The effective control of trees in transmission line corridors is crucial to mitigate the damage that they can cause to transmission lines. Investigating trees in these corridors presents a significant challenge, particularly in classifying individual tree species. Although the current deep learning model can segment single tree species, it exhibits low recognition accuracy in areas with dense forest canopies. The detection speed is also subject to limitations. To address these challenges, this study relies on aerial multispectral images obtained from drones as the primary data source. The process begins by extracting single tree crowns and establishing a sample dataset, divided in a 9:1 ratio into training and verification sets. Subsequently, the training set undergoes iterative parameter training using the YOLO v7 network. Once optimal parameters are obtained, the system outputs information on individual tree types. The verification sample set is then employed to assess the accuracy. Simultaneously, the YOLO v4 network model is applied to the same data, and the training results of the YOLO v7 network are compared and analyzed, revealing peak accuracy of 85.42% in recognizing single tree species. This approach provides an effective solution, offering reliable data for the in-depth investigation of trees in transmission line corridors and the accurate monitoring of concealed tree hazards.

Pine wilt disease (PWD) poses a significant threat to global pine resources because of its rapid spread and management challenges. This study uses high-resolution helicopter imagery and the deep learning model You Only Look Once version 7 (YOLO v7) to detect symptomatic trees in forests. Attention mechanism technology from artificial intelligence is integrated into the model to enhance accuracy. Comparative analysis indicates that the YOLO v7-SE model exhibited the best performance, with a precision rate of 0.9281, a recall rate of 0.8958, and an F1 score of 0.9117. This study demonstrates efficient and precise automatic detection of symptomatic trees in forest areas, providing reliable support for prevention and control efforts, and emphasizes the importance of attention mechanisms in improving detection performance.

Single tree detection is essential in forest resource surveys. Efficient, accurate, and rapid extraction of individual trees facilitates the timely acquisition of forest resource information. Traditional tree surveys rely on manual field measurements, which are limited in coverage, costly, and time-consuming. To address these issues, this study utilizes multispectral imagery captured by unmanned aerial vehicles (UAVs) as the data foundation and applies deep learning methods for tree species segmentation in the study area. An enhanced ECA-Unet model, derived from the U-net architecture, integrates a channel attention mechanism in the Decoder to augment the model’s accuracy and precision in small-scale UAV-based tree segmentation, attaining an overall accuracy of 85.87%. The performance of the suggested model was compared with that of conventional semantic segmentation models within the same study region. Compared with the original U-net model, the model in this study improved the mAP and Accuracy by 2.1% and 1.23%, respectively, in terms of tree species segmentation accuracy. Compared with the traditional semi-automatic forest crown segmentation method, the model in this paper can output forest single tree segmentation results more quickly and effectively. The findings indicate that the proposed technique yields optimal segmentation accuracy and performance for individual trees within the dataset, facilitating the rapid and exact differentiation of trees in wooded settings utilizing limited sample data.

The vertical structure of forests, including the measurement of canopy height, helps researchers understand forest characteristics such as density and growth stages. It is one of the key variables for estimating forest biomass and is crucial for accurately monitoring changes in forest carbon storage. However, current technologies face challenges in achieving cost-effective, accurate measurement of canopy height on a widespread scale. This study introduces a method aimed at extracting accurate forest canopy height from The Global Ecosystem Dynamics Investigation (GEDI) data, followed by a comprehensive large-scale analysis utilizing this approach. Before mapping, verifying and analyzing the accuracy and sensitivity of parameters that may affect the precision of GEDI data extraction, such as slope, aspect, and vegetation coverage, can aid in assessment and decision-making, enhancing inversion accuracy. Consequently, a random forest method based on parameter sensitivity analysis is developed to break through the constraints of traditional issues and achieve forest canopy height inversion. Sensitivity analysis of influencing parameters surpasses the uniform parameter calculation of traditional methods by differentiating the effects of various land use types, thereby enhancing the precision of height inversion. Moreover, potential factors affecting the accuracy of GEDI data, such as vegetation cover density, terrain complexity, and data acquisition conditions, are thoroughly analyzed and discussed. Subsequently, large-scale forest canopy height estimation is conducted by integrating vegetation cover Normalized Difference Vegetation Index (NDVI), sun altitude angle and terrain data, among other variables, and accuracy validation is performed using airborne LiDAR data. With an R² value of 0.64 and an RMSE of 8.62, the mapping accuracy underscores the resilience of the proposed method in delineating forest canopy height within the Changbai Mountain forest domain.

The tasseling stage of maize, as a critical period of maize cultivation, is essential for predicting maize yield and understanding the normal condition of maize growth. However, the branches overlap each other during the growth of maize seedlings and cannot be used as an identifying feature. However, during the tasseling stage, its apical ear blooms and has distinctive features that can be used as an identifying feature. However, the sizes of the maize tassels are small, the background is complex, and the existing network has obvious recognition errors. Therefore, in this paper, unmanned aerial vehicle (UAV) RGB images and an improved YOLOv8 target detection network are used to enhance the recognition accuracy of maize tassels. In the new network, a microscale target detection head is added to increase the ability to perceive small-sized maize tassels; In addition, Spatial Pyramid Pooling—Fast (SPPF) is replaced by the Spatial Pyramid Pooling with Efficient Layer Aggregation Network (SPPELAN) in the backbone network part to connect different levels of detailed features and semantic information. Moreover, a dual-attention module synthesized by GAM-CBAM is added to the neck part to reduce the loss of features of maize tassels, thus improving the network’s detection ability. We also labeled the new maize tassels dataset in VOC format as the training and validation of the network model. In the final model testing results, the new network model’s precision reached 93.6% and recall reached 92.5%, which was an improvement of 2.8–12.6 percentage points and 3.6–15.2 percentage points compared to the mAP₅₀ and F1-score values of other models. From the experimental results, it is shown that the improved YOLOv8 network, with high performance and robustness in small-sized maize tassel recognition, can accurately recognize maize tassels in UAV images, which provides technical support for automated counting, accurate cultivation, and large-scale intelligent cultivation of maize seedlings.