LightAgro: Lightweight Blockchain IoT Based Fabrication for Smart Irrigation
DOI:
https://doi.org/10.13052/jmm1550-4646.213413Keywords:
SF, SI, LightAgro, ML, SC, BlockchainAbstract
India is supposed to be the global agricultural powerhouse. Around three-quarters of Indian family rely on agriculture for their livelihood. Water is the most crucial resource that must be accounted for to create a better irrigation system. The introduction of IIoT 4.0 also expanded its contribution to smart irrigation (SI). IoT-based innovative irrigation management achieves optimum water-resource utilization, bridging the gap between cyber-physical Systems (CPS). This paper improves the prediction rate needed for irrigation by sensing different parameters like temperature, pH value, humidity, NPK fertilizer, and rainfall of the crop field. In this paper, Deep Learning, an AI-based proposed system, integrates remote sensor data to predict irrigation in intelligent agriculture correctly. As open-source technology is involved in decision-making, security and trust issues are at stake. We propose a shallow neural network model, namely, LightAgro autonomous network, to recover intruder issues. LightAgro outputs (local prediction) are securely signed using a lightweight secp256k1 curve for users’ authentication. The vast amount of sensor data stored in client-server architecture in traditional systems is challenging. The result shows ML accuracy of 85.26 % using Gradient boosting techniques.
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R. Santhana Krishnan and E. Golden Julie and Y. Harold Robinson and S. Raja and Raghvendra Kumar and Pham Huy Thong and Le Hoang Son, Fuzzy Logic based Smart Irrigation System using Internet of Things, Journal of Cleaner Production, volume 252, pages 119902, year 2020.
Sitharthan R, Rajesh M, Vimal S, Saravana Kumar E, Yuvaraj S, Abhishek Kumar, Jacob Raglend I, Vengatesan K, A novel autonomous irrigation system for smart agriculture using AI and 6G enabled IoT network, Microprocessors and Microsystems, Volume 101,2023,104905, ISSN 0141-9331, doi.org/10.1016/j.micpro.2023.104905.
Hossain, Md Mamun and Rahman, Md Ashiqur and Chaki, Sudipto and Ahmed, Humayra and Haque, Ahsanul and Tamanna, Iffat and Lima, Sweety and Most, Jannatul Ferdous and Rahman, Md Saifur, International Journal of Advanced Computer Science and Applications, volume 14, number 7, year 2023.
Goap, Amarendra and Sharma, Deepak and Shukla, A Krishna and Krishna, C Rama, An IoT based smart irrigation management system using Machine learning and open source technologies, Elsevier, Computers and electronics in agriculture, volume-155, pages-41–49, 2018.
Jilito, Mideksa Fufa and Wedajo, Desalegn Yadeta and Feyisa, Bekele Wegi and Tuke, Tsegaye Woldetsadik, Water storage practices for small-scale irrigation systems in East Hararghe zone, Ethiopia, Water Supply, IWA Publishing, voume 21, number 4, pages 1674–1686, year 2021.
Khan, Asif Irshad and Alsolami, Fawaz and Alqurashi, Fahad and Abushark, Yoosef B and Sarker, Iqbal H, Novel energy management scheme in IoT enabled smart irrigation system using optimized intelligence methods, Elsevier, Engineering Applications of Artificial Intelligence, volume 114, pages 104996, year 2022.
Tiglao, Nestor Michael and Alipio, Melchizedek and Balanay, Jezy Verence and Saldivar, Eunice and Tiston, Jean Louise, Agrinex: A low-cost wireless mesh-based smart irrigation system, Elsevier, Measurement, volume 161, pages 107874, year 2020.
Zeng, Yuan-Fu and Chen, Ching-Tien and Lin, Gwo-Fong, Practical application of an intelligent irrigation system to rice paddies in Taiwan, Elsevier, Agricultural Water Management, volume 280, pages 108216, year 2023.
Nawandar, Neha K and Satpute, Vishal R, IoT based low cost and module for smart irrigation system, Elsevier, Computers and electronics in agriculture, volume 162, pages 979–990, year 2019.
Angelopoulos, Constantinos Marios and Filios, Gabriel and Nikoletseas, Sotiris and Raptis, Theofanis P, Keeping data at the edge of smart irrigation networks: A case study in strawberry greenhouses, Elsevier, Computer Networks, volume 167, pages 107039, year 2020.
Behzadipour intelligent, F., Ghasemi Nezhad Raeini, M., Abdanan Mehdizadeh, S. et al. A smart IoT-based irrigation system design using AI and prediction model. Neural Comput Applic 35, 24843–24857 (2023). https://doi.org/10.1007/s00521-023-08987-y.
Pius Agbulu, G and Joselin Retna Kumar, G, A LoRa-Based Internet of Things Smart Irrigation Control Solution with Hybrid Classifier CNN-SVM, Springer, Wireless Personal Communications, volume 137, number 1, pages 523–539, year 2024. Gamal, Yomna and Soltan, Ahmed and Said, Lobna A and Madian, Ahmed H and Radwan, Ahmed G, Smart irrigation systems: Overview, IEEE, IEEE Access, 2023.
Vallejo-Gomez, David and Osorio, Marisol and Hincapie, Carlos A, Smart irrigation systems in agriculture: A systematic review, MDPI, Agronomy, volume 13, number 2, pages 342, year 2023.
Nawandar, Neha K and Satpute, Vishal R, IoT based low cost and intelligent module for smart irrigation system, Elsevier, Computers and electronics in agriculture, volume 162, pages 979–990, 2019.
Laura and Parra, Lorena and Jimenez, Jose M and Lloret, Jaime and Lorenz, Pascal, IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture, MDPI, Sensors, volume 20, number 4, pages 1042, year 2020.
Gupta, Maanak and Abdelsalam, Mahmoud and Khorsandroo, Sajad and Mittal, Sudip, Security and privacy in smart farming: Challenges and opportunities, IEEE, IEEE access, volume 8, pages 34564–34584, year 2020.
Wolfert, Sjaak and Ge, Lan and Verdouw, Cor and Bogaardt, Marc-Jeroen, Big data in smart farming – a review, Elsevier, Agricultural systems, volume 153, pages 69—80, year 2017.
Patrizi, Gabriele and Bartolini, Alessandro and Ciani, Lorenzo and Gallo, Vincenzo and Sommella, Paolo and Carrat, A virtual soil moisture sensor for smart farming using deep learning, IEEE, IEEE Transactions on Instrumentation and Measurement, volume 71, pages 1–11, year 2022.
Mohy-eddine, Mouaad and Guezzaz, Azidine and Benkirane, Said and Azrour, Mourade, Malicious detection model with artificial neural network in IoT-based smart farming security, Springer, Cluster Computing, volume 27, pages 1–16, year 2024.
Cordeiro, Matheus and Markert, Catherine and Ara, Towards Smart Farming: Fog-enabled intelligent irrigation system using deep neural networks, Elsevier, Future Generation Computer Systems, volume 129, pages 115–124, year 2022.
