AI-Driven Farming Systems With Minimal Human Intervention - Softcover

Softcover

ISBN 13: 9798337392967

Publisher: Engineering Science Reference, 2026

Synopsis

AI-driven farming systems reshape agriculture by integrating sensing technologies, machine learning, and autonomous machinery into a data-informed ecosystem. These systems monitor soil conditions, weather patterns, crop health, and resource use in real time, enabling precise decisions that optimize yields while reducing waste and environmental impact. By automating tasks, AI-driven farms may address labor shortages and increase efficiency across operations. They also raise important questions about technological access, data ownership, and the evolving role of farmers, positioning this approach as both a transformative opportunity and a critical area for thoughtful policy and ethical consideration. AI-Driven Farming Systems With Minimal Human Intervention explores AI-powered agricultural innovations, bridging the gap between research and practice. It highlights the transformative role of AI in tackling global agricultural challenges and emphasizes the need for advanced AI-driven solutions to enhance productivity, optimize resource use, promote climate resilience, reduce costs, improve yields, and support sustainable farming. This book covers topics such as irrigation, automation, and climatology, and is a useful resource for engineers, business owners, farmers, academicians, researchers, and scientists.

About this title

About the Authors

Kashif Naseer Qureshi is currently serving as a Associate Professor in the Department of Electronic and Computer Engineering at the esteemed University of Limerick. His dedication to academia is mirrored in his role as a principal investigator for a government-funded cybersecurity projects. Dr. Qureshi's name stands proudly within the top 2% of scientists globally, as acknowledged by Stanford University, USA. Commencing in 2020 and extending through 2025, he garnered consecutive Best Researcher Awards for his outstanding and impactful work. With a wealth of expertise spanning fourteen years, Dr. Qureshi's career is distinguished by his dual proficiency in teaching and research within prestigious academic institutions. His academic journey culminated in the attainment of a Ph.D. Degree from the University Teknologi Malaysia (UTM) in 2016, where his specialization centered on wireless communication. Prior to this, he achieved Master's degrees in Computer Science (MCS) and Information Technologies (MSIT) from renowned universities. His qualifications extend beyond the theoretical, as he is a certified network professional by both Cisco and Microsoft. Dr. Qureshi's collaborative efforts have traversed the globe, encompassing ventures in the United Kingdom, Canada, Korea, Malaysia, and China. These cooperative projects have been instrumental in the realms of cyber security, wireless communication, smart cities, IoTs, artificial intelligence, intelligent transportation systems, ad hoc networks, and cyber-physical systems. His intellectual prowess is evidenced by the publication of approximately 169 research papers in esteemed international journals, collectively amassing a noteworthy Impact Factor score exceeding 400. In recent times, his scholarly pursuits have focused on critical domains such as cybersecurity, trust-oriented edge and cloud solutions, secure Internet connectivity for vehicles, drone-enabled networking security, electric vehicle charging management, and safeguarded healthcare systems. Beyond his research contributions, Dr. Qureshi has taken on additional roles as an Associate and Guest Editor in distinguished SCI journals, as well as special issues for esteemed platforms such as IEEE, Sensors, and Hindawi. His multifaceted engagements underscore his commitment to advancing knowledge and fostering innovation within his fields of expertise.

Kayhan Zrar Ghafoor is the Dean of the College of Technology at Erbil Polytechnic University (EPU), Kurdistan Region, Iraq. He previously served as the President of Knowledge University and was a Postdoctoral Research Fellow at Shanghai Jiao Tong University, China, as well as a Visiting Researcher at Universiti Teknologi Malaysia (UTM), Malaysia. He received his B.Sc. in Electrical Engineering and M.Sc. in Remote Weather Monitoring Systems from Salahaddin University-Erbil, and earned his Ph.D. in Wireless Networks from Universiti Teknologi Malaysia (UTM), where he was honoured with the Vice-Chancellor Award for academic excellence. Dr. Ghafoor is an internationally recognised researcher in wireless communications, smart cities, cybersecurity, artificial intelligence, and intelligent systems. He has authored more than 250 scientific publications in ISI and Scopus-indexed journals and international conferences, four academic books, and has accumulated over 5,500 citations. He is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and has been recognised among the World's Top 2% Scientists in the Stanford University–Elsevier ranking for five consecutive years (2021–2025). Throughout his academic career, he has served as a principal investigator, associate editor, editorial board member, reviewer, and general chair of numerous international conferences and workshops, while contributing to research projects funded by international agencies.

Saman Muhammad Omer received the BSc degree in Software Engineering from Koya University in 2009, the MSc(ENG) degree in Advanced Software Engineering from University of Sheffield in 2012 and the PhD degree in Information system Engineering from Erbil Polytechnique University in 2023. He is working as an associate professor in the Department of Software Engineering at University of Raparin-Sulaymanyah, Kurdistan Region. He has published scientific/research papers in international journals and conferences. Dr. Saman served as a reviewer for numerous international and national journals, and worked as a Scientific member of international conferences. His research interest areas include artificial intelligence, computer vision, machine learning, and deep learning, particularly in image-based classification and detection systems.

Edition details

• Publisher: Engineering Science Reference
• Publication date: 2026
• Language: English
• ISBN 13: 9798337392967
• Binding: Paperback
• Number of pages: 462
• Editor: Qureshi Kashif Naseer, Ghafoor Kayhan Zrar, Omer Saman Muhammad