Exploring the Integration of Artificial Intelligence and IoT in Smart Farming: A Systematic Review
Menjelajahi Integrasi Kecerdasan Buatan dengan IoT dalam Pertanian Cerdas: Tinjauan Sistematis
Abstract
Abstract – This study examines the integration
of artificial intelligence (AI) and the Internet of
Things (IoT) in smart farming through a
systematic literature review. This research
focuses on the application of AI, the AIoT
architecture, the datasets used, and the
problems solved by this technology. The main
problems faced are the complexity of
technology integration and the limitations of
infrastructure in implementation in the field.
The purpose of the research is to provide a
comprehensive understanding of the
advancements and challenges of AIoT
technology in the agricultural sector. The
method used follows the guidance of
Kitchenham (2007) by reviewing the latest
relevant literature. The results show that AIoT
has great potential in improving the efficiency
and sustainability of the agricultural sector
through efficient data management and datadriven
decision-making. However, the success
of the implementation of this technology is
highly dependent on the availability of quality
datasets and the adaptability of the technology
at scale. This research provides practical
recommendations for the development and
application of AIoT in various smart
agriculture scenarios in the future.
References
[1] N. Abdullah et al., “Towards Smart Agriculture Monitoring Using Fuzzy Systems,” IEEE Access, vol. 9, pp. 4097–4111, 2021, doi: 10.1109/ACCESS.2020.3041597.
[2] Z. Raza, I. U. Haq, and M. Muneeb, “Agri-4-All: A Framework for Blockchain Based Agricultural Food Supply Chains in the Era of Fourth Industrial Revolution,” IEEE Access, vol. 11, pp. 29851–29867, 2023, doi: 10.1109/ACCESS.2023.3259962.
[3] M. Gupta, M. Abdelsalam, S. Khorsandroo, and S. Mittal, “Security and Privacy in Smart Farming: Challenges and Opportunities,” IEEE Access, vol. 8, pp. 34564–34584, 2020, doi: 10.1109/ACCESS.2020.2975142.
[4] M. S. M. Shah, Y. B. Leau, Z. Yan, and M. Anbar, “Hierarchical Naming Scheme in Named Data Networking for Internet of Things: A Review and Future Security Challenges,” 2022, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2022.3151864.
[5] H. A. Alharbi and M. Aldossary, “Energy-Efficient Edge-Fog-Cloud Architecture for IoT-Based Smart Agriculture Environment,” IEEE Access, vol. 9, pp. 110480–110492, 2021, doi: 10.1109/ACCESS.2021.3101397.
[6] A. U. H. Hashmi et al., “Effects of IoT Communication Protocols for Precision Agriculture in Outdoor Environments,” IEEE Access, vol. 12, pp. 46410–46421, 2024, doi: 10.1109/ACCESS.2024.3381522.
[7] S. Qazi, B. A. Khawaja, and Q. U. Farooq, “IoT-Equipped and AI- Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends,” 2022, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2022.3152544.
[8] E. Elbasi et al., “Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review,” 2023, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2022.3232485.
[9] B. Kitchenham and S. Charters, “Guidelines for Performing Systematic Literature Reviews in Software Engineering,” 2007. doi: 10.1541/ieejias.126.589.
[10] I. Ali, I. Ahmedy, A. Gani, M. U. Munir, and M. H. Anisi, “Data Collection in Studies on Internet of Things (IoT), Wireless Sensor Networks (WSNs), and Sensor Cloud (SC): Similarities and Differences,” IEEE Access, vol. 10, pp. 33909–33931, 2022, doi: 10.1109/ACCESS.2022.3161929.
[11] D. Javeed, T. Gao, M. S. Saeed, and P. Kumar, “An Intrusion Detection System for Edge-Envisioned Smart Agriculture in Extreme Environment,” IEEE Internet Things J, vol. 11, no. 16, pp. 26866–26876, 2024, doi: 10.1109/JIOT.2023.3288544.
