Authors :
Nancy Raghav; Shaleen Rai; Utsav Kumar Singh
Volume/Issue :
Volume 9 - 2024, Issue 12 - December
Google Scholar :
https://tinyurl.com/y48k8hxm
Scribd :
https://tinyurl.com/murwcxh4
DOI :
https://doi.org/10.5281/zenodo.14945042
Abstract :
Agriculture is at a pivotal point in addressing global challenges such as food security, environmental sustainability,
and resource efficiency, driven by a rapidly growing population and the impacts of climate change. Traditional farming
methods, while effective in earlier eras, are insufficient to meet these challenges, necessitating the adoption of advanced
technologies like the Internet of Things (IoT), artificial intelligence (AI), and cloud analytics. These innovations enable
precision agriculture, which leverages data-driven decision-making to enhance productivity, optimize resource utilization,
and minimize environmental impact.
This review focuses on the integration of IoT and cloud analytics within the framework of smart farming systems,
highlighting the transformative potential of real-time data collection, predictive modelling and user-centric interfaces. The
study critically examines state-of-the-art solutions such as IoT-enabled sensors for soil and crop monitoring, cloud platforms
for data aggregation and real-time analytics, and AI-based algorithms for predictive and prescriptive insights. While these
advancements demonstrate significant promise, challenges such as data security, system scalability, and accessibility for
smallholder farmers remain pressing.
In light of these gaps, the proposed "Smart Farming System With Cloud Analytics" aims to address critical limitations
by offering a scalable, cost-effective, and user-friendly platform that integrates real-time IoT data, predictive analytics, and
region-specific insights. By leveraging open-source technologies, the system provides intuitive dashboards that empower
farmers with actionable recommendations, regardless of technical expertise. By bridging the gap between cutting-edge
innovations and practical applications, the "Smart Farming System with Cloud Analytics" has the potential to redefine the
agricultural landscape, fostering a more productive and sustainable future.
References :
- Lytos, T. Lagkas, P. Sarigiannidis, "Towards smart farming: systems, frameworks and exploitation of multiple sources," Computer Networks, vol. 172, 2020. DOI: 10.1016/j.comnet.2020.107147.
- F. M. Javed Mehedi Shamrat, A.K.M. Sazzadur Rahman, Zarrin Tasnim, et al., “A Smart Automated System Model for Vehicles Detection to Maintain Traffic by Image Processing,” International Journal of Scientific & Technology Research, vol. 9, no. 2, Feb. 2020, pp. 2921–2928.
- S. Wolfert, L. Ge, C. Verdouw, et al., "Big Data in Smart Farming – A review," Agricultural Systems, vol. 153, 2017, pp. 69–80. DOI: 10.1016/j.agsy.2017.01.023.
- M. Javed Mehedi Shamrat, Naimul Islam Nobel, Zarrin Tasnim, et al., "Implementation of a Smart Embedded System for Passenger Vessel Safety," International Conference on Computational Intelligence, Security & IoT (ICCISIoT), vol. 1192, Mar. 2020, pp. 357–370. DOI: 10.1007/978-981-15-3666-3_29.
- G. Barrett, I. Nitze, S. Green, et al., "Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches," Remote Sensing of Environment, vol. 152, 2014, pp. 109–124. DOI: 10.1016/j.rse.2014.05.018.
- Sjaak Wolfert, Lan Ge, Cor Verdouw, Marc-Jeroen Bagaardt “Big Data in Smart Farming – A review” Agriculture Systems, Vol 152, May 2017, Pages 69-80
- Biswaranjan Acharya, Kyvalya Garikapati, Anuradha Yarlagadda, Sujata Dash, “Internet of things (IoT) and data analytics in smart agriculture: Benefits and challenges” Ai, Edge and Iot-based Smart Agriculture, Intelligent Data-Centric Systems, 2022, Pages 3-16
- Mohammad Amiri-Zarandi, Rozita A. Dara, Emily Duncan, Evan D. G. Fraser “Big Data Privacy in Smart Farming: A Review” Frontiers in Agrifood Value Chain and Sustainable Agriculture Economics, 25-July-2022
- Muthumanickam Dhanaraju, Poongodi Chenniappan, Kumaraperumal Ramalingam, Sellaperumal Pazhanivelan, Ragunath Kaliaperumal “Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture” Digital Innovations in Agriculture, 21 october 2022
- Chinling Li, Ben Niu “Design of smart agriculture based on big data and Internet of thing” International Journal of Distributed Sensor Networks, Vol 16, Issue 5, May 2020
- Lytos, A., Lagkas, T., Sarigiannidis, P., et al. "Towards smart farming: systems, frameworks, and exploitation of multiple sources." Computer Networks, vol. 172, 2020, DOI: 10.1016/j.comnet.2020.107147.
- Kalatzis, N., Stylianou, A., & Giannakopoulou, M. (2020). Smart Farming Techniques for Climate Change Adaptation in Cyprus. Atmosphere, 11(6), 557.
Agriculture is at a pivotal point in addressing global challenges such as food security, environmental sustainability,
and resource efficiency, driven by a rapidly growing population and the impacts of climate change. Traditional farming
methods, while effective in earlier eras, are insufficient to meet these challenges, necessitating the adoption of advanced
technologies like the Internet of Things (IoT), artificial intelligence (AI), and cloud analytics. These innovations enable
precision agriculture, which leverages data-driven decision-making to enhance productivity, optimize resource utilization,
and minimize environmental impact.
This review focuses on the integration of IoT and cloud analytics within the framework of smart farming systems,
highlighting the transformative potential of real-time data collection, predictive modelling and user-centric interfaces. The
study critically examines state-of-the-art solutions such as IoT-enabled sensors for soil and crop monitoring, cloud platforms
for data aggregation and real-time analytics, and AI-based algorithms for predictive and prescriptive insights. While these
advancements demonstrate significant promise, challenges such as data security, system scalability, and accessibility for
smallholder farmers remain pressing.
In light of these gaps, the proposed "Smart Farming System With Cloud Analytics" aims to address critical limitations
by offering a scalable, cost-effective, and user-friendly platform that integrates real-time IoT data, predictive analytics, and
region-specific insights. By leveraging open-source technologies, the system provides intuitive dashboards that empower
farmers with actionable recommendations, regardless of technical expertise. By bridging the gap between cutting-edge
innovations and practical applications, the "Smart Farming System with Cloud Analytics" has the potential to redefine the
agricultural landscape, fostering a more productive and sustainable future.