外文摘要：With the development of artificial intelligence, the intelligence of agriculture has become a trend. Intelligent monitoring of agricultural activities is an important part of it. However, due to difficulties in achieving a balance between quality and cost, the goal of improving the economic benefits of agricultural activities has not reached the expected level. Farm supervision requires intensive human effort and may not produce satisfactory results. In order to achieve intelligent monitoring of agricultural activities and improve economic benefits, this paper proposes a solution that combines unmanned aerial vehicles (UAVs) with deep learning models. The proposed solution aims to detect and classify objects using UAVs in the agricultural industry, thereby achieving independent agriculture without human intervention. To achieve this, a highly reliable target detection and tracking system is developed using Unmanned Aerial Vehicles. The use of deep learning methods allows the system to effectively solve the target detection and tracking problem. The model utilizes data collected from DJI Mirage 4 unmanned aerial vehicles to detect, track, and classify different types of targets. The performance evaluation of the proposed method shows promising results. By combining UAV technology and deep learning models, this paper provides a cost-effective solution for intelligent monitoring of agricultural activities. The proposed method offers the potential to improve the economic benefits of farming while reducing the need for intensive hum.
外文关键词：deep learning;object detection;DeepSort algorithm;livestock classification;object tracking
作者：Gao, Jerry;Wu, Jane;Bambrah, Charanjit Kaur;Parihar, Nidhi;Kshirsagar, Sharvaree;Mallarapu, Sruthi;Yu, Hailong;Yang, Yunyun
作者单位：San Jose State Univ;Taiyuan Univ Technol;BRI
期刊名称：AGRICULTURE-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：14（4）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台