[12] A. Saputhanthri, C. De Alwis, and M. Liyanage, “Survey on Blockchain- Based IoT Payment and Marketplaces,” IEEE Access, vol. 10, pp. 103411–103437, 2022, doi: 10.1109/ACCESS.2022.3208688.
[13] P. V. Astillo, J. Kim, V. Sharma, and I. You, “SGF-MD: Behavior rule specification-baseddistributed misbehavior detection of embedded iot devices in a closed-loop smart greenhouse farming system,” IEEE Access, vol. 8, pp. 196235–196252, 2020, doi: 10.1109/ACCESS.2020.3034096.
[14] E. Elbasi, N. Mostafa, C. Zaki, Z. AlArnaout, A. E. Topcu, and L. Saker, “Optimizing Agricultural Data Analysis Techniques through AI- Powered Decision-Making Processes,” Applied Sciences (Switzerland), vol. 14, no. 17, Dec. 2024, doi: 10.3390/app14178018.
[15] V. Kumar, K. V. Sharma, N. Kedam, A. Patel, T. R. Kate, and U. Rathnayake, “A comprehensive review on smart and sustainable agriculture using IoT technologies,” Smart Agricultural Technology, vol. 8, Dec. 2024, doi: 10.1016/j.atech.2024.100487.
[16] G. Singh and S. Sharma, “A comprehensive review on the Internet of Things in precision agriculture,” Multimed Tools Appl, 2024, doi: 10.1007/s11042-024-19656-0.
[17] M. Anila and O. Daramola, “Applications, technologies, and evaluation methods in smart aquaponics: a systematic literature review,” Artif Intell Rev, vol. 58, no. 1, Dec. 2025, doi: 10.1007/s10462-024- 11003-x.
[18] I. A. Ali, W. A. Bukhari, M. Adnan, M. I. Kashif, A. Danish, and A. Sikander, “Security and privacy in IoT-based Smart Farming: a review,” Multimed Tools Appl, 2024, doi: 10.1007/s11042- 024-19653-3.
[19] B. Maroua, A. A. Rachida, and M. Abdelaziz, “Smart farming architectures based on IoT review: comparative study,” in Procedia Computer Science, Elsevier B.V., 2022, pp. 783–788. doi: 10.1016/j.procs.2022.07.117.
[20] K. Chicaiza, R. Paredes, I. M. Sarzosa, S. G. Yoo, and N. Zang, “Smart Farming Technologies: A Methodological Overview and Analysis,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3487497.
[21] P. D. Rosero-Montalvo, C. A. Gordillo- Gordillo, and W. Hernandez, “Smart Farming Robot for Detecting Environmental Conditions in a Greenhouse,” IEEE Access, vol. 11, pp. 57843–57853, 2023, doi:10.1109/ACCESS.2023.3283986.
[22] A. Ahmed, I. Parveen, S. Abdullah, I. Ahmad, N. Alturki, and L. Jamel, “Optimized Data Fusion With Scheduled Rest Periods for Enhanced Smart Agriculture via Blockchain Integration,” IEEE Access, vol. 12, pp. 15171–15193, 2024, doi: 10.1109/ACCESS.2024.3357538.
[23] A. A. Alzubi and K. Galyna, “Artificial Intelligence and Internet of Things for Sustainable Farming and Smart Agriculture,” IEEE Access, vol. 11, pp. 78686–78692, 2023, doi: 10.1109/ACCESS.2023.3298215.
[24] S. Ghosh, “Neuro-Fuzzy-Based IoT Assisted Power Monitoring System for Smart Grid,” IEEE Access, vol. 9, pp. 168587–168599, 2021, doi: 10.1109/ACCESS.2021.3137812.
[25] M. Abdurohman, A. G. Putrada, and M. M. Deris, “A Robust Internet of Things- Based Aquarium Control System Using Decision Tree Regression Algorithm,” IEEE Access, vol. 10, pp. 56937–56951, 2022, doi:10.1109/ACCESS.2022.3177225.
