Analysis of Various Machine Learning Algorithms for Using Drone Images in Livestock Farms

Jerry Gao, Charanjit Kaur Bambrah, Nidhi Parihar, Sharvaree Kshirsagar, Sruthi Mallarapu, Hailong Yu, Jane Wu and Yunyun Yang ()
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Jerry Gao: Department of Computer Engineering, San Jose State University, San Jose, CA 95192, USA
Charanjit Kaur Bambrah: Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA
Nidhi Parihar: Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA
Sharvaree Kshirsagar: Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA
Sruthi Mallarapu: Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA
Hailong Yu: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Jane Wu: BRI, Santa Clara, CA 95050, USA
Yunyun Yang: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China

Agriculture, 2024, vol. 14, issue 4, 1-31

Abstract: 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.

Keywords: deep learning; DeepSort algorithm; livestock classification; object detection; object tracking (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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