Quantitative estimation of rapeseed yield is important for precision crop management and sustainable agricultural development. Traditional manual measurements are inefficient and destructive, making them unsuitable for large-scale applications. This study proposes a canopy-volume estimation and yield-modeling framework based on unmanned aerial vehicle light detection and ranging (UAV-LiDAR) data combined with a HybridMC-Poisson reconstruction algorithm. At the early yellow ripening stage, 20 rapeseed plants were reconstructed in 3D, and field data from 60 quadrats were used to establish a regression relationship between plant volume and yield. The results indicate that the proposed method achieves stable volume reconstruction under complex canopy conditions and yields a volume–yield regression model. When applied at the field scale, the model produced predictions with a relative error of approximately 12% compared with observed yields, within an acceptable range for remote sensing–based yield estimation. These findings support the feasibility of UAV-LiDAR–based volumetric modeling for rapeseed yield estimation and help bridge the scale from individual plants to entire fields. The proposed method provides a reference for large-scale phenotypic data acquisition and field-level yield management.

Under the dual imperatives of environmental protection and sustainable socio-economic development, the high energy consumption of the manufacturing sector has become a significant barrier to its transition toward industrial ecological transformation. Integrating machine tool energy-saving technologies into engineering education serves as a pivotal strategy for enhancing energy efficiency and reducing operational consumption while fostering professionals who possess both sustainability awareness and technical expertise. Building on the core principles and future applications of energy-efficient machining, this study introduces a targeted curriculum reform. In response to challenges including cross-disciplinary content, heavy theoretical demands, and insufficient experiential learning, a blended teaching framework is designed, integrating virtual instruction with practical factory-based training. Implementation outcomes demonstrate that this reform significantly enhances students’ comprehension of energy-saving mechanisms and their capacity to apply sustainable concepts in practical engineering contexts. This study provides a reference for integrating energy-saving education into higher education under the framework of sustainable development. It also offers educational support for the manufacturing industry’s pursuit of low-carbon transition and industrial ecological development, demonstrating the important role of engineering education in advancing ecological civilization and contributing to the broader environmental governance system.

To address the excessive headland space occupation and pronounced vehicle body roll caused by traditional U-shaped turning paths during plant protection sprayer operations in soybean–maize intercropping systems, particularly in fragmented and irregular plots, this study proposes a two-way operation scheme for unmanned sprayers. An improved Reeds–Shepp (RS) curve-based hybrid coverage path planning (CCPP) method is developed to optimize headland turning in non-perpendicular boundary scenarios and generate full-coverage paths for irregular fields. Simulation and field experiments conducted on four plots with an average area of 0.42 had demonstrated that, compared with the conventional U-shaped path, the proposed method reduces the average reserved headland width by 35.21% and shortens the non-operational path length by 21.76%. Under the same path-tracking controller, the turning heading deviation and roll amplitude are reduced by 21.38% and 31.73%, respectively. The results indicate that the improved RS-based path planning method can effectively reduce headland space occupation and enhance the stability and operational efficiency of plant protection sprayers.

Pumpkin seedlings serve as rootstocks for watermelon grafting, and the partial leaf trimming operation performed approximately two days before grafting is crucial for the survival rate of grafted watermelon seedlings. Extracting the position of the main veins of the leaf is a prerequisite for achieving automated partial pruning. The existing methods have problems such as low segmentation accuracy and misclassification between primary and branch veins in the pumpkin seedling segmentation task. This study proposes a three-classification segmentation model Dynamic Region Enhancement Transformer (DRE-Former) of main vein, branch vein and background, as well as a post-processing system. The encoder of DRE-Former consists of two modules. The former is Dynamic Frequency Conv and Normalized Efficient Conv (DN Block), which can enhance the feature extraction ability for small targets. The latter is the Region Transformer Block, which enhances the ability to distinguish between the main vein and the branch vein. In addition, in the skip connection part of the model, a Skip Connection Fusion Block (SCF Block) has been added, which can reduce the dilution degree of detailed features. The post-processing section outputs the cutting position and cutting Angle through rule-based methods and geometric analysis. The experimental results show that the proposed model achieves mean Intersection-over-Union (mIoU) and Overall Accuracy (OA) of 90.80% and 95.88%, respectively, outperforming the comparative models. In stability and error testing, the average standard deviation is 0.60, and the average relative error is 11.90%. Compared with the primary mIoU data in the dataset, the average relative error differs by only 2.11%. The post-processing system enables the accurate determination of cutting positions and angles, but it has a strong dependence on the segmentation model. The research can provide reliable technical support for the subsequent automatic cutting equipment for pumpkin seedlings.

Sesame seeds are small, irregularly shaped, and poorly flowable, often causing insufficient filling and unstable discharge in external fluted-roller seed-metering devices. This study developed a sesame–companion-particle binary seed-metering system to improve metering stability. The discrete element method (DEM) was used to simulate particle motion, and the model was validated through YOLO11-based machine-vision experiments. Response surface methodology was then applied to evaluate the effects of mass mixing ratio, companion-particle density ratio B, and particle diameter on seed-metering performance. The normalized cumulative discharge trajectories from the experiments and simulations showed high agreement, with mean Pearson correlation coefficients exceeding 0.99 for sesame seeds, sorghum particles, and total particles. The recommended parameter combination was a sesame-to-companion-particle mass mixing ratio of 9.102:1, B = 1.315 (ρc = 1222.95 kg·m⁻³), and a companion-particle diameter of 1.636 mm. Under this combination, the proportion of strong force chains was 14.25 percentage points lower than the maximum among the investigated conditions. Appropriate matching of companion-particle parameters can restructure interparticle contacts, weaken strong force chains near the seed outlet, and improve sesame seed-metering stability and discharge-composition uniformity.