[26] F. Kaçar et al., “Editorial Board Editor in Chief Associate Editors Assistant Editor Advisory Board.” [Online]. Available: https://electricajournal.org/en/instructi ons-to-authors-1013.
[27] A. Hamadani and N. A. Ganai, “Development of a multi-use decision support system for scientific management and breeding of sheep,” Sci Rep, vol. 12, no. 1, Dec. 2022, doi: 10.1038/s41598-022-24091-y.
[28] T. Rathod et al., “Blockchain-Driven Intelligent Scheme for IoT-Based Public Safety System beyond 5G Networks,” Sensors, vol. 23, no. 2, Dec. 2023, doi: 10.3390/s23020969.
[29] N. N. Thilakarathne, M. S. A. Bakar, P.Abas, and H. Yassin, “A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming,” Sensors, vol. 22, no. 16, Dec. 2022, doi: 10.3390/s22166299.
[30] K. S. Balamurugan, C. K. Pradhan, A. N. Venkateswarlu, G. Harini, and P. Geetha, “An internet of things based smart agriculture monitoring system using convolution neural network algorithm,” EAI Endorsed Transactions on Internet of Things, vol. 10, 2024, doi: 10.4108/eetiot.5105.
[31] K. Ragazou, A. Garefalakis, E. Zafeiriou, and I. Passas, “Agriculture 5.0: A New Strategic Management Mode for a Cut Cost and an Energy Efficient Agriculture Sector,” Energies (Basel), vol. 15, no. 9, Dec. 2022, doi: 10.3390/en15093113.
[32] S. Yonbawi et al., “Modeling of Sensor Enabled Irrigation Management for Intelligent Agriculture Using Hybrid Deep Belief Network,” Computer Systems Science and Engineering, vol. 46, no. 2, pp. 2319–2335, 2023, doi: 10.32604/csse.2023.036721.
[33] E. M. Raouhi, M. Lachgar, H. Hrimech, A. Kartit, and E. Jadida, “Unmanned Aerial Vehicle based Applications in Smart Farming: A Systematic Review.” [Online]. Available: www.ijacsa.thesai.org
[34] N. J. Lemphane, B. Kotze, and R. B. Kuriakose, “Developing a Digital Twin Model for Improved Pasture Management at Sheep Farm to Mitigate the Impact of Climate Change,” 2024. [Online]. Available: www.ijacsa.thesai.org
[35] V. Balaska, Z. Adamidou, Z. Vryzas, and A. Gasteratos, “Sustainable Crop Protection via Robotics and Artificial Intelligence Solutions,” Machines, vol. 11, no. 8, Dec. 2023, doi: 10.3390/machines11080774.
[36] J. Kim, I. Do Ha, S. Kwon, I. Jang, and M. H. Na, “A Smart Farm DNN Survival Model Considering Tomato Farm Effect,” Agriculture (Switzerland), vol. 13, no. 9, Dec. 2023, doi: 10.3390/agriculture13091782.
[37] N. Zelisko, N. Raiter, N. Markovych, H. Matskiv, and O. Vasylyna, “Improving business processes in the agricultural sector considering economic security, digitalization, risks, and artificial intelligence,” Ekonomika APK, vol. 31, no. 3, pp. 10–21, Dec. 2024, doi: 10.32317/2221-1055.2024030.10.
[38] J. Mehare and A. Gaikwad, “Secured Framework for Smart Farming in Hydroponics with Intelligent and Precise Management based on IoT with Blockchain Technology,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, pp. 244–254, 2023, doi: 10.17762/ijritcc.v11i9s.7418.
[39] B. Edwin et al., “Smart agriculture monitoring system for outdoor and hydroponic environments,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 25, no. 3, pp. 1679–1687, Dec. 2022, doi: 10.11591/ijeecs.v25.i3.pp1679-1687.
