Global climate change has triggered frequent extreme weather events, leading to a significant increase in the frequency and intensity of forest fires. Traditional fire monitoring methods such as manual inspections, sensor technologies, and remote sensing satellites have limitations. With the advancement of drone technology and deep learning, using drones combined with artificial intelligence for fire monitoring has become mainstream. This paper proposes an improved YOLOv8-based model that incorporates local convolution instead of full convolution in the C2F module and integrates the EMA module to enhance the feature channel interaction modeling capability and contextual information utilization, thereby reducing model complexity and increasing efficiency. Additionally, in order to address the risk of false positives and missed detections caused by vegetation, terrain, and lighting changes in forests, we have introduced the AgentAttention module in the Backbone. This module combines Softmax and linear attention to optimize feature extraction, improving the model’s accuracy and robustness. Furthermore, in order to tackle the challenges of detecting flames and smoke at different scales and angles, we have designed the BiFormer module, which adaptively fuses global and local features, significantly enhancing the model’s multi-scale and multi-angle detection capability. Experimental results show that the improved model achieves Precision and Recall of 93.57% and 88.51%, respectively, representing improvements of 5.05% and 2.72% over the original model. It also optimizes FPS, GFLOPs, and Params by 14.3%, 25%, and 19.7%, respectively. This research has significant application prospects in forest fire early warning, emergency response, and loss reduction, while also providing strong technical support for forest resource protection and public safety.

In response to the numerous challenges faced by traditional human pose recognition methods in practical applications, such as dense targets, severe edge occlusion, limited application scenarios, complex backgrounds, and poor recognition accuracy when targets are occluded, this paper proposes a YOLO-Pose algorithm for human pose estimation. The specific improvements are divided into four parts. Firstly, in the Backbone section of the YOLO-Pose model, lightweight GhostNet modules are introduced to reduce the model’s parameter count and computational requirements, making it suitable for deployment on unmanned aerial vehicles (UAVs). Secondly, the ACmix attention mechanism is integrated into the Neck section to improve detection speed during object judgment and localization. Furthermore, in the Head section, key points are optimized using coordinate attention mechanisms, significantly enhancing key point localization accuracy. Lastly, the paper improves the loss function and confidence function to enhance the model’s robustness. Experimental results demonstrate that the improved model achieves a 95.58% improvement in mAP50 and a 69.54% improvement in mAP50-95 compared to the original model, with a reduction of 14.6 M parameters. The model achieves a detection speed of 19.9 ms per image, optimized by 30% and 39.5% compared to the original model. Comparisons with other algorithms such as Faster R-CNN, SSD, YOLOv4, and YOLOv7 demonstrate varying degrees of performance improvement.

The escalating global climate change significantly impacts the yield and quality of maize, a vital staple crop worldwide, especially during seedling stage droughts. Traditional detection methods are limited by their single-scenario approach, requiring substantial human labor and time, and lack accuracy in the real-time monitoring and precise assessment of drought severity. In this study, a novel early drought detection method for maize based on unmanned aerial vehicle (UAV) images and Yolov8+ is proposed. In the Backbone section, the C2F-Conv module is adopted to reduce model parameters and deployment costs, while incorporating the CA attention mechanism module to effectively capture tiny feature information in the images. The Neck section utilizes the BiFPN fusion architecture and spatial attention mechanism to enhance the model’s ability to recognize small and occluded targets. The Head section introduces an additional 10 × 10 output, integrates loss functions, and enhances accuracy by 1.46%, reduces training time by 30.2%, and improves robustness. The experimental results demonstrate that the improved Yolov8+ model achieves precision and recall rates of approximately 90.6% and 88.7%, respectively. The mAP@50 and mAP@50:95 reach 89.16% and 71.14%, respectively, representing respective increases of 3.9% and 3.3% compared to the original Yolov8. The UAV image detection speed of the model is up to 24.63 ms, with a model size of 13.76 MB, optimized by 31.6% and 28.8% compared to the original model, respectively. In comparison with the Yolov8, Yolov7, and Yolo5s models, the proposed method exhibits varying degrees of superiority in mAP@50, mAP@50:95, and other metrics, utilizing drone imagery and deep learning techniques to truly propel agricultural modernization.

