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

Author

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  • Mohamed Farag Taha

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
    Department of Soil and Water Sciences, Faculty of Environmental Agricultural Sciences, Arish University, Arish 45516, Egypt)

  • 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
    Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt)

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.

Suggested Citation

  • Mohamed Farag Taha & Hanping Mao & Zhao Zhang & Gamal Elmasry & Mohamed A. Awad & Alwaseela Abdalla & Samar Mousa & Abdallah Elshawadfy Elwakeel & Osama Elsherbiny, 2025. "Emerging Technologies for Precision Crop Management Towards Agriculture 5.0: A Comprehensive Overview," Agriculture, MDPI, vol. 15(6), pages 1-32, March.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:6:p:582-:d:1608607
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    References listed on IDEAS

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    1. Dimitris Mourtzis & John Angelopoulos & Nikos Panopoulos, 2022. "A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0," Energies, MDPI, vol. 15(17), pages 1-29, August.
    2. Min Dai & Yutian Shen & Xiaoyin Li & Jingjing Liu & Shanwen Zhang & Hong Miao, 2024. "Digital Twin System of Pest Management Driven by Data and Model Fusion," Agriculture, MDPI, vol. 14(7), pages 1-19, July.
    3. Houda Orchi & Mohamed Sadik & Mohammed Khaldoun & Essaid Sabir, 2023. "Automation of Crop Disease Detection through Conventional Machine Learning and Deep Transfer Learning Approaches," Agriculture, MDPI, vol. 13(2), pages 1-35, January.
    4. Jin, Kaijun & Zhang, Jihong & Wang, Zhenhua & Zhang, Jinzhu & Liu, Ningning & Li, Miao & Ma, Zhanli, 2024. "Application of deep learning based on thermal images to identify the water stress in cotton under film-mulched drip irrigation," Agricultural Water Management, Elsevier, vol. 299(C).
    5. Hoda Galal & Salah Elsayed & Osama Elsherbiny & Aida Allam & Mohamed Farouk, 2022. "Using RGB Imaging, Optimized Three-Band Spectral Indices, and a Decision Tree Model to Assess Orange Fruit Quality," Agriculture, MDPI, vol. 12(10), pages 1-19, September.
    6. Osama Elsherbiny & Yangyang Fan & Lei Zhou & Zhengjun Qiu, 2021. "Fusion of Feature Selection Methods and Regression Algorithms for Predicting the Canopy Water Content of Rice Based on Hyperspectral Data," Agriculture, MDPI, vol. 11(1), pages 1-21, January.
    7. Yang Yang & Min Lin & Yangfei Lin & Chen Zhang & Celimuge Wu, 2025. "A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things," Future Internet, MDPI, vol. 17(1), pages 1-54, January.
    8. Yulin Shen & Benoît Mercatoris & Zhen Cao & Paul Kwan & Leifeng Guo & Hongxun Yao & Qian Cheng, 2022. "Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery," Agriculture, MDPI, vol. 12(6), pages 1-13, June.
    9. Nahina Islam & Md Mamunur Rashid & Santoso Wibowo & Cheng-Yuan Xu & Ahsan Morshed & Saleh A. Wasimi & Steven Moore & Sk Mostafizur Rahman, 2021. "Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm," Agriculture, MDPI, vol. 11(5), pages 1-13, April.
    10. Martin Kuradusenge & Eric Hitimana & Damien Hanyurwimfura & Placide Rukundo & Kambombo Mtonga & Angelique Mukasine & Claudette Uwitonze & Jackson Ngabonziza & Angelique Uwamahoro, 2023. "Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize," Agriculture, MDPI, vol. 13(1), pages 1-19, January.
    11. Ziang Niu & Ting Huang & Chengjia Xu & Xinyue Sun & Mohamed Farag Taha & Yong He & Zhengjun Qiu, 2025. "A Novel Approach to Optimize Key Limitations of Azure Kinect DK for Efficient and Precise Leaf Area Measurement," Agriculture, MDPI, vol. 15(2), pages 1-20, January.
    12. Yafei Wang & Tiezhu Li & Tianhua Chen & Xiaodong Zhang & Mohamed Farag Taha & Ning Yang & Hanping Mao & Qiang Shi, 2024. "Cucumber Downy Mildew Disease Prediction Using a CNN-LSTM Approach," Agriculture, MDPI, vol. 14(7), pages 1-17, July.
    13. Jinmei Kou & Long Duan & Caixia Yin & Lulu Ma & Xiangyu Chen & Pan Gao & Xin Lv, 2022. "Predicting Leaf Nitrogen Content in Cotton with UAV RGB Images," Sustainability, MDPI, vol. 14(15), pages 1-10, July.
    14. Suk-Ju Hong & Sang-Yeon Kim & Eungchan Kim & Chang-Hyup Lee & Jung-Sup Lee & Dong-Soo Lee & Jiwoong Bang & Ghiseok Kim, 2020. "Moth Detection from Pheromone Trap Images Using Deep Learning Object Detectors," Agriculture, MDPI, vol. 10(5), pages 1-12, May.
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