Soil pH is an essential indicator for assessing soil quality and soil health. In this study, based on the Chinese farmland soil survey dataset and meteorological dataset, the spatial distribution characteristics of soil pH in coastal eastern China were analyzed using kriging interpolation. The relationships between hydrothermal conditions and soil pH were explored using regression analysis with mean annual precipitation (MAP), mean annual temperature (MAT), the ratio of precipitation to temperature (P/T), and the product of precipitation and temperature (P*T) as the main explanatory variables. Based on this, a model that can rapidly estimate soil pH was established. The results showed that: (a) The spatial heterogeneity of soil pH in coastal eastern China was obvious, with the values gradually decreasing from north to south, ranging from 4.5 to 8.5; (b) soil pH was significantly correlated with all explanatory variables at the 0.01 level. In general, MAP was the main factor affecting soil pH (r = −0.7244), followed by P/T (r = −0.6007). In the regions with MAP < 800 mm, soil pH was negatively correlated with MAP (r = −0.4631) and P/T (r = −0.7041), respectively, and positively correlated with MAT (r = 0.6093) and P*T (r = 0.3951), respectively. In the regions with MAP > 800 mm, soil pH was negatively correlated with MAP (r = −0.6651), MAT (r = −0.5047), P/T (r = −0.3268), and P*T (r = −0.5808), respectively. (c) The estimation model of soil pH was: y = 23.4572 − 6.3930 × lgMAP + 0.1312 × MAT. It has been verified to have a high accuracy (r = 0.7743, p < 0.01). The mean error, the mean absolute error, and the root mean square error were 0.0450, 0.5300, and 0.7193, respectively. It provides a new path for rapid estimation of the regional soil pH, which is important for improving the management of agricultural production and slowing down soil degradation.

As sixth-generation mobile communication (6G) advances towards ultra-high speed and global coverage, visible light communication (VLC) has emerged as a crucial complementary technology due to its ultra-high bandwidth, low power consumption, and immunity to electromagnetic interference. Modulation format recognition (MFR) plays a vital role in the dynamic optimization and adaptive transmission of VLC systems, significantly influencing communication performance in complex channel environments. This paper systematically reviews the research progress in MFR for VLC, comparing the theoretical frameworks and limitations of traditional likelihood-based (LB) and feature-based (FB) methods. It also explores the advancements brought by deep learning (DL) technology, particularly in enhancing noise robustness, classification accuracy, and cross-scenario adaptability through automatic feature extraction and nonlinear mapping. The findings indicate that DL-based MFR substantially enhances recognition performance in intricate channels via multi-dimensional feature fusion, lightweight architectures, and meta-learning paradigms. Nonetheless, challenges remain, including high model complexity and a strong reliance on labeled data. Future research should prioritize multi-domain feature fusion, interdisciplinary collaboration, and hardware–algorithm co-optimization to develop lightweight, high-precision, and real-time MFR technologies that align with the 6G vision of space–air–ground–sea integrated networks.

The diversity and complexity of the agricultural environment pose significant challenges for the collection of pest and disease data. Additionally, pest and disease datasets often suffer from uneven distribution in quantity and inconsistent annotation standards. Enhancing the accuracy of pest and disease recognition remains a challenge for existing models. We constructed a representative agricultural pest and disease dataset, FIP6Set, through a combination of field photography and web scraping. This dataset encapsulates key issues encountered in existing agricultural pest and disease datasets. Referencing existing bounding box regression (BBR) loss functions, we reconsidered their geometric features and proposed a novel bounding box similarity comparison metric, DDRIoU, suited to the characteristics of agricultural pest and disease datasets. By integrating the focal loss concept with the DDRIoU loss, we derived a new loss function, namely Focal-DDRIoU loss. Furthermore, we modified the network structure of YOLOV7 by embedding the MobileViTv3 module. Consequently, we introduced a model specifically designed for agricultural pest and disease detection in precision agriculture. We conducted performance evaluations on the FIP6Set dataset using mAP75 as the evaluation metric. Experimental results demonstrate that the Focal-DDRIoU loss achieves improvements of 1.12%, 1.24%, 1.04%, and 1.50% compared to the GIoU, DIoU, CIoU, and EIoU losses, respectively. When employing the GIoU, DIoU, CIoU, EIoU, and Focal-DDRIoU loss functions, the adjusted network structure showed enhancements of 0.68%, 0.68%, 0.78%, 0.60%, and 0.56%, respectively, compared to the original YOLOv7. Furthermore, the proposed model outperformed the mainstream YOLOv7 and YOLOv5 models by 1.86% and 1.60%, respectively. The superior performance of the proposed model in detecting agricultural pests and diseases directly contributes to reducing pesticide misuse, preventing large-scale pest and disease outbreaks, and ultimately enhancing crop yields. These outcomes strongly support the promotion of sustainable agricultural development.

Due to complex backgrounds, significant scale variations of targets, and dense distributions of small objects in Unmanned Aerial Vehicle (UAV) aerial images, traditional object detection algorithms face challenges in adapting to such scenarios. This article introduces a drone detection model, MEP-YOLOv5s, which optimizes the Backbone, Neck layer, and C3 module based on YOLOv5s, and combines effective attention mechanisms to improve the training efficiency of the model by replacing the traditional CIoU loss (Complete Intersection over Union) with MPDIoU (Minimum Point Distance-based Intersection over Union) loss. This model demonstrates an excellent performance in handling typical drone detection scenarios, especially for small and dense objects. To holistically balance the detection accuracy and inference efficiency, we propose a Comprehensive Performance Indicator (CPI), which evaluates the model performance by considering both accuracy and efficiency. Evaluations on the VisDrone2019 dataset demonstrate that MEP-YOLOv5s achieves a 3.3% improvement in precision (P), a 20.9% increase in mAP@0.5, and a 19.86% gain in the CPI ($\mathsf{\alpha }$ = 0.5) compared with the baseline model. Additional experiments on the NWPU VHR-10 dataset confirm that MEP-YOLOv5s outperforms the existing state-of-the-art methods, offering a robust solution for UAV-based small object detection with enhanced feature extraction and attention-driven adaptability.