Emerging Technologies for Precision Crop Management Towards Agriculture 5.0: A Comprehensive Overview

Mohamed Farag Taha, Hanping Mao (), Zhao Zhang, Gamal Elmasry, Mohamed A. Awad, Alwaseela Abdalla, Samar Mousa, Abdallah Elshawadfy Elwakeel and Osama Elsherbiny
Additional contact information
Mohamed Farag Taha: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Hanping Mao: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Zhao Zhang: Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, China
Gamal Elmasry: Agricultural Engineering Department, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt
Mohamed A. Awad: Department of Plant Production, Faculty of Environmental Agricultural Sciences, Arish University, Arish 45516, Egypt
Alwaseela Abdalla: Department of Agricultural Biology, Colorado State University, Fort Collins, CO 80523, USA
Samar Mousa: Agricultural Botany Department, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt
Abdallah Elshawadfy Elwakeel: Agricultural Engineering Department, Faculty of Agriculture and Natural Resources, Aswan University, Aswan 81528, Egypt
Osama Elsherbiny: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 6, 1-32

Abstract: Agriculture 5.0 (Ag5.0) represents a groundbreaking shift in agricultural practices, addressing the global food security challenge by integrating cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), robotics, and big data analytics. To adopt the transition to Ag5.0, this paper comprehensively reviews the role of AI, machine learning (ML) and other emerging technologies to overcome current and future crop management challenges. Crop management has progressed significantly from early agricultural methods to the advanced capabilities of Ag5.0, marking a notable leap in precision agriculture. Emerging technologies such as collaborative robots, 6G, digital twins, the Internet of Things (IoT), blockchain, cloud computing, and quantum technologies are central to this evolution. The paper also highlights how machine learning and modern agricultural tools are improving the way we perceive, analyze, and manage crop growth. Additionally, it explores real-world case studies showcasing the application of machine learning and deep learning in crop monitoring. Innovations in smart sensors, AI-based robotics, and advanced communication systems are driving the next phase of agricultural digitalization and decision-making. The paper addresses the opportunities and challenges that come with adopting Ag5.0, emphasizing the transformative potential of these technologies in improving agricultural productivity and tackling global food security issues. Finally, as Agriculture 5.0 is the future of agriculture, we highlight future trends and research needs such as multidisciplinary approaches, regional adaptation, and advancements in AI and robotics. Ag5.0 represents a paradigm shift towards precision crop management, fostering sustainable, data-driven farming systems that optimize productivity while minimizing environmental impact.

Keywords: Agriculture 5.0 (Ag5.0); artificial intelligence (AI); autonomous mobile robots; crop monitoring; convolutional neural networks (CNNs) (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: 2025
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