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 precise prevention and control of forest pests and diseases has always been a research hotspot in ecological environmental protection. With the continuous advancement of sensor technology, the fine-grained identification of discolored tree crowns based on UAV technology has become increasingly important in forest monitoring. Existing deep learning models face challenges such as prolonged training time and low recognition accuracy when identifying discolored tree crowns caused by pests or diseases from airborne images. To address these issues, this study improves the Faster-RCNN model by using Inception-ResNet-V2 as the feature extractor, replacing the traditional VGG16 feature extractor, aiming to enhance the accuracy of discolored tree crown recognition. Experiments and analyses were conducted using UAV aerial imagery data from Jilin Changbai Mountain. The improved model effectively identified discolored tree crowns caused by pine wood nematodes, achieving a precision of 90.22%, a mean average precision (mAP) of 83.63%, and a recall rate of 92.33%. Compared to the original RCNN model, the mAP of the improved model increased by 4.68%, precision improved by 10.11%, and recall improved by 5.23%, significantly enhancing the recognition performance of discolored tree crowns. This method provides crucial technical support and scientific basis for the prevention and control of forest pests and diseases, facilitating early detection and precise management of forest pest outbreaks.

Landslides are destructive geological hazards, making accurate landslide susceptibility assessment essential for disaster prevention and mitigation. However, existing studies often lack scientific rigor in negative sample construction and have unclear model applicability. This study focuses on Ya’an City, Sichuan Province, China, and proposes an innovative approach to negative sample construction using Geographically Weighted Regression (GWR), which is then integrated with Tabular Network (TabNet), a deep learning architecture tailored to structured tabular data, to assess landslide susceptibility. The performance of TabNet is compared against Random Forest, Light Gradient Boosting Machine, deep neural networks, and Residual Networks. The experimental results indicate that (1) the GWR-based sampling strategy substantially improves model performance across all tested models; (2) TabNet trained using the GWR-based negative samples achieves superior performance over all other evaluated models, with an average AUC of 0.9828, exhibiting both high accuracy and interpretability; and (3) elevation, land cover, and annual Normalized Difference Vegetation Index are identified as dominant predictors through TabNet’s feature importance analysis. The results demonstrate that combining GWR and TabNet substantially enhances landslide susceptibility modeling by improving both accuracy and interpretability, establishing a more scientifically grounded approach to negative sample construction, and providing an interpretable, high-performing modeling framework for geological hazard risk assessment.

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.