Volume 14, Issue 9

Precision Farming Using Edge Analytics and Edge Intelligence: Literature Review

Author

Parul Saxena, Rakesh Kumar

Abstract

Abstract 
Precision Farming integrates advanced technologies to enhance crop production while maintaining environmental sustainability. By leveraging the Internet of Things (IoT), sensors are strategically deployed in fields to monitor agricultural parameters in real time. The integration of Edge Analytics further minimizes latency by enabling faster data processing at the edge, while Edge Intelligence enhances systems with decision-making capabilities. This study compares various computational models and technological approaches within Precision Farming, highlighting their effectiveness in achieving optimized crop growth. The comparative analysis also synthesizes insights from existing literature, offering valuable perspectives for researchers and practitioners in sustainable agriculture. Conclusively, Edge Intelligence and Edge Analytics hold significant potential for advancing precision agriculture, their integration in this domain has received relatively limited research attention.

REFERENCES
[1] R. K. Singh, R. Berkvens, and M. Weyn, “AgriFusion: An architecture for IoT and emerging technologies based on a precision agriculture survey,” IEEE Access, vol. 9, pp. 136253–136283, 2021, doi: 10.1109/ACCESS.2021.3116814.
[2] S. Revathy and S. S. Priya, “Blockchain based producer-consumer model for farmers,” in Proc. 4th Int. Conf. Computer, Communication and Signal Processing (ICCCSP), 2020, pp. 1–5, doi: 10.1109/ICCCSP49186.2020.9315214.
[3] M. A. Ferrag, L. Shu, X. Yang, A. Derhab, and L. Maglaras, “Security and privacy for green IoT-based agriculture: Review, blockchain solutions, and challenges,” IEEE Access, vol. 8, pp. 32031–32053, 2020, doi: 10.1109/ACCESS.2020.2973178.
[4] P. Radoglou-Grammatikis, P. Sarigiannidis, T. Lagkas, and E. Moscholios, “A compilation of UAV applications for precision agriculture,” Comput. Netw., vol. 172, p. 107148, 2020.
[5] D. C. Tsouros, S. Bibi, and P. G. Sarigiannidis, “A review on UAV-based applications for precision agriculture,” Information, vol. 10, no. 11, p. 349, 2019, doi: 10.3390/info10110349.
[6] P. Angin, M. H. Anisi, F. Göksel, C. Gürsoy, and A. Büyükgülcü, “AgriLoRa: A digital twin framework for smart agriculture,” J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl., vol. 11, no. 4, pp. 77–96, 2020.
[7] R. P. Sishodia, R. L. Ray, and S. K. Singh, “Applications of remote sensing in precision agriculture: A review,” Remote Sens., vol. 12, no. 19, p. 3136, 2020.
[8] M. Merz et al., “Autonomous UAS-based agriculture applications: General overview and relevant European case studies,” Drones, vol. 6, no. 5, p. 128, 2022.
[9] S. A. Bhat and N.-F. Huang, “Big data and AI revolution in precision agriculture: Survey and challenges,” IEEE Access, vol. 9, pp. 110209–110222, 2021, doi: 10.1109/ACCESS.2021.3102227.
[10] A. Caruso, S. Chessa, S. Escolar, J. Barba, and J. C. López, “Collection of data with drones in precision agriculture: Analytical model and LoRa case study,” IEEE Internet Things J., vol. 8, no. 22, pp. 16692–16704, Nov. 2021, doi: 10.1109/JIOT.2021.3075561.
[11] A. Rejeb, A. Abdollahi, K. Rejeb, and H. Treiblmaier, “Drones in agriculture: A review and bibliometric analysis,” Comput. Electron. Agric., vol. 198, p. 107017, 2022, doi: 10.1016/j.compag.2022.107017.
[12] M. Gardezi, D. T. Adereti, R. Stock, and A. Gunyiola, “In pursuit of responsible innovation for precision agriculture technologies,” J. Responsible Innov., 2022, doi: 10.1080/23299460.2022.2071668.
[13] D. K. Singh, R. Sobti, A. Jain, P. K. Malik, and D. N. Le, “LoRa based intelligent soil and weather condition monitoring with internet of things for precision agriculture in smart cities,” IET Commun., vol. 16, no. 5, pp. 604–618, 2022.