[40] M. Nawaz and M. I. K. Babar, “IoT and AI: a panacea for climate change- resilient smart agriculture,” Discover Applied Sciences, vol. 6, no. 10, Dec. 2024, doi: 10.1007/s42452-024-06228-y.
[41] A. R. Yanes, R. Abbasi, P. Martinez, and R. Ahmad, “Digital Twinning of Hydroponic Grow Beds in Intelligent Aquaponic Systems,” Sensors, vol. 22, no. 19, Dec. 2022, doi: 10.3390/s22197393.
[42] O. H. Abdelkader, H. Bouzebiba, D. Pena, and A. P. Aguiar, “Energy- Efficient IoT-Based Light Control System in Smart Indoor Agriculture,” Sensors, vol. 23, no. 18, Dec. 2023, doi: 10.3390/s23187670.
[43] J. Popp, J. Oláh, M. Neményi, and A. Nyéki, “Global challenges and the ‘farm to fork’ strategies of the European Green Deal: Blessing or curse,” Progress in Agricultural Engineering Sciences, 2024, doi: 10.1556/446.2024.00113.
[44] A. Sarkar, M. M. Singh, and H. S. Sharma, “Artificial recurrent neural network coordinated secured transmission towards safeguarding confidentiality in smart Industrial Internet of Things,” International Journal of Machine Learning and Cybernetics, 2024, doi: 10.1007/s13042-024-02310-4.
[45] D. Sharma, R. Kumar, and K. H. Jung, “A Bibliometric Analysis of Convergence of Artificial Intelligence and Blockchain for Edge of Things,” J Grid Comput, vol. 21, no. 4, Dec. 2023, doi: 10.1007/s10723-023-09716-4.
[46] R. Manikandan, G. Ranganathan, and Bindhu, “Deep Learning Based IoT Module for Smart Farming in Different Environmental Conditions,” Wirel Pers Commun, vol. 128, no. 3, pp. 1715–1732, Dec. 2023, doi: 10.1007/s11277-022-10016-5.
[47] F. A. Almalki and M. C. Angelides, “A synthesis of machine learning and internet of things in developing autonomous fleets of heterogeneous unmanned aerial vehicles for enhancing the regenerative farming cycle,” Computing, Dec. 2024, doi: 10.1007/s00607-024-01347-1.
[48] S. J. Soheli, N. Jahan, M. B. Hossain, A. Adhikary, A. R. Khan, and M. Wahiduzzaman, “Smart Greenhouse Monitoring System Using Internet of Things and Artificial Intelligence,” Wirel Pers Commun, vol. 124, no. 4, pp. 3603–3634, Dec. 2022, doi: 10.1007/s11277-022-09528-x.
[49] K. Kethineni and G. Pradeepini, “Intrusion detection in internet of things-based smart farming using hybrid deep learning framework,” Cluster Comput, vol. 27, no. 2, pp. 1719–1732, Dec. 2024, doi:10.1007/s10586-023-04052-4.
[50] H. Y. Riskiawan et al., “Artificial Intelligence Enabled Smart Monitoring and Controlling of IoT-Green House,” Arab J Sci Eng, vol. 49, no. 3, pp. 3043–3061, Dec. 2024, doi: 10.1007/s13369-023-07887-6.
[51] M. Mohy-eddine, A. Guezzaz, S. Benkirane, and M. Azrour, “Malicious detection model with artificial neural network in IoT-based smart farming security,” Cluster Comput, vol. 27, no. 6, pp. 7307–7322, Dec. 2024, doi:10.1007/s10586-024-04334-5.
[52] Y. Al Mashhadany, H. R. Alsanad, M. A. Al-Askari, S. Algburi, and B. A. Taha, “Irrigation intelligence— enabling a cloud-based Internet of Things approach for enhanced water management in agriculture,” Environ Monit Assess, vol. 196, no. 5, Dec. 2024, doi: 10.1007/s10661-024-12606-1.
