This article explores the use of drones in agriculture and discusses the various types of drones employed for different agricultural applications. Drones, also known as unmanned aerial vehicles (UAVs), offer numerous advantages in farming practices. They provide real-time and high-resolution data collection, enabling farmers to make informed irrigation, fertilization, and pest management decisions. Drones assist in precision spraying and application of agricultural inputs, minimizing chemical wastage and optimizing resource utilization. They offer accessibility to inaccessible areas, reduce manual labor, and provide cost savings and increased operational efficiency. Drones also play a crucial role in mapping and surveying agricultural fields, aiding crop planning and resource allocation. However, challenges such as regulations and limited flight time need to be addressed. The advantages of using drones in agriculture include precision agriculture, cost and time savings, improved data collection and analysis, enhanced crop management, accessibility and flexibility, environmental sustainability, and increased safety for farmers. Overall, drones have the potential to revolutionize farming practices, leading to increased efficiency, productivity, and sustainability in agriculture.
Chasing Lions: Co-Designing Human-Drone Interaction in Sub-Saharan Africa
Ben Gurion University of the Negev（内盖夫本古里安大学）；Interdisciplinary Center (IDC) Herzliya（赫兹利亚跨学科中心）；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Aalto University（阿尔托大学）
On Adversarial Attacks In Acoustic Drone Localization
Department of Computer Science（计算机科学系）；Technion – Israel Institute of Technology（技术ion-以色列理工学院）；School of Electrical and Computer Engineering（电气与计算机工程学院）；Ben-Gurion University of the Negev（贝内尔谢瓦大学）；Institute of Science and Technology Austria（奥地利科学与技术研究院）
A Deep Learning-based Pest Insect Monitoring System for Ultra-low Power Pocket-sized Drones
Dalle Molle Institute for Artificial Intelligence (IDSIA)（达勒·莫勒人工智能研究所）；USI（瑞士意大利语区大学）；SUPSI（瑞士应用科学大学）；Integrated Systems Laboratory (IIS)（集成系统实验室）；ETH Zürich（苏黎世联邦理工学院）
DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environment
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Irvine（加利福尼亚大学欧文分校）；George Washington University（乔治·华盛顿大学）；Beijing Sineva Robot Technology Co., Ltd（北京思知机器人科技有限公司）；University of Southern California（南加利福尼亚大学）；Santa Clara University（圣克拉拉大学）；Mangosuthu University of Technology（曼戈苏图科技大学）
Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors
Dalle Molle Institute for Artificial Intelligence, USI and SUPSI（达勒·莫勒人工智能研究所（瑞士意大利语区大学与瑞士应用科学大学））；Integrated Systems Laboratory, ETH Zürich（苏黎世联邦理工学院集成系统实验室）
Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD)
Dept. of Computer Science & Engineering, University of Minnesota（计算机科学与工程系，明尼苏达大学）；Dept. of Mechanical Engineering, University of Minnesota（机械工程系，明尼苏达大学）
Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科中心）；University of Luxembourg（卢森堡大学）
