Aiming to meet the growing need for automated harvesting, an automatic alignment method based on Euclidean clustering and K-means clustering is proposed to address issues of driver fatigue and inaccurate driving in manually operated corn harvesters. Initially, the corn field environment is scanned using LiDAR to obtain point cloud data, which are then subjected to pass-through filtering and statistical filtering to remove noise and non-corn contour points. Subsequently, Euclidean clustering and K-means clustering methods are applied to the filtered point cloud data. To validate the impact of Euclidean clustering on subsequent clustering, two separate treatments of the obtained point cloud data were conducted during experimental validation: the first used the K-means clustering algorithm directly, while the second involved performing Euclidean clustering followed by K-means clustering. The results demonstrate that the combined method of Euclidean clustering and K-means clustering achieved a success rate of 81.5%, representing a 26.5% improvement over traditional K-means clustering. Additionally, the Rand index increased by 0.575, while accuracy improved by 57% and recall increased by 61%.

This study theoretically analyzed the cutting process of castor and determined the structural parameters of the key component of the castor disc-cutting device, aiming to obtain the optimal operation parameter combination and reduce the cutting resistance and power consumption during the harvesting process. The effects of the cutting-disc thickness, cutting-disc rotational speed, feeding speed, and edge angle on the cutting power consumption were studied using an orthogonal rotation combination experiment. The response surface method was used to optimize the parameters, and the mathematical relationship model between the cutting power consumption and each factor was established to determine the optimal parameter combination for disc cutting. The simulation results showed that the optimal combination of cutting parameters was cutting-disc thickness of 3 mm, cutting-disc rotational speed of 550 r/min, feeding speed of 0.6 m/s, and edge angle of 20°. Under these conditions, the cutting power consumption was 1.20375 J. The test results were basically consistent with the model prediction results. Therefore, this study provided a theoretical basis and reference for the design and improvement of castor harvesters.

In China’s Yangtze River and Yellow River basin cotton-growing regions, the complex terrain, scattered planting areas, and poor adaptability of the existing machinery have led to a mechanized cotton harvesting rate of less than 10%. To address this issue, we designed a crawler chassis for a brush-roller cotton harvester. It is specifically tailored to meet the 76 cm row spacing agronomic requirement. We also conducted a theoretical analysis of the power transmission system for the crawler chassis. Initially, we considered the terrain characteristics of China’s inland cotton-growing regions and the current cotton agronomy practices. Based on these, we selected and designed the power system and chassis; then, a finite element static analysis was carried out on the chassis frame to ensure safety during operation; finally, field tests on the harvester’s operability, stability, and speed were carried out. The results show that the inverted trapezoidal crawler walking device, combined with a hydraulic continuously variable transmission and rear-drive design, enhances the crawler’s passability. The crawler parameters included a ground contact length of 1650 mm, a maximum ground clearance of 270 mm, a maximum operating speed of 6.1 km/h, and an actual turning radius of 2300 mm. The maximum deformation of the frame was 2.198 mm, the deformation of the walking chassis was 1.0716 mm, the maximum equivalent stress was 216.96 MPa, and the average equivalent stress of the entire frame was 5.6356 MPa, which complies with the physical properties of the selected material, Q235. The designed cotton harvester crawler chassis features stable straight-line and steering performance. The vehicle’s speed can be adjusted based on the complexity of the terrain, with timely steering responses, minimal compaction on cotton, and reduced soil damage, meeting the requirements for mechanized harvesting in China’s inland small plots.

In order to alleviate the problems of lack of research on threshing and cleaning equipment and poor operational performance of castor harvester, an axial-flow threshing and cleaning device was designed and evaluated for a roller brush type castor harvester. This paper introduces the overall machine structure and elaborates on the working principles of the castor threshing and cleaning device. It clarifies the design and analysis of key components such as the conveyor design, rod-tooth structure design, collision force analysis between the fruit and rod-tooth, concave sieve design, and guide plate design. The main indicators for evaluating the castor threshing and cleaning device include the impurity rate, damage rate, and separation loss rate. Based on the previous experimental research, the working parameters of castor threshing and cleaning device are tested and studied by using the Box–Behnken central combined test method. The three-factor three-level quadratic regression orthogonal test design is carried out based on the forward speed, roller rotational speed, and threshing gap of concave sieve. A response surface mathematical model was established, analyzing the impact of various factors on work quality and conducting comprehensive optimization of influencing factors. The experimental results indicate that the significance order of factors affecting the impurity rate was forward speed > roller rotational speed > threshing gap of concave sieve; the significance order for damage rate was roller rotational speed > threshing gap of concave sieve > forward speed; and the significance order for separation loss rate was roller rotational speed > forward speed > threshing gap of concave sieve. The field test results show that the optimal working parameter combination is forward speed of 0.87 m∙s⁻¹, roller rotational speed of 462 r∙min⁻¹, and threshing gap of concave sieve of 30 mm, with an impurity rate of 2.95%, a damage rate of 1.75%, and a separation loss rate of 0.49%. The research findings can provide references for the structural improvement and operational parameter optimization of the castor harvester’s threshing and cleaning device.

The harvesting of castor is highly seasonal and labor-intensive, necessitating a reliance on mature mechanical harvesting equipment. Castor harvesting machinery is a weak link in the castor industry chain, severely constraining the development of China’s castor industry. This article elaborates on the current status of the castor industry and the harvesting modes, which mainly include combined harvesting and segmented harvesting. It systematically summarizes the harvesting platforms, cleaning technologies, and shelling technologies of castor harvesting machinery in China and internationally. It analyzes the main structural forms and working principles of the harvesting platforms of castor harvesters. The cleaning technologies and different types of shelling technologies of harvesters are also analyzed and summarized. Finally, this article identifies the existing problems in castor harvesting in China and provides an outlook on future development trends. It suggests that China’s castor cultivation will strengthen the integration of agricultural machinery and agronomy, while harvesting will develop in the direction of combined harvesting. In the future, the focus will be on the development of high-efficiency harvesting, specialized low-loss shelling machines, and breakthroughs in key core technologies to promote the development of mechanized castor harvesting technology in China.

Automatic row guidance is important for efficient and low-loss corn harvesting. However, complex field conditions challenge reliable inter-row perception, while many deep learning models remain computationally demanding. To address this, a lightweight semantic segmentation network is proposed. A dataset covering challenging field conditions was constructed. Based on DeepLabV3+, GhostNetV2 was adopted to reduce computational cost, an SP-ASPP was designed to enhance the representation of elongated inter-row structures, and BiFormer was introduced to strengthen long-range contextual modeling. We jointly exploited directional multi-scale context and sparse long-range interactions to preserve continuous row-space structures under occlusion and background interference while maintaining a lightweight architecture. A composite loss combining Focal, Dice, and Boundary losses was further employed to improve region completeness and boundary localization. A navigation-line extraction algorithm was then developed to generate stable guidance paths. After structured pruning and TensorRT FP16 optimization, the model achieved an mIoU of 82.28% at 53.1 FPS on the edge platform. The extracted navigation line yielded a mean absolute lateral error of 4.2 cm and a mean absolute heading error of 2.17°. These results demonstrate that the proposed method provides accurate real-time navigation perception on resource-constrained hardware, supporting low-cost vision-based corn harvester guidance.