Journal of Agriculture and Forestry is an international, peer-reviewed open access journal dedicated to advancing research the field of agricultural and forestry sciences. The journal provides a rapid publication process to ensure wide dissemination of high-quality articles to scientists, professionals, and interested individuals worldwide. Our goal is to serve as an efficient, reliable, and trusted platform for scholars and readers, publishing cutting-edge research in the field.
Abstract: The species Toddalia asiatica (L.) Lam. has many values; with the strengthening of its development and utilization in recent years, wild resources are gradually decreasing. Based on the extensive specimen records, by using an optimized MaxEnt ecological model, the potential distribution of the suitable habitat of T. asiatica was predicted to understand suitable areas of T. asiatica in China and the changes of the suitable areas under different climate scenarios, which provides theoretical references for species protection, resource investigation, and introduction and cultivation of T. asiatica resources. The results showed that the medium suitable areas and high suitable areas of T. asiatica in China were mainly concentrated in tropical and subtropical monsoon climate areas such as South China, Southeast of Southwest China, West of Central China and South of East China, under the current climatic conditions. Under the future climate conditions, its suitable area has a tendency to migrate to the southwest with climate warming, and shows a trend of fragmentation. Compared with the current climatic conditions, the total suitable area showed a decreasing trend, but the high suitable area of T. asiatica increased under the SSPs126 and SSPs245 scenarios. This result may be related to global warming caused by increased carbon emissions. Under the future climatic conditions, the suitable areas for the expansion and contraction of T. asiatica are mainly distributed in the marginal areas of the whole suitable area, and the high suitable areas remain unchanged, which are distributed in most areas of Chongqing, Guizhou, Guangxi, and Guangdong, as well as parts of Yunnan, Sichuan, Fujian, Hainan, and Taiwan provinces. The two climatic factors of precipitation and temperature are the main causes and sources of differences limiting the geographical distribution of T. asiatica. Among them, bio18 (Precipitation of Warmest Quarter) and bio11 (Mean Temperature of Coldest Quarter) are the dominant environmental variables limiting the distribution of T. asiatica.Abstract: The species Toddalia asiatica (L.) Lam. has many values; with the strengthening of its development and utilization in recent years, wild resources are gradually decreasing. Based on the extensive specimen records, by using an optimized MaxEnt ecological model, the potential distribution of the suitable habitat of T. asiatica was predicted to unders...Learn More
Abstract: This study aims to explore the application of Artificial Intelligence (AI) technology in wetland bird monitoring to improve monitoring efficiency and accuracy. By integrating modern communication networks and AI technologies, a sky-ground integrated monitoring network covering key ecosystems has been established. In the Chen Lake Wetland in Hubei, high-definition cameras, sound sensors, and drones were deployed to achieve real-time video surveillance and sound data collection of bird activities. Deep learning algorithms were utilized to analyze video streams and sound signals, automatically identifying bird species and behavior patterns. The study selected 11 bird species as monitoring objects and collected a large amount of image data using drones. YOLOv8 and PP-HGNET algorithms were employed for bird recognition and classification. The results showed that the AI monitoring system achieved a mean Average Precision (mAP) of 87.7% in bird recognition accuracy, providing reliable data support for bird dynamic monitoring, epidemic source monitoring, and conservation research. Additionally, AI technology can monitor wetland environmental indicators, offering a scientific basis for ecological protection. The research demonstrates that AI technology has broad application prospects in wetland bird monitoring, capable of driving the innovation of monitoring technologies and enhancing the efficiency and effectiveness of ecological conservation.Abstract: This study aims to explore the application of Artificial Intelligence (AI) technology in wetland bird monitoring to improve monitoring efficiency and accuracy. By integrating modern communication networks and AI technologies, a sky-ground integrated monitoring network covering key ecosystems has been established. In the Chen Lake Wetland in Hubei, ...Learn More
