Application of digital technologies for ensuring agricultural productivity

被引:62
作者
Abiri, Rambod [1 ]
Rizan, Nastaran [2 ]
Balasundram, Siva K. [2 ]
Shahbazi, Arash Bayat [3 ]
Abdul-Hamid, Hazandy [1 ,4 ]
机构
[1] Univ Putra Malaysia, Fac Forestry & Environm, Dept Forestry Sci & Biodivers, Serdang 43400, Malaysia
[2] Univ Putra Malaysia, Fac Agr, Dept Agr Technol, Serdang 43400, Malaysia
[3] Univ Teknol Malaysia, Fac Engn, Sch Comp, Dept Informat Syst, Johor Baharu 81310, Malaysia
[4] Univ Putra Malaysia, Inst Trop Forestry & Forest Prod INTROP, Lab Bioresource Management, Serdang 43400, Malaysia
关键词
Digital agriculture; Internet of things; Digital technology; Big data; Smart farming; CYBER-PHYSICAL SYSTEMS; WIRELESS SENSOR NETWORKS; BIG DATA; INDUSTRY; 4.0; BLOCKCHAIN; MANAGEMENT; ARCHITECTURE; CHALLENGES; OPERATIONS;
D O I
10.1016/j.heliyon.2023.e22601
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Over the decades, agri-food security has become one of the most critical concerns in the world. Sustainable agri-food production technologies have been reliable in mitigating poverty caused by high demands for food. Recently, the applications of agri-food system technologies have been meaningfully changing the worldwide scene due to both external strengths and internal forces. Digital agriculture (DA) is a pioneering technology helping to meet the growing global demand for sustainable food production. Integrating different sub-branches of DA technologies such as artificial intelligence, automation and robotics, sensors, Internet of Things (IoT) and data analytics into agriculture practices to reduce waste, optimize farming inputs and enhance crop production. This can help shift from tedious operations to continuously automated processes, resulting in increasing agricultural production by enabling the traceability of products and processes. The application of DA provides agri-food producers with accurate and real-time observations regarding different features influencing their productivity, such as plant health, soil quality, weather conditions, and pest and disease pressure. Analyzing the results achieved by DA can help agricultural producers and scholars make better decisions to increase yields, improve efficiency, reduce costs, and manage resources. The core focus of the current work is to clarify the benefits of some sub-branches of DA in increasing agricultural production efficiency, discuss the challenges of practical DA in the field, and highlight the future perspectives of DA. This review paper can open new directions to speed up the DA application on the farm and link traditional agriculture with modern farming technologies.
引用
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页数:21
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