In response to the problems of low production efficiency and severe ring mold failure in traditional biomass particle forming machines. In this study, a biomimetic vertical ring mold biomass particle forming machine was proposed to deal with the issue based on a honeycomb ordered multi mold hole structure. The feature parameters of honeycomb individual geometric mold holes were extracted using image processing such as ultrafiltration operators, and the coordinates of the mold hole network were obtained; Meanwhile, a force model was constructed for the coupling effect of the ring mold, pressure roller, and material. The parameters of bionic ring die, die hole and press roller of particle molding machine are designed with material intake and extrusion pressure as design objectives. Eventually, the decision is to adopt a three pressure roller biomimetic ring mold structure, with an inner diameter of 550 mm, a thickness of 65 mm, a width of 150 mm, and a hole opening rate of 31.2%. Under the premise of ensuring productivity, the length of the feed cavity for the mold hole is 8mm, the side length of the regular hexagonal mold hole on the inner side of the ring mold is 6mm, and the thickness of the spacing between the connecting film holes is 1.5 mm. The prototype is made of corn straw with a particle size of 2-4 mm under greenhouse conditions, with a moisture content of 12% and a spindle speed of 100 r/min. According to the measurement standards of NY/T1881.7-2010 "Test Methods for Solid Biomass Formed Fuel", the particle density, bending strength, crushing resistance index, and specific energy consumption per ton of the prototype are measured. The results show that at the same rotational speed, the average energy consumption per ton of the prototype is 40.46 kW·h/t. The optimized biomimetic vertical ring mold biomass particle molding machine reduces the energy consumption by 10.08% compared to traditional ring mold molding. The particle fuel molding density was greater than or equal to 1.32 g/cm³, the deformation resistance pressure is 1.79 kN, the deformation resistance is increased by 40.9%, and the shattering resistance index is 99.4%. The study the indicators have reached the requirements of the standards and higher than that of traditional biomass pellet machine.

Wheat is one of the main food crops. Yield per unit area is one of the most important indexes in wheat production. The level of yield per unit area can greatly contribute to the national grain competitiveness. Among them, seeding quality directly dominated the wheat quality and yield level. The wide seedling and precision sowing can be expected to effectively improve the sowing quality of wheat. But the outer slot wheel seeding device cannot fully meet the wheat-wide belt seeding at present, due to the unstable displacement and low uniform seed distribution in the belt. In this study, a kind of precision wheat seeding device with staggered convex teeth was designed for the wide seedling belt, according to the outer slot wheel seeding device. The structure of the key parts was optimized to improve the seeding quality of the device. The continuous filling analysis was also carried out to derive the displacement formula. The key structural parameters were determined as the height and angle of the convex teeth influencing the consistent displacement and the uniform seeding. The simulation tests were then conducted to optimize the structure of the seeding device. First of all, the seeding tests were conducted to alleviate the influence of the staggered angle of the convex teeth. Different staggered angles of the convex teeth were arranged at different operating speeds. The test results showed that better consistent displacement and uniform seeding were achieved when the staggered angle of the convex teeth was 14°. Then, the center rotation combination simulation was carried out on these key structural parameters. The significant influencing factors on the consistent displacement cy and uniform seed distribution were ranked in the descending order of the convex tooth angle, the convex tooth height, and the operating speed, indicating all interactive effects. Response surface analysis showed that better displacement consistency and seeding uniformity were achieved when the convex tooth height was 5 mm and the convex tooth angle was 75°. The average coefficients of variation were 2.27% and 7.61%, respectively. The optimal combination of parameters was also tested in the bench test at the end of the simulation experiment. The bench test showed that the variation coefficients of simulated uniformity were less than 5%, indicating reliable and accurate optimization. A field comparison experiment was carried out between the precision seeding device of staggered spindle-belt wheat and the outer slot wheel seeding device, in order to further verify the simulation and bench test. The sowing capacity was set at 120, 150 and 180 kg/hm², respectively. The variation coefficients of sowing consistency, longitudinal sowing uniformity and transverse sowing uniformity were reduced by 0.99, 3.01, and 9.38 percentage points, respectively, compared with the outer slot wheel seeding device, which met the agronomic requirements of wheat sowing with the wide seedling belt. The key structural parameters were determined accurately on the discharge rate. The structure was also designed for the precision seed separator of zigzagging wide-seedling belt wheat. The cycle of structure optimization was reduced significantly by simulation. The finding can provide a strong reference for the design of wheat seeding device, in order to improve the seed uniformity.

