A Secure, Scalable System for Precision Agriculture Using IoT, Blockchain, and Predictive Analytics
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Abstract
This paper aims to advance precision agriculture by developing a modular, scalable system that integrates IoT sensors, blockchain security, and a marketplace insights dashboard. Current systems often face challenges related to data security, scalability, and limited alignment with market needs. To address these issues, we designed a modular architecture that allows flexible deployment and replacement of sensors, making it adaptable to various agricultural environments. Blockchain technology is used to secure and validate data from IoT devices, improving data integrity and transparency by approximately 30%. Predictive analytics models, including Random Forests and neural networks, were employed for crop yield forecasting and plant stress detection, reducing prediction errors by 20-25% compared to traditional methods. The system features a decision support dashboard that offers actionable recommendations for irrigation, fertilization, and pest control, optimizing resource use and reducing waste by up to 15%. Additionally, a market insights dashboard synchronizes production strategies with real-time market trends, potentially increasing profitability by 10-20%. Despite its advantages, the system requires high computational resources and more region-specific data. Future research should focus on improving scalability and computational efficiency to expand its applicability across diverse agricultural contexts.
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References
M. Torky and A. E. Hassanein, “Integrating blockchain and the Internet of Things in precision agriculture: Analysis, opportunities, and challenges,” Comput. Electron. Agric., vol. 178, p. 105476, 2020.
O. Lamtzidis, D. Pettas, and J. Gialelis, “A novel combination of distributed ledger technologies on Internet of Things: Use case on precision agriculture,” Appl. Syst. Innov., vol. 2, no. 3, p. 30, 2019.
W. Liu, X. F. Shao, C. H. Wu, and P. Qiao, “A systematic literature review on applications of information and communication technologies and blockchain technologies for precision agriculture development,” J. Clean. Prod., vol. 298, p. 126763, 2021.
G. Pradeep, S. Ramamoorthy, M. Krishnamurthy, P. S. Rajakumar, and V. Saritha, “Hybrid Energy-Efficient Task Offloading Algorithm (HEETA): A framework for optimizing edge computing offloading decisions,” J. Electr. Syst., vol. 20, no. 5s, 2024. doi: 10.52783/jes.1835.
G. Pradeep, S. Ramamoorthy, M. Krishnamurthy, and V. Saritha, “Energy prediction and task optimization for efficient IoT task offloading and management,” Int. J. Intell. Syst. Appl. Eng., vol. 12, no. 1s, pp. 411–427, 2023.
W. Lin et al., “Blockchain technology in current agricultural systems: From techniques to applications,” IEEE Access, vol. 8, pp. 143920–143937, 2020.
K. Dey and U. Shekhawat, “Blockchain for sustainable e-agriculture: Literature review, architecture for data management, and implications,” J. Clean. Prod., vol. 316, p. 128254, 2021.
R. Chaganti, V. Varadarajan, V. S. Gorantla, T. R. Gadekallu, and V. Ravi, “Blockchain-based cloud-enabled security monitoring using Internet of Things in smart agriculture,” Future Internet, vol. 14, no. 9, p. 250, 2022.
L. Hang, I. Ullah, and D. H. Kim, “A secure fish farm platform based on blockchain for agriculture data integrity,” Comput. Electron. Agric., vol. 170, p. 105251, 2020.
M. Pincheira, M. Vecchio, and R. Giaffreda, “Characterization and costs of integrating blockchain and IoT for agri-food traceability systems,” Systems, vol. 10, no. 3, p. 57, 2022.
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.
A. Z. Babar and Ö. B. Akan, “Sustainable and precision agriculture with the Internet of Everything (IoE),” arXiv preprint, arXiv:2404.06341, 2024.
O. Amraouy, Y. Boukhali, A. Bouazi, M. N. Kabbaj, and M. Benbrahim, “Blockchain-based IoT for precision agriculture: Applications, research challenges, and future directions,” in Enhancing Performance, Efficiency, and Security Through Complex Systems Control, pp. 147–174, 2024.
S. Hu, S. Huang, J. Huang, and J. Su, “Blockchain and edge computing technology enabling organic agricultural supply chain: A framework solution to trust crisis,” Comput. Ind. Eng., vol. 153, p. 107079, 2021.
A. Kharche, S. Badholia, and R. K. Upadhyay, “Implementation of blockchain technology in integrated IoT networks for constructing scalable ITS systems in India,” Blockchain: Res. Appl., p. 100188, 2024.
A. A. Khan et al., “A blockchain and metaheuristic-enabled distributed architecture for smart agricultural analysis and ledger preservation solution: A collaborative approach,” Appl. Sci., vol. 12, no. 3, p. 1487, 2022.
K. Demestichas and E. Daskalakis, “Data lifecycle management in precision agriculture supported by information and communication technology,” Agronomy, vol. 10, no. 11, p. 1648, 2020.
R. Patel, “Crop yield prediction dataset,” Kaggle. [Online]. Available: https://www.kaggle.com/datasets/patelris/crop-yield-prediction-dataset