In the context of rapidly advancing agricultural technology, precise and efficient methods for crop detection and counting play a crucial role in enhancing productivity and efficiency in crop management. Monitoring corn tassels is key to assessing plant characteristics, tracking plant health, predicting yield, and addressing issues such as pests, diseases, and nutrient deficiencies promptly. This ultimately ensures robust and high-yielding corn growth. This study introduces a method for the recognition and counting of corn tassels, using RGB imagery captured by unmanned aerial vehicles (UAVs) and the YOLOv8 model. The model incorporates the Pconv local convolution module, enabling a lightweight design and rapid detection speed. The ACmix module is added to the backbone section to improve feature extraction capabilities for corn tassels. Moreover, the CTAM module is integrated into the neck section to enhance semantic information exchange between channels, allowing for precise and efficient positioning of corn tassels. To optimize the learning rate strategy, the sparrow search algorithm (SSA) is utilized. Significant improvements in recognition accuracy, detection efficiency, and robustness are observed across various UAV flight altitudes. Experimental results show that, compared to the original YOLOv8 model, the proposed model exhibits an increase in accuracy of 3.27 percentage points to 97.59% and an increase in recall of 2.85 percentage points to 94.40% at a height of 5 m. Furthermore, the model optimizes frames per second (FPS), parameters (params), and GFLOPs (giga floating point operations per second) by 7.12%, 11.5%, and 8.94%, respectively, achieving values of 40.62 FPS, 14.62 MB, and 11.21 GFLOPs. At heights of 10, 15, and 20 m, the model maintains stable accuracies of 90.36%, 88.34%, and 84.32%, respectively. This study offers technical support for the automated detection of corn tassels, advancing the intelligence and precision of agricultural production and significantly contributing to the development of modern agricultural technology.

This study aims to enhance the localization of the inner and outer circles of the iris while addressing issues of excessive invalid computations and inaccuracies. To achieve this objective, diverse methods are employed to improve the process to varying extents. Initially, the image undergoes pre-processing operations, including grayscale conversion, mathematical morphological transformation, noise reduction, and image enhancement. Subsequently, the accurate localization of the inner and outer edges is achieved by applying algorithms such as Canny edge detection and the Hough transform, allowing for the determination of their corresponding center and radius values within the iris image. Lastly, an improvement is made to the particle swarm optimization algorithm by combining various algorithms, namely LinWPSO, RandWPSO, contraction factor, LnCPSO, and AsyLnCPSO, employing mechanisms such as simulated annealing and the ant colony algorithm. Through dual validation on the CASIA-Iris-Syn dataset and a self-built CASIA dataset, this approach significantly enhances the precision of iris localization and reduces the required iteration count.

Traditional plant phenotyping methods are typically constrained by several key limitations: insufficient segmentation accuracy, high computational costs, lack of multi-scale analysis capabilities, and ineffective modeling of complex nonlinear relationships. This study utilizes RGB imagery captured by Unmanned Aerial Vehicles (UAV), integrating plant phenotypic traits with soil moisture data, to quantitatively analyze the relationship between maize seedling phenotypes and soil moisture. Key morphological features of maize leaves—including connectivity, length, area, and shape characteristics—were transformed into matrix data using image segmentation, edge detection, and the Whale Optimization Algorithm (WOA). A quantitative relationship model between maize seedling phenotypes and soil moisture was established via regression analysis. To enhance segmentation accuracy further, we introduced the LSKNet module into YOLOv8, which optimizes leaf segmentation performance significantly under varying moisture stress conditions through multi-scale feature fusion and selective kernel convolution. The regression analysis results indicate high predictive accuracy under optimal moisture conditions (moisture content between 0.647 and 0.813), with an R2 of 0.869 and an RMSE of 0.0256. However, under drought conditions (moisture content between 0.246 and 0.354), prediction accuracy declines, with R2 decreasing to 0.851 and RMSE increasing to 0.0272. This study generates drought severity heatmaps using imagery from UAV, providing an efficient visualization tool for assessing maize plant health status. These heatmaps offer valuable references for early drought detection and precision irrigation decision-making, significantly advancing crop health management and the development of precision agriculture.