[14] T. Anand, S. Sinha, M. Mandal, V. Chamola, and F. R. Yu, “AgriSegNet: Deep aerial semantic segmentation framework for IoT-assisted precision agriculture,” IEEE Sens. J., vol. 21, no. 16, pp. 17581–17590, 2021.
[15] R. Akhter and S. A. Sofi, “Precision agriculture using IoT data analytics and machine learning,” J. King Saud Univ.-Comput. Inf. Sci., 2021.
[16] M. Cicioğlu and A. Çalhan, “Smart agriculture with internet of things in cornfields,” Comput. Electr. Eng., vol. 90, p. 106982, 2021.
[17] M. N. Akhtar et al., “Smart sensing with edge computing in precision agriculture for soil assessment and heavy metal monitoring: A review,” Agriculture, vol. 11, no. 6, p. 475, 2021.
[18] S. J. Anand, “IoT-based secure and energy efficient scheme for precision agriculture using blockchain and improved leach algorithm,” Turkish J. Comput. Math. Educ. (TURCOMAT), vol. 12, no. 10, pp. 2466–2475, 2021.
[19] S. U. Amin and M. S. Hossain, “Edge intelligence and internet of things in healthcare: A survey,” IEEE Access, vol. 9, pp. 45–59, 2021, doi: 10.1109/ACCESS.2020.3045115.
[20] G. Plastiras, M. Terzi, C. Kyrkou, and T. Theocharidcs, “Edge intelligence: Challenges and opportunities of near-sensor machine learning applications,” in Proc. 29th Int. Conf. Application-Specific Systems, Architectures and Processors (ASAP), 2018, pp. 1–7, doi: 10.1109/ASAP.2018.8445118.
[21] J. Liu et al., “Boost precision agriculture with unmanned aerial vehicle remote sensing and edge intelligence: A survey,” Remote Sens., vol. 13, no. 21, p. 4387, 2021.
[22] D. Shadrin et al., “Enabling precision agriculture through embedded sensing with artificial intelligence,” IEEE Trans. Instrum. Meas., vol. 69, no. 7, pp. 4103–4113, Jul. 2020, doi: 10.1109/TIM.2019.2947125.
[23] D. I. Patrício and R. Rieder, “Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review,” Comput. Electron. Agric., vol. 153, pp. 69–81, 2018.
[24] H. Li, K. Ota, and M. Dong, “Learning IoT in edge: Deep learning for the internet of things with edge computing,” IEEE Netw., vol. 32, no. 1, pp. 96–101, Jan.–Feb. 2018, doi: 10.1109/MNET.2018.1700202.
[25] S. Nayak, R. Patgiri, L. Waikhom, and A. Ahmed, “A review on edge analytics: Issues, challenges, opportunities, promises, future directions, and applications,” Digit. Commun. Netw., 2022.
[26] M. Satyanarayanan, “The emergence of edge computing,” Computer, vol. 50, no. 1, pp. 30–39, 2017.
[27] Y. Zhang and Y. Liu, “Edge intelligence: The convergence of edge computing and artificial intelligence,” IEEE Internet Things J., 2019.
[28] Y. Shi, K. Yang, T. Jiang, J. Zhang, and K. B. Letaief, “Communication-efficient edge AI: Algorithms and systems,” IEEE Commun. Surveys Tuts., vol. 22, no. 4, pp. 2167–2191, 4th Quart. 2020, doi: 10.1109/COMST.2020.3007787.
[29] Y. Sahni, J. Cao, S. Zhang, and L. Yang, “Edge mesh: A new paradigm to enable distributed intelligence in internet of things,” IEEE Access, vol. 5, pp. 16441–16458, 2017, doi: 10.1109/ACCESS.2017.2739804.
[30] D. Xu et al., “Edge intelligence: Empowering intelligence to the edge of network,” Proc. IEEE, vol. 109, no. 11, pp. 1778–1837, 2021.
[31] X. Wang et al., “Towards edge intelligence: A comprehensive survey on edge AI,” IEEE Trans. Emerg. Topics Comput., 2021.
[32] K. Bhargava, S. Ivanov, W. Donnelly, and C. Kulatunga, “Using edge analytics to improve data collection in precision dairy farming,” in Proc. 41st Conf. Local Computer Networks Workshops (LCN Workshops), 2016, pp. 137–144.
[33] A. Khattab, A. Abdelgawad, and K. Yelmarthi, “Design and implementation of a cloud-based IoT scheme for precision agriculture,” in Proc. 28th Int. Conf. Microelectronics (ICM), 2016, pp. 201–204.
[34] J. Muangprathub, N. Boonnam, S. Kajornkasirat, N. Lekbangpong, A. Wanichsombat, and P. Nillaor, “IoT and agriculture data analysis for smart farm,” Comput. Electron. Agric., vol. 156, pp. 467–474, 2019
 

DOI

https://doi.org/10.62226/ijarst20252578

PAGES : 1647 | 1652 VIEWS | 0 DOWNLOADS

Download Full Article