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.

Wetlands, situated at the vital intersection of terrestrial and aquatic ecosystems, are pivotal in preserving global biodiversity and maintaining environmental equilibrium. The escalating trend of global urbanization necessitates the utilization of high-resolution satellite imagery for accurate wetland delineation, which is essential for establishing efficacious conservation strategies. This study focuses on the wetlands of Macau, characterized by distinctive coastal and urban features. A noteworthy enhancement in this study is the integration of the Coordinate Attention mechanism with the RegNet model, forming the CA-RegNet. This combined model demonstrates superior performance, outdoing previous Macau wetlands segmentation studies that used ResNet, evidenced by an approximate rise of 2.7% in overall accuracy (OA), 4.0% in the Kappa coefficient, 1.9% in the mAcc, and 0.5% in the mIoU. Visual evaluations of the segmentation results reinforce the competence of the CA-RegNet model in precisely demarcating coastal wetlands and Saiwan Lake, thereby overcoming the former constraints of ResNet and underscoring the robustness and innovation of this study.

Tourism destinations are now facing a dilemma choice of controlling the epidemic or developing the economy. This paper takes Macao, a typical international tourist city, as an example to study the strategy of tourist source control during the COVID-19 period. According to the published epidemic data of Macao, this study has established an improved SEIR (Susceptible-Exposed-Infected-Recovered) model, formulated six control strategies against the current epidemic, and used the model above to simulate the time required for all confirmed cases to recover and discharge under different strategies. By taking into consideration the gross revenue of Macao’s gambling industry from 2017 to 2019, the impact of different strategies on the economy is predicted and three control strategies are found to be feasible. This study shows that an effective way to break through the above dilemma is to design the tourist management strategy by screening the source of passengers and controlling the upper limit of capacity of destination. These findings provide a scientific basis for tourism destinations in formulating public policies. The improved SEIR model is more consistent with the actual conversion rule of patients in the current COVID-19 epidemic, and it can be applied to further public health related research.

Rivers, as fundamental components of freshwater supply and wetland ecosystems, play an essential role in sustaining biodiversity and facilitating sustainable resource utilization. This study introduces the integration of the attention mechanism within the convolutional neural network (CNN) framework and constructs seven enhanced models. A novel dataset has been independently developed utilizing high spatial resolution remote sensing images obtained from China’s Gaofen-2 satellite (GF-2), which enables the efficient and precise extraction of river distribution. The city of Zhuhai, characterized by its intricate river network located in the lower reaches of the Pearl River Basin, has been selected as the experimental area for this research. The experimental results indicate that the CNN model enhanced by the attention mechanism significantly surpasses the baseline model across several performance metrics, including overall accuracy, Kappa coefficient, Precision, Recall, F1-score, Mean Intersection over Union, and the extraction result map. Notably, the model incorporating the Bottleneck Attention Module demonstrates the highest performance, achieving overall accuracy and Kappa coefficient values of 93.09% and 0.8618, respectively, which surpass the baseline model by 12.62% and 0.2524. This study thus provides crucial spatial data and method support for river resource management, supporting ecological conservation and sustainable wetland management.

Wetlands are essential components of our natural ecosystems, performing critical functions that contribute to biodiversity conservation, water purification, climate regulation, and shoreline protection. Remote sensing classification of wetlands, wetland resource surveys and other wetland research is booming, for the development of environmental protection policies continue to provide reliable data. Macau is a coastal city with rich wetland resources, but there is no relevant research on wetlands in Macau. In this paper, we use the high spatial resolution remote sensing data from Gaofen-2 satellite to extract the wetlands of Macau SAR to fill the gap of related research. This paper used two distinct machine learning classifiers, including Random Forest (RF) and Support Vector Machine (SVM), to analyse the high spatial resolution remote sensing data of GF-2 satellite in order to extract wetlands in Macau Special Region Area. In this research, the accuracy of RF model is much higher than that of SVM model, comparing the indexes of overall accuracy (OA) and Kappa coefficient. After analysing the resultant images generated after segmentation, the image segmentation result of RF model is better than that of SVM model. As for the RF model, the overall accuracy (OA) is 0.9941 and Kappa coefficient is 0.9899.