The significance of environmental considerations has been highlighted by the substantial impact of plant pests on ecosystems. Addressing the urgent demand for sophisticated pest management solutions in arboreal environments, this study leverages advanced deep learning technologies to accurately detect and classify common tree pests, such as “mole cricket”, “aphids”, and “Therioaphis maculata (Buckton)”. Through comparative analysis with the baseline model ResNet-18 model, this research not only enhances the SE-RegNetY and SE-RegNet models but also introduces innovative frameworks, including GCT-Inception-ResNet-V3, SE-Inception-ResNet-V3, and SE-Inception-RegNetY-V3 models. Notably, the GCT-Inception-ResNet-V3 model demonstrates exceptional performance, achieving a remarkable average overall accuracy of 94.59%, average kappa coefficient of 91.90%, average mAcc of 94.60%, and average mIoU of 89.80%. These results signify substantial progress over conventional methods, outperforming the baseline model’s results by margins of 9.1%, nearly 13.7%, 9.1%, and almost 15% in overall accuracy, kappa coefficient, mAcc, and mIoU, respectively. This study signifies a considerable step forward in blending sustainable agricultural practices with environmental conservation, setting new benchmarks in agricultural pest management. By enhancing the accuracy of pest identification and classification in agriculture, it lays the groundwork for more sustainable and eco-friendly pest control approaches, offering valuable contributions to the future of agricultural protection.

Rice yields are expected to drop significantly due to the increasing spread of rice pests. Detecting rice pests in a timely manner using deep learning models has become a prevalent approach for rapid pest control. However, current datasets related to rice pests often suffer from limited sample sizes or poorly annotated labels, which compromises the training accuracy of deep learning models. Building upon the large-scale IP102 dataset, this study refines the rice pest segment of IP102 by separating adult specimens and larva specimens, acquiring additional pest images via web crawler techniques, and re-annotating all adult samples. The pest category names, originally in English, are replaced with the Latin scientific names of the corresponding families to improve both clarity and scientific accuracy. The resulting dataset, designated RP11, includes 11 adult categories with 4559 images and 7 larval categories with 2467 images. All annotations follow a labeling format compatible with YOLO model training. The sample count in RP11 is approximately four times that of the rice-specific subset in IP102. In this work, YOLOv11 was employed to evaluate RP11’s performance, with IP102 serving as a comparison dataset. The results demonstrate that RP11 outperforms IP102 in precision (83.0% vs. 58.9%), recall (79.7% vs. 63.1%), F1-score (81.3% vs. 60.9%), mAP50 (87.2% vs. 62.0%), and mAP50–95 (73.3% vs. 37.9%).

Volcanism is an important geological evolutionary process on the Moon. The study of lunar volcanic features is of great significance and value to understanding the geological evolution of the Moon better. Lunar domes are one of the essential volcanic features of the Moon. However, the existing lunar dome detection methods are still traditional manual or semiautomatic identification approaches that require extensive prior knowledge and have a complex identification process. Therefore, this paper proposes an automatic detection method based on improved YOLOv7 for lunar dome detection. First, a new lunar dome dataset was created by digital elevation model (DEM) data, and the effective squeeze and excitation (ESE) attention mechanism module was added to the backbone and neck sections to reduce information loss in the feature map and enhance network expressiveness. Then, a new SPPCSPC-RFE module was proposed by adding the receptive field enhancement (RFE) module into the neck section, which can adapt to dome feature maps of different shapes and sizes. Finally, the bounding box regression loss function complete IOU (CIOU) was replaced by wise IOU (WIOU). The WIOU loss function improved the model’s performance for the dome detection effect. Furthermore, this study combined several data enhancement strategies to improve the robustness of the network. To evaluate the performance of the proposed model, we conducted several experiments using the dome dataset developed in this study. The experimental results indicate that the improved method outperforms related methods with a mean average precision (mAP@0.5) value of 88.7%, precision (P) value of 85.6%, and recall (R) value of 86.4%. This study provides an effective solution for lunar dome detection.