IoT and AI-based Plant Monitoring System
Abstract
Plants plays a vital role in the environment because it provides the health support through absorbing the carbon dioxide and releasing the oxygen to the atmosphere. Although, it required to maintain the proper plant growth and health as well as provide the appropriate monitoring. To overcome these concerns, an Artificial Intelligence (AI) and Internet of Things (IoT) based solution is proposed to monitor the plant’s growth and health. This study demonstrates the real-time monitoring of the plants via environmental sensors such as DHT 11 and soil moisture sensors. Real-time values stored in the cloud server and applied the machine learning models to predict the plant’s growth. The Statistical parameters such as RMSE, MAE are used to analyze the resulting outcome from the system.
References
Kohli, A., Kohli, R., Singh, B., & Singh, J. (2020). Smart plant monitoring system using IoT technology. In Handbook of Research on the Internet of Things Applications in Robotics and Automation (pp. 318-366). IGI Global.
bin Sadli, M. D. D. (2019, April). An IoT-based Smart Garden with Weather Station System. In 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE) (pp. 38-43). IEEE.
Puri, V., Chandramouli, M., Van Le, C., & Hoa, T. H. (2020, March). Internet of Things and Fuzzy logic based hybrid approach for the Prediction of Smart Farming System. In 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA) (pp. 1-5). IEEE.
Siddagangaiah, S. (2016). A novel approach to IoT-based plant health monitoring system. Int. Res. J. Eng. Technol, 3(11), 880-886.
Tangworakitthaworn, P., Tengchaisri, V., Rungsuptaweekoon, K., & Samakit, T. (2018, July). A game-based learning system for plant monitoring based on IoT technology. In 2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp. 1-5). IEEE.
Ezhilazhahi, A. M., & Bhuvaneswari, P. T. V. (2017, May). IoT enabled plant soil moisture monitoring using wireless sensor networks. In 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS) (pp. 345-349). IEEE.
Pavel, M. I., Kamruzzaman, S. M., Hasan, S. S., & Sabuj, S. R. (2019, February). An IoT based plant health monitoring system implementing image processing. In 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) (pp. 299-303). IEEE.
Athawale, S. V., Solanki, M., Sapkal, A., Gawande, A., & Chaudhari, S. (2020). An IoT-Based Smart Plant Monitoring System. In Smart Computing Paradigms: New Progresses and Challenges (pp. 303-310). Springer, Singapore.
Singh, R., Srivastava, S., & Mishra, R. (2020, February). AI and IoT Based Monitoring System for Increasing the Yield in Crop Production. In 2020 International Conference on Electrical and Electronics Engineering (ICE3) (pp. 301-305). IEEE.
Ragavi, B., Pavithra, L., Sandhiyadevi, P., Mohanapriya, G. K., & Harikirubha, S. (2020, March). Smart Agriculture with AI Sensor by Using Agrobot. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1-4). IEEE.
Bhanu, K. N., Jasmine, H. J., & Mahadevaswamy, H. S. (2020, June). Machine learning Implementation in IoT based Intelligent System for Agriculture. In 2020 International Conference for Emerging Technology (INCET) (pp. 1-5). IEEE.
Walczak, S. (2019). Artificial neural networks. In Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction (pp. 40-53). IGI Global.
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
Hebb, D. O. (1949). The organization of behavior: a neuropsychological theory. J. Wiley; Chapman & Hall.
Quek, S. G., Selvachandran, G., Munir, M., Mