Empowering Farmers with IoT, UAVs, and Deep Learning in Smart Agriculture

^* Ahl Al Bayt University, Karbala, Iraq
^† National University Of Science And Technology, Dhi Qar, Iraq
^‡ Medical Technical College, Al-Farahidi University, Baghdad, Iraq
^§ College of technical engineering, The Islamic university, Najaf, Iraq

^* Corresponding author: sabah.abdulameer@uobabylon.edu.iq
^† Corresponding author: mohammed.alkhafaji@nust.edu.iq
^‡ Corresponding author: zain.jaffer@uoalfarahidi.edu.iq
^§ Corresponding author: mhussien074@gmail.com

Abstract

This review article explores the transformative influence of Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), and Deep Learning (DL) in modern agriculture, outlining their applications and impact on Smart Agriculture Systems (SAS). Examining various wireless communication technologies within IoT, including LoRa, Zigbee, and cellular networks like 5G, the study delineates their roles in enabling real-time monitoring and data transmission across expansive agricultural landscapes. Moving to UAVs, the review highlights their pivotal role in precision agriculture, elucidating how these aerial platforms equipped with diverse sensing technologies and cameras facilitate crop monitoring, disease detection, and targeted pesticide spraying. The integration of Deep Learning techniques, particularly Convolutional Neural Networks (CNNs), is discussed to emphasise their significance in disease detection, pest management, soil parameter estimation, and weed identification. The synthesis of these technologies reshapes traditional agricultural methodologies, empowering farmers with data-driven decision-making tools for optimized yield, sustainable practices, and efficient resource utilization. This comprehensive exploration aims to provide insights into the synergy of IoT, UAVs, and DL, laying the groundwork for the evolution of agricultural practices worldwide towards increased productivity and sustainability.