Cutting the roots of sugarcane using cutters is a critical part of sugarcane harvesting, and the degree of breakage of the roots after cutting affects the germination and growth of sugarcane to a certain extent in the following year. However, the intricate interactions between the cutter and the stalk remain unclear. In order to fill this gap, this study first analyzed the conditions for no missed cuts during the operation of a double-disk cutter. Secondly, the research established a model of sugarcane stalk with anisotropy using the User-defined Material Mechanical Behavior (UMAT) subroutine based on the secondary development module of ABAQUS/Explicit. The cutting force curves obtained from simulation and test show a high correlation coefficient (R²=0.9621), indicating the reliability of the model of sugarcane stalk in mechanical transfer. Subsequently, the simulation test of the blade rotating cutting characteristics in this study indicates that at a blade tilt angle of 11.3°, a blade rotating speed of 659.3 r/min, and a forward speed of 1.5 km/h, the maximum shear force on the blade is the largest, while the maximum cutting force is the smallest. Finally, based on the simulation results, this paper discussed the internal factors affecting the breakage rate of sugarcane stalks and predicted the damage location and damage force of the stalks by studying the stress wave transmission effect. Additionally, it analyzed the effects of single-knife cutting and multi-cutting on stalk incisions. The results indicated that multi-cutting causes more damage to the stalks and increases the breakage rate of sugarcane. The results of this study can provide a theoretical basis and technical reference for exploring the reduction of sugarcane residual cutting rate.

The traditional recognition algorithm is prone to miss detection targets in the complex tea garden environment, and it is difficult to satisfy the requirement for tea bud recognition accuracy and efficiency. In this study, the YOLOv7 model was developed to improve tea bud recognition accuracy for some extreme tea garden scenarios. In the improved model, a lightweight MobileNetV3 network is adopted to replace the original backbone network, which reduces the size of the model and improves detection efficiency. The convolutional block attention module is introduced to enhance the attention to the features of small and occluded tea buds, suppressing the interference of the complex tea garden environment on tea bud recognition and strengthening the feature extraction capability of the recognition model. Moreover, to further improve recognition accuracy for dense and occlusive scenarios, the soft non-maximum suppression strategy is integrated into the recognition model. Experimental results show that the improved YOLOv7 model has the precision, recall, and mean average precision (mAP) values of 88.3%, 87.4%, and 88.5%, respectively. Compared with the Faster R-CNN, SSD, and original YOLOv7 algorithms, the mAP of the improved YOLOv7 model is increased by 7.4, 7.9, and 3.9 percentage points, respectively, and its recognition speed is also promoted by 94.9%, 46.2%, and 16.9%. The proposed model can rapidly and accurately identify the tea buds in multiple complex tea garden scenarios - such as dense distribution, being close to the background color, and mutual occlusion - with high generalization and robustness, which can provide theoretical and technical support for the recognition of tea-picking robots.

Efficient leaf azimuth angles and plant spacing are crucial for enhancing light interception efficiency in maize, thereby increasing yield per unit area. Traditional methods for measuring these traits are labor-intensive and prone to error. This study aimed to develop an accurate and efficient method for determining leaf azimuth angles and plant spacing in maize to improve understanding of field competition and support breeding programs. Utilizing light detection and ranging (Lidar) technology, 3D point cloud data of maize plants were collected, enabling effective 3D morphological reconstruction through multi-frame stitching. Principal component analysis (PCA) was employed to determine the leaf azimuth angles of individual maize plants. Additionally, a method based on point density analysis was developed to identify the central axis position of single maize plants. Specifically, point density in the neighborhood of each point in the maize point cloud was calculated, with the central axis determined along the direction of highest point density. The integration of PCA-based leaf azimuth detection and point density analysis provided a robust framework for accurately determining leaf azimuth angles and plant spacing. In the detection of leaf azimuth angles, this method achieved an R² of 0.87 and an RMSE of 5.19°. For plant spacing detection, the R² was 0.83 and the RMSE was 0.08 m. This approach facilitates parameterized modeling of field competition, significantly enhancing the efficiency of breeding programs by providing detailed and precise phenotypic data. Despite the high accuracy demonstrated by the proposed methods, further investigation is needed to evaluate their effectiveness under varying environmental conditions and across different maize varieties. Additionally, challenges related to partial occlusions and complex canopy structures may impact the accuracy of point cloud data analysis, necessitating further refinement of the algorithms.