[53] X. Wang and V. Jannesari, “Towards a crop pest control system based on the Internet of Things and fuzzy logic,” Telecommun Syst, vol. 85, no. 4, pp. 665–677, Dec. 2024, doi: 10.1007/s11235-024-01106-9.
[54] N. G. Rezk, E. E. D. Hemdan, A. F. Attia, A. El-Sayed, and M. A. El- Rashidy, “An efficient IoT based framework for detecting rice disease in smart farming system,” Multimed Tools Appl, vol. 82, no. 29, pp. 45259–45292, Dec. 2023, doi: 10.1007/s11042-023- 15470-2.
[55] E. Shakeripour and M. H. Ronaghi, “Proposing an artificial intelligence maturity model to illustrate a road map for cleaner animal farming management,” Operations Management Research, Dec. 2024, doi: 10.1007/s12063-024-00502-3.
[56] A. Dahane, R. Benameur, and B. Kechar, “An IoT Low-Cost Smart Farming for Enhancing Irrigation Efficiency of Smallholders Farmers,” Wirel Pers Commun, vol. 127, no. 4, pp. 3173–3210, Dec. 2022, doi: 10.1007/s11277-022-09915-4.
[57] S. Babu, S. Madhusudanan, M. Sathiyanarayanan, M. Z. Mortka, J. Szymański, and R. Rahul, “Soil mapping for farming productivity: internet of things (IoT) based sustainable agriculture,” Microsystem Technologies, 2024, doi: 10.1007/s00542-024-05608-z.
[58] S. Zyoud and A. H. Zyoud, “Internet of things supporting sustainable solid waste management: global insights, hotspots, and research trends,” International Journal of Environmental Science and Technology, 2024, doi: 10.1007/s13762-024-06146-x.
[59] A. C. R, A. K. Pani, and P. Kumar, “Blockchain-enabled Smart Contracts and the Internet of Things: Advancing the research agenda through a narrative review,” Multimed Tools Appl, 2024, doi: 10.1007/s11042-024-18931-4.
[60] M. Gao, A. Souri, M. Zaker, W. Zhai, X. Guo, and Q. Li, “A comprehensive analysis for crowd counting methodologies and algorithms in Internet of Things,” Cluster Comput, vol. 27, no. 1, pp. 859–873, Dec. 2024, doi: 10.1007/s10586-023-03987-y.
[61] C. S. kumar and R. V. Anand, “A Review of Energy-Efficient Secured Routing Algorithm for IoT-Enabled Smart Agricultural Systems,” Journal of Biosystems Engineering, vol. 48, no. 3, pp. 339–354, Dec. 2023, doi: 10.1007/s42853-023-00192-y.
[62] M. Rahaman, C. Y. Lin, P. Pappachan, B. B. Gupta, and C. H. Hsu, “Privacy- Centric AI and IoT Solutions for Smart Rural Farm Monitoring and Control,” Sensors, vol. 24, no. 13, Dec. 2024, doi: 10.3390/s24134157.
[63] W. Li, W. Dong, X. Zhang, and J. Zhang, “A New Remote Sensing Service Mode for Agricultural Production and Management Based on Satellite–Air–Ground Spatiotemporal Monitoring,” Agriculture (Switzerland), vol. 13, no. 11, Dec. 2023, doi: 10.3390/agriculture13112063.
[64] R. Benameur, A. Dahane, B. Kechar, and A. E. H. Benyamina, “An Innovative Smart and Sustainable Low- Cost Irrigation System for Anomaly Detection Using Deep Learning,” Sensors, vol. 24, no. 4, Dec. 2024, doi: 10.3390/s24041162.
[65] P. Indira, I. S. Arafat, R. Karthikeyan, S. Selvarajan, and P. K. Balachandran, “Fabrication and investigation of agricultural monitoring system with IoT & AI,” SN Appl Sci, vol. 5, no. 12, Dec. 2023, doi: 10.1007/s42452-023- 05526-1.
