Semantic segmentation of large-scale point clouds in 3D computer vision is a challenging problem. Existing feature extraction modules often emphasize learning local geometry while not giving adequate consideration to the integration of color information. This limitation prevents the network from thoroughly learning local features, thereby impacting segmentation accuracy. In this study, we propose three modules for robust feature extraction and aggregation, forming a novel point cloud segmentation network (HFA-Net) for large-scale point cloud semantic segmentation. First, we introduce the Hybrid Feature Extraction Component (HFEC) and the Hybrid Bilateral Enhancement Component (HBAC) to comprehensively extract and enhance the geometric, color, and semantic information of point clouds. Second, we incorporate the Ternary-Distance Attention Pooling (TDAP) module, which leverages trilateral distances to further refine the network’s focus on various features, enabling it to emphasize both locally important features and broader local neighborhoods. These modules are stacked into dense residual components to expand the network’s receptive field. Our experiments on several large-scale benchmark datasets, including Semantic3D, Toronto3D, S3DIS and LASDU demonstrate the effectiveness of HFA-Net when compared to state-of-the-art networks.

Runner bean is an important food source worldwide, and effective disease prevention and control are crucial to ensuring food security. However, runner bean is vulnerable to various diseases during its growth, which significantly affect both yield and quality. Despite the continuous advancement of disease detection technologies, existing legume disease detection models still face significant challenges in identifying small-scale, irregular, and visually insignificant disease types, limiting their practical application. To address this issue, this study proposes an improved detection model, YOLOv8_RBean, based on the YOLOv8n object detection framework, specifically designed for runner bean leaf disease detection. The model enhances detection performance through three key innovations: (1) the BeanConv module, which integrates depthwise separable convolution and pointwise convolution to improve multi-scale feature extraction; (2) a lightweight LA attention mechanism that incorporates spatial, channel, and coordinate information to enhance feature representation; and (3) a lightweight BLBlock structure built upon DWConv and LA attention, which optimizes computational efficiency while maintaining high accuracy. Experimental results on the runner bean disease dataset demonstrate that the proposed model achieves a precision of 88.7%, with mAP50 and mAP50-95 reaching 83.5% and 71.3%, respectively. Moreover, the model reduces the number of parameters to 2.71 M and computational cost to 7.5 GFLOPs, representing reductions of 10% and 7.4% compared to the baseline model. Notably, the method shows clear advantages in detecting morphologically subtle diseases such as viral infections, providing an efficient and practical technical solution for intelligent monitoring and prevention of runner bean diseases.

Greenhouse gas emission is a key issue in the sustainable development of agriculture. To effectively predict the greenhouse gas emissions of beef cattle, a model is proposed based on system dynamics and greenhouse gas emission calculation methods, and a scenario is set as a ‘Straw to Beef’ project in Jilin Province. The model was built on a baseline emission scenario (feed precision: 60%, breeding environment: dry fattening farm, corn straw utilization: burning straw), with single- and comprehensive emission reduction scenarios considered, predicting trends and reduction potentials in greenhouse gas emissions from cattle breeding and straw burning in Jilin Province from 2013 to 2028, measured in CO₂-eq (CO₂ equivalent). The model also explored the impact of 11 controllable variables on greenhouse gas emissions. Results showed that (1) From 2013 to 2022, greenhouse gas emissions from straw burning and cattle breeding in Jilin Province increased significantly and had an annual growth rate of 6.51% in 2020. (2) Single emission reduction scenarios showed an increasing trend in greenhouse gas emissions, while comprehensive emission reduction scenarios showed a decreasing trend. Among them, the S2.2.1 scenario (feed precision: 80%, breeding environment: livestock barn manure pit, corn straw utilization: burning straw) had the strongest emission reduction ability in the single reduction scenario, the S3.2.2 scenario (feed precision: 80%, breeding environment: livestock barn manure pit, corn straw utilization: Feed-processing straw) had the strongest emission reduction ability in the comprehensive reduction scenario, reducing emissions by 5.10% and 69.24%, respectively, compared to the baseline scenario. This suggests that the comprehensive emission reduction scenarios which utilized straw resources reasonably can greatly reduce agricultural greenhouse gas emissions. (3) The optimal emission reduction scenario indicated that the higher the proportion of digestible energy in beef cattle’s total energy intake, the more perfect the fecal treatment process, and the higher the utilization rate of straw feed, the lower the greenhouse gas emissions. Therefore, to effectively reduce greenhouse gas emissions from cattle breeding and straw burning in Jilin Province, it is important to implement comprehensive emission reduction scenarios prioritizing the efficient utilization of straw resources and improving beef cattle management practices.

The classification of urban functional zones is crucial for improving land use efficiency and promoting balanced development across urban areas. Existing methods for classifying urban functional zones using mobile signaling data face challenges primarily due to the limitations of single data sources, insufficient utilization of multidimensional data, and inherent inaccuracies in mobile signaling data. To address these issues, this study proposes an innovative classification method that employs advanced multimodal data fusion techniques to enhance the accuracy and reliability of functional zone classification. Mobile signaling data are mapped into image data using timestamp and geographic location information and combined with point of interest (POI) data to construct a comprehensive multimodal dataset. Deep learning techniques are then applied to fuse the multimodal data features, enabling precise and reliable classification of functional zones. The experimental results demonstrate that this method achieves an accuracy of 95.128% in classifying urban functional zones, significantly outperforming methods that use single-modal data.

Forest canopy cover is a crucial indicator for measuring ecological functions. However, traditional plot-based measurement methods suffer from low efficiency and insufficient spatial continuity. Addressing issues in UAV RGB imagery—such as tree crown boundary adhesion, shadow interference, and texture confusion—this paper proposes a lightweight and edge-sensitive tree crown segmentation network. The model employs MobileNetV3-Large to replace the traditional U-Net encoder, significantly reducing parameter count and computational load while satisfying the potential for edge device deployment. In the decoding phase, a Semantic-guided Channel Compression and Focus (SCCF) module is designed to enhance semantic-guided channel compression and feature focusing. Furthermore, a Gradient-guided Morphological Tree Crown Attention Module (G-MTCAM) is proposed. By utilizing Gradient-Induced Center Difference Convolution (GI-CDC) and a variance-based statistical gating mechanism, this module constructs a dual-stream architecture for morphology and texture interaction, achieving precise cutting of tree crown boundaries and effective filtering of background noise. Additionally, a boundary-enhanced composite loss function is introduced to improve the accuracy of crown edge identification. Experimental results indicate that the proposed model achieves an IoU, Acc, and F1 score of 88.59%, 88.62%, and 93.77%, respectively. Compared to the classic U-Net, these represent improvements of 2.77%, 1.71%, and 1.44%, while the parameter count and computational cost are only 5.98 M and 6.71 GFLOPs. The forest Canopy Cover (CC) estimated based on the segmentation results shows high consistency with ground-based near-zenith canopy hemispherical percentage ($CH{P}_{0–30}$, denoted as $C{C}_{obs}$), with a correlation coefficient ($R^2$) exceeding 0.90. This verifies the effectiveness of the method in forest canopy structure monitoring and provides technical support for the application of consumer-grade UAVs in forestry surveys.