2771-1463ISSN (Online)
2771-1471ISSN (Print)
Volume 5 , Issue 1 , PP: 32-41, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
J. Chandraleka 1 * , P. Selvaraj 2
Doi: https://doi.org/10.54216/JCHCI.050103
Agricultural use of alternative energy has become more prevalent. Utilizing the alternative source when it is widely accessible is economical and prudent. Drip irrigation may be even more effective when alleviated with renewable energy via a power grid link. Fog computing is a cutting-edge method for extending cloud services to the network's edge. With compute and storage capabilities, it offers a widely dispersed, virtualized platform. Fog could analyze vast volumes of data before sending it to the cloud. This work proposes an innovative agricultural system with integrated hydropower management and its functional blocks. For processing and decision- making in this system, the fog router received field data from the aggregator. To use the data for analysis in the future, it will be stored in the cloud. We have constructed an intelligent irrigation and power management system based on the IoT in our suggested design with IIPMS. This prototype model detects heat and light using temperature and light sensors. If this dual parameter is discovered to be sufficient, the intelligent switch automates the switchover to solar power. The gadget and motor operate on a regular power supply from the power plant. Through GSM technology, the cloud informs the farmer about the type of electricity being used and information linked to power, such as voltage. To inform the farmer of the availability of solar power, a built-in prediction module was also proposed with the Time Series Analysis based forecasting to carry out forecasting duties (TSA). Based on the simulation study, we claimed that the proposed approach performs better in various real-world agricultural scenarios. We also compared our energy consumption model with the existing models and claimed the efficacies of the proposed approach.
Smart Farming , IoT , Fog Computing , Dual Power management
[1].Liu, Zhiyan, et al. "Internet of Things (IoT)andMachine Learning Model of Plant Disease Prediction–Blister Blight for Tea Plant." IEEE Access 10 (2022): 44934-44944.
[2]. Buja, I.; Sabella, E.; Monteduro, A.G.; Chiriacò, M.S.; De Bellis, L.; Luvisi, A.; Maruccio, G. “Advances in Plant Disease Detection and Monitoring: From Traditional Assays to In-Field Diagnostics”. Sensors 2021
[3]. Suma, V. "Internet-of-Things (IoT) based Smart Agriculture in India-An Overview." Journal of ISMAC 3.01 (2021): 1-15.
[4]. Ponnusamy V, Natarajan S, Ramasamy N, Clement JC, Rajalingam P, Mitsunori M. “An IoT-Enabled Augmented Reality Framework for Plant Disease Detection”. Rev. d'Intelligence Artif.. 2021 Jun;35(3):185-92.
[5]. Rokade, Ashay, et al. "IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms." Computational and Mathematical Methods in Medicine 2022 (2022).
[6]. More, Sujeet, and Jimmy Singla. "Machine learning techniques with IoT in agriculture." International Journal of Advanced Trends in Computer Science and Engineering 8, no. 3 (2019): 742-747.
[7]. Maduranga, M. W. P., and Ruvan Abeysekera. "Machine learning applications in IoT based agriculture and smart farming: A review." Int. J. Eng. Appl. Sci. Technol 4.12 (2020): 24-27.
[8]. Rokade A, Singh M, Arora SK, Nizeyimana E.IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms. Computational and Mathematical Methods in Medicine. 2022 Aug 30;2022.
[9]. Mishra M, Choudhury P, Pati B. “Modified ride-NN optimizer for the IoT based plant disease detection”. Journal of Ambient Intelligence and Humanized Computing. 2021 Jan;12(1):691-703.
[10]. Akhter, Ravesa, and Shabir Ahmad Sofi. "Precision agriculture using IoT data analytics and machine learning." Journal of King Saud University-Computer and Information Sciences 34.8 (2022): 5602-5618.
[11]. Zuo, Xin, et al. "Route Optimization of Agricultural Product Distribution Based on Agricultural Iot and Neural Network from the Perspective of Fabric Blockchain." Wireless Communications and Mobile Computing 2022 (2022).
[12]. Doshi, Jash, Tirthkumar Patel, and Santosh kumar Bharti. "Smart Farming using IoT, a solution for optimally monitoring farming conditions." Procedia Computer Science 160 (2019)
[13]. https://www.kaggle.com/datasets/venky73/temperatur es-of-india?resource=download [14]. Gopal Chaudhary, smriti Srivastava, Manju Khari , “Generative Edge Intelligence for Securing IoT-assisted Smart Grid against Cyber-Threats”, International Journal of Wireless and Ad Hoc Communication” Volume 6, Issue 1, pp 38-49, 2023.
[15]. Narayanan, L. K., Subbaiyah, P., Gururajan,, Sampathkumar, R., & Muralidharan, R. R. A. (2021). IoT-Fog integrated voice of the plant based smart irrigation and power management system for smart farming. NVEO-Natural Volatiles & Essential Oils Journal| NVEO, 5411-5421.
