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.

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.

Accurate semantic segmentation of high-resolution remote sensing imagery is crucial for applications such as land cover mapping, urban development monitoring, and disaster response. However, remote sensing data still present inherent challenges, including complex spatial structures, significant intra-class variability, and diverse object scales, which demand models capable of capturing rich contextual information from both local and global regions. To address these issues, we propose ArgusNet, a novel segmentation framework that enhances multi-scale representations through a series of carefully designed fusion mechanisms. At the core of ArgusNet lies the synergistic integration of Adaptive Windowed Additive Attention (AWAA) and 2D Selective Scan (SS2D). Specifically, our AWAA extends additive attention into a window-based structure with a dynamic routing mechanism, enabling multi-perspective local feature interaction via multiple global query vectors. Furthermore, we introduce a decoder optimization strategy incorporating three-stage feature fusion and a Macro Guidance Module (MGM) to improve spatial detail preservation and semantic consistency. Experiments on benchmark remote sensing datasets demonstrate that ArgusNet achieves competitive and improved segmentation performance compared to state-of-the-art methods, particularly in scenarios requiring fine-grained object delineation and robust multi-scale contextual understanding.