Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review

被引:34
作者
Adli, Hasyiya Karimah [1 ]
Remli, Muhammad Akmal [2 ]
Wong, Khairul Nizar Syazwan Wan Salihin [2 ]
Ismail, Nor Alina [1 ]
Gonzalez-Briones, Alfonso [3 ]
Corchado, Juan Manuel [3 ]
Mohamad, Mohd Saberi [4 ]
机构
[1] Univ Malaysia Kelantan, Fac Data Sci & Comp, City Campus, Kota Baharu 16100, Kelantan, Malaysia
[2] Univ Malaysia Kelantan, Inst Artificial Intelligence & Big Data, City Campus, Kota Baharu 16100, Kelantan, Malaysia
[3] Univ Salamanca, Fac Ciencias, Dept Informat & Automat, Grp Invest BISITE,Inst Invest Biomed Salamanca, Calle Espejo 2, Salamanca 37007, Spain
[4] United Arab Emirates Univ, Coll Med & Hlth Sci, Dept Genet & Genom, Hlth Data Sci Lab, Al Ain 17666, U Arab Emirates
关键词
artificial intelligence of things; smart agriculture; internet of things; artificial intelligence; post-pandemic; crop diseases; SYSTEM; IOT; INTELLIGENCE; PESTS;
D O I
10.3390/s23073752
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
As the most popular technologies of the 21st century, artificial intelligence (AI) and the internet of things (IoT) are the most effective paradigms that have played a vital role in transforming the agricultural industry during the pandemic. The convergence of AI and IoT has sparked a recent wave of interest in artificial intelligence of things (AIoT). An IoT system provides data flow to AI techniques for data integration and interpretation as well as for the performance of automatic image analysis and data prediction. The adoption of AIoT technology significantly transforms the traditional agriculture scenario by addressing numerous challenges, including pest management and post-harvest management issues. Although AIoT is an essential driving force for smart agriculture, there are still some barriers that must be overcome. In this paper, a systematic literature review of AIoT is presented to highlight the current progress, its applications, and its advantages. The AIoT concept, from smart devices in IoT systems to the adoption of AI techniques, is discussed. The increasing trend in article publication regarding to AIoT topics is presented based on a database search process. Lastly, the challenges to the adoption of AIoT technology in modern agriculture are also discussed.
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页数:22
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共 100 条
[1]  
Adli HK, 2021, 2021 5TH INTERNATIONAL CONFERENCE ON INFORMATICS AND COMPUTATIONAL SCIENCES (ICICOS 2021), DOI [10.1109/ICICoS53627.2021.9651831, 10.1109/ICICOS53627.2021.9651831]
[2]  
Ahamad S., 2022, P 2 INT C ADV COMPUT, P135
[3]   Vision on the bog: Cranberry crop risk evaluation with deep learning [J].
Akiva, Peri ;
Planche, Benjamin ;
Roy, Aditi ;
Oudemans, Peter ;
Dana, Kristin .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 203
[4]   Efficient Control of PTZ Cameras in Automated Video Surveillance Systems [J].
Al-Hadrusi, Musab S. ;
Sarhan, Nabil J. .
2012 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM), 2012, :356-359
[5]  
Alam M, 2020, 2020 7TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ICEEE 2020), P273, DOI [10.1109/iceee49618.2020.9102505, 10.1109/ICEEE49618.2020.9102505]
[6]   A Survey of Preprocessing Methods Used for Analysis of Big Data Originated From Smart Grids [J].
Alghamdi, Turki Ali ;
Javaid, Nadeem .
IEEE ACCESS, 2022, 10 :29149-29171
[7]  
Amarasingam Narmilan, 2022, Remote Sensing Applications: Society and Environment, DOI 10.1016/j.rsase.2022.100712
[8]  
Ambildhuke G, 2022, INT J ADV COMPUT SC, V13, P267
[9]   State of the Art of Ensemble Learning Approach for Crop Prediction [J].
Apat, Shraban Kumar ;
Mishra, Jyotirmaya ;
Raju, K. Srujan ;
Padhy, Neelamadhab .
NEXT GENERATION OF INTERNET OF THINGS, 2023, 445 :675-685
[10]   Towards applicability of machine learning techniques in agriculture and energy sector [J].
Arumugam, K. ;
Swathi, Yarnagula ;
Sanchez, Domenic T. ;
Mustafa, Malik ;
Phoemchalard, Chirasak ;
Phasinam, Khongdet ;
Okoronkwo, Ethelbert .
MATERIALS TODAY-PROCEEDINGS, 2022, 51 :2260-2263