High-throughput measurements of phenotypic parameters in plants generate substantial data, significantly improving agricultural production optimization and breeding efficiency. However, these measurements face several challenges, including environmental variability, sample heterogeneity, and complex data processing. This study presents a method applicable to stem and leaf segmentation and parameter extraction during the tomato seedling stage, utilizing three-dimensional point clouds. Focusing on tomato seedlings, data was captured using a depth camera to create point cloud models. The RANSAC, region-growing, and greedy projection triangulation algorithms were employed to extract phenotypic parameters such as plant height, stem thickness, leaf area, and leaf inclination angle. The results showed strong correlations, with coefficients of determination for manually measured parameters versus extracted 3D point cloud parameters being 0.920, 0.725, 0.905, and 0.917, respectively. The root-mean-square errors were 0.643, 0.168, 1.921, and 4.513, with absolute percentage errors of 3.804%, 5.052%, 5.509%, and 7.332%. These findings highlight a robust relationship between manual measurements and the extracted parameters, establishing a technical foundation for high-throughput automated phenotypic parameter extraction in tomato seedlings.

Artificial supplemental lighting represents a crucial agricultural technique for enhancing plant growth and development, with researchers continuously investigating the effectiveness of various light sources in horticultural applications. Laser technology, characterized by its monochromatic nature, high coherence, and elevated energy density, presents a promising light source whose potential applications and underlying mechanisms in plant supplemental lighting remain to be thoroughly explored. To investigate the effects of different red-to-blue light ratios in laser supplemental lighting on rice (Oryza sativa L. cv. Jijing129) seedlings, we conducted a seedling-stage lighting experiment on the rice cultivar Jijing129 in a greenhouse using an LGI-660/450 dual-wavelength semiconductor laser system. The experimental design included a natural light control (AL) and three laser treatment groups, with red: blue (R:B) ratios and corresponding photon flux densities as follows: BL (50:50; 150:150 μmol m⁻² s⁻¹), CL (60:40; 180:120 μmol m⁻² s⁻¹), and DL (75:25; 225:75 μmol m⁻² s⁻¹). We systematically analyzed short-term morphological, physiological, and gene expression changes to elucidate the potential mechanisms underlying yield enhancement under different laser spectra. The results indicated that, compared to AL, all laser treatments (BL, CL, and DL) significantly increased root fresh weight, dry weight, and nitrogen content in seedlings. Furthermore, the final yield was significantly improved in all laser-treated groups, with the CL treatment exhibiting the highest yield. Transcriptome sequencing identified 10,497, 10,441, 10,700, and 10,757 expressed genes in the AL, BL, CL, and DL groups, respectively. Comparative analysis revealed 101, 1645, and 2247 differentially expressed genes (DEGs) in the BL/AL, CL/AL, and DL/AL comparisons, respectively. Gene Ontology (GO) enrichment analysis showed that these DEGs were significantly enriched in pathways such as metabolic processes, nitrogen metabolism, and protein amino acid phosphorylation. Notably, genes involved in the regulation of nitrogen compound metabolism were significantly upregulated in the CL and DL treatments. Further analysis of nitrogen metabolism and photosynthesis pathways revealed that laser irradiation induced the upregulation of specific genes. Interestingly, although physiological assays showed no significant changes in CAT, SOD, and POD activities, the expression of their corresponding genes was upregulated by laser treatment, suggesting these genes play a regulatory role during the supplemental lighting process. Therefore, our results indicated that laser supplemental lighting during the rice seedling stage increased the nitrogen content in plants and modulated the expression of related genes, and these changes might have been associated with the subsequent increase in rice yield. This study lays a foundation for understanding the molecular mechanisms of laser supplemental lighting and provides empirical support for the application of laser technology as an effective light source in agriculture.

Grain boundary segmentation in scanning electron microscope (SEM) images of pure copper presents substantial challenges for traditional image processing methods, including constrained segmentation precision and difficulties in identifying elongated grain boundaries and intricate topological structures. To overcome these constraints, this research introduces a comprehensive framework that integrates dataset development, advanced data augmentation, and model optimization to achieve precise grain boundary segmentation. This work proposes three principal innovations. First, a meticulously curated small-scale dataset, combined with a sophisticated adaptive data augmentation strategy, addresses data scarcity and ensures high-quality, robust training data. Second, the U-Net model was refined by incorporating a self-attention mechanism, markedly enhancing its capability to capture global contextual information and accurately detect complex grain boundary features. Third, an optimized stratified K-fold cross-validation method was implemented to ensure equitable data partitioning and reduce overfitting, thereby strengthening the model’s generalization capability. Experimental results demonstrate that the proposed framework delivers exceptional performance on the validation dataset, achieving a global accuracy of 0.96, a Dice coefficient of 0.91, and a mean Intersection over Union (mIoU) of 0.85. These metrics underscore significant advancements in grain boundary segmentation precision for polycrystalline metal systems. The framework validates the power of deep learning in microstructural characterization and establishes a reliable computational tool for quantitative metallographic analysis. It is well-positioned to extend to the microstructural analysis of a broad range of heterogeneous materials, enabling deeper insights into microstructure–property relationships in materials engineering.

Aiming at the safety and comfort of urban rail transit operation, a machine learning KNN algorithm is proposed to predict the degree of track irregularity. There are many fundamental factors that cause the track to be uneven, mainly reflected in the lateral irregularity of the track and the vertical irregularity. In this paper, the inertial navigation system is used to realize the acquisition of the orbital dynamic acceleration data, and the secondary displacement of the acceleration can obtain the relevant displacement information. The measurement of the gauge distance can be achieved by using a laser displacement sensor. By extracting the different displacement feature data of the same position at the same time and calculating the distance between the feature values, the purpose of predicting the degree of track damage can be achieved.