent sensing, precision control, and system-level integration. We then propose an online monitoring and closed-loop regulation framework for strongly coupled conditions, such as heavy clay soils, plastic-mulch residues, and vine interference. Key bottlenecks include limited cross-regional adaptability, persistent trade-offs between low damage and high throughput, cost constraints on intelligent functions, and the lack of shared datasets and standardized evaluation protocols. Future progress should be anchored in integrated equipment sets and supporting operating specifications, guided by multi-source sensing-based quality indicators and interpretable control strategy libraries, to reduce harvest losses, stabilize marketable quality, improve operational efficiency, and enable scalable adoption.

Agricultural production in hilly and mountainous areas is characterized by fragmented fields, frequent slope variations, narrow farm roads, and highly variable soil bearing and adhesion conditions, which impose distinctive requirements on the traction and mobility, slope stability, maneuverability, and operational adaptability of agricultural power machinery. Focusing on tractors and related power chassis, this review synthesizes research conducted in China and elsewhere from two complementary perspectives: equipment configurations and key terrain-adaptive technologies. Research progress is reviewed for wheeled tractors, crawler tractors, slope-leveling and other special-configuration tractors, micro-tillers, and lightweight power platforms, with particular emphasis on lightweight and overall structural design, traction and machine–soil interaction, slope stability and attitude adjustment, high-maneuverability steering, powertrain and transmission systems, and intelligent operation control. The reviewed studies indicate a transition from compact, low-center-of-gravity, and fixed-configuration designs toward active leveling, continuously variable transmission, multimode steering, vehicle-implement coordination, and intelligent autonomous operation. However, pronounced trade-offs remain between lightweight design and traction capacity, high maneuverability and slope stability, and high terrain mobility and low soil disturbance. The reliability, economic viability, and long-term performance of many specialized machines under complex operating conditions also require further validation. Based on the reviewed evidence, the evolution of terrain adaptation is summarized into three stages: passive terrain adaptation, coordinated terrain adaptation, and active terrain adaptation. Future research should focus on lightweight and reconfigurable design, active slope stabilization, efficient power transmission, autonomous operation in complex terrain, and machine–soil–crop coordination, thereby advancing power machinery from fixed-structure adaptation toward multisystem coordination and ultimately perception-driven active adaptation.