[66] Y. W. Lin, Y. B. Lin, T. C. Y. Chang, and B. X. Lu, “An Edge Transfer Learning Approach for Calibrating Soil Electrical Conductivity Sensors,” Sensors (Basel), vol. 23, no. 21, Dec. 2023, doi: 10.3390/s23218710.
[67] P. Majumdar, S. Mitra, D. Bhattacharya, and B. Bhushan, “Enhancing sustainable 5G powered agriculture 4.0 : Summary of low power connectivity, internet of UAV things, AI solutions and research trends,” Multimed Tools Appl, 2024, doi: 10.1007/s11042-024-19728-1.
[68] O. H. Abdelkader, H. Bouzebiba, D. Pena, and A. P. Aguiar, “Energy- Efficient IoT-Based Light Control System in Smart Indoor Agriculture,” Sensors, vol. 23, no. 18, Dec. 2023, doi: 10.3390/s23187670.
[69] A. Siddique, J. Sun, K. J. Hou, M. I. Vai, S. H. Pun, and M. A. Iqbal, “SpikoPoniC: A Low-Cost Spiking Neuromorphic Computer for Smart Aquaponics,” Agriculture (Switzerland), vol. 13, no. 11, Dec. 2023, doi:10.3390/agriculture13112057.
[70] D. Kalfas, S. Kalogiannidis, O. Papaevangelou, K. Melfou, and F. Chatzitheodoridis, “Integration of Technology in Agricultural Practices towards Agricultural Sustainability: A Case Study of Greece,” Sustainability (Switzerland) , vol. 16, no. 7, Dec. 2024, doi: 10.3390/su16072664.
[71] M. Bhattacharyya et al., “Designing optimal middle-mile network architecture for smart farming applications in rural areas,” Innov Syst Softw Eng, 2024, doi: 10.1007/s11334- 024-00574-1.
[72] N. J. Lemphane, B. Kotze, and R. B. Kuriakose, “Developing a Digital Twin Model for Improved Pasture Management at Sheep Farm to Mitigate the Impact of Climate Change,” 2024. [Online]. Available: www.ijacsa.thesai.org
[73] I. K. Gandhi, “AIoT-Driven Edge Computing for Rural Small-Scale Poultry Farming: Smart Environmental Monitoring and Anomaly Detection for Enhanced Productivity,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 8, pp. 44–52, Dec. 2023, doi: 10.17762/ijritcc.v11i8.7923.
[74] Z. A. Pampori and A. A. Sheikh, “Technology driven livestock farming for food security and sustainability,” Environ Conserv J, vol. 24, no. 4, pp. 355–366, Dec. 2023, doi: 10.36953/ECJ.15072477.
[75] J. Zhang et al., “Achieving the Rewards of Smart Agriculture,” Agronomy, vol. 14, no. 3, Dec. 2024, doi: 10.3390/agronomy14030452.
[76] M. H. Widianto, Y. D. Setiawan, B. Ghilchrist, and G. Giovan, “Smart farming based on IoT to predict conditions using machine learning,” International Journal of Reconfigurable and Embedded Systems, vol. 13, no. 3, pp. 595–603, Dec. 2024, doi: 10.11591/ijres.v13.i3.pp595 603.
[77] N. Ghavipanje, M. H. F. Nasri, and E. Vargas-Bello-Pérez, “Trends and future directions of artificial intelligence applications in Iranian livestock production systems,” Annals of Animal Science, 2024, doi: 10.2478/aoas-2024-0098.
[78] G. Gebresenbet et al., “A concept for application of integrated digital technologies to enhance future smart agricultural systems,” Smart Agricultural Technology, vol. 5, Dec. 2023, doi: 10.1016/j.atech.2023.100255.
[79] A. Morchid, R. El Alami, A. A. Raezah, and Y. Sabbar, “Applications of internet of things (IoT) and sensors technology to increase food security and agricultural Sustainability: Benefits and challenges,” Ain Shams Engineering Journal, vol. 15, no. 3, Dec. 2024, doi: 10.1016/j.asej.2023.102509.
[80] P. Rajak, A. Ganguly, S. Adhikary, and S. Bhattacharya, “Internet of Things and smart sensors in agriculture: Scopes and challenges,” J Agric Food Res, vol. 14, Dec. 2023, doi: 10.1016/j.jafr.2023.100776.
[81] B. Fasciolo, A. Awouda, G. Bruno, and E. Lombardi, “A smart aeroponic system for sustainable indoor farming,” in Procedia CIRP, Elsevier B.V., 2023, pp. 636–641. doi: 10.1016/j.procir.2023.02.107.
[82] M. G. S. Wicaksono, E. Suryani, and R. Hendrawan, “Increasing productivity of rice plants based on IoT (Internet of Things) to realize Smart Agriculture using System Thinking approach,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 607–616. doi: 10.1016/j.procs.2021.12.179.
[83] H. Liang, W. Gao, J. H. Nguyen, M. F. Orpilla, and W. Yu, “Internet of Things Data Collection Using Unmanned Aerial Vehicles in Infrastructure Free Environments,” IEEE Access, vol. 8,pp. 3932–3944, 2020, doi: 10.1109/ACCESS.2019.2962323.
[84] V. P. Kour and S. Arora, “Recent Developments of the Internet of Things in Agriculture: A Survey,” 2020, Institute of of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2020.3009298.
[85] R. Y. Aburasain, “Enhanced Black Widow Optimization With Hybrid Deep Learning Enabled Intrusion Detection in Internet of Things-Based Smart Farming,” IEEE Access, vol. 12, pp. 16621–16631, 2024, doi: 10.1109/ACCESS.2024.3359043.
[86] M. N. Mowla, N. Mowla, A. F. M. S. Shah, K. M. Rabie, and T. Shongwe, “Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey,” IEEE Access, vol. 11, pp. 145813–145852, 2023, doi: 10.1109/ACCESS.2023.3346299.
[87] M. F. Alumfareh, M. Humayun, Z. Ahmad, and A. Khan, “An Intelligent LoRaWAN-based IoT Device for Monitoring and Control Solutions in Smart Farming through anomaly detection integrated with unsupervised machine learning,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3450587.
[88] K. Huang et al., “Photovoltaic agricultural internet of things towards realizing the next generation of smart farming,” IEEE Access, vol. 8, pp. 76300–76312, 2020, doi: 10.1109/ACCESS.2020.2988663.
[89] M. S. Farooq, S. Riaz, M. A. Helou, F.
S. Khan, A. Abid, and A. Alvi, “Internet of Things in Greenhouse Agriculture: A Survey on Enabling Technologies, Applications, and Protocols,” IEEE Access, vol. 10, pp. 53374–53397, 2022, doi: 10.1109/ACCESS.2022.3166634.
[90] R. Alfred, J. H. Obit, C. P. Y. Chin, H. Haviluddin, and Y. Lim, “Towards paddy rice smart farming: A review on big data, machine learning, and rice production tasks,” 2021, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2021.3069449.
[91] A. Pagano, D. Croce, I. Tinnirello, and Vitale, “A Survey on LoRa for Smart Agriculture: Current Trends and Future Perspectives,” IEEE Internet Things J, vol. 10, no. 4, pp. 3664–3679, Dec. 2023, doi: 10.1109/JIOT.2022.3230505.
[92] A. Massaoudi, A. Berguiga, A. Harchay, M. Ben Ayed, and H. Belmabrouk, “Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems,” Applied Sciences (Switzerland), vol. 13, no. 19, Dec. 2023, doi: 10.3390/app131910739.
Downloads
Published
How to Cite
License
Copyright (c) 2025 Aswin Rosadi, Mokh Sholihul Hadi
This work is licensed under a Creative Commons Attribution 4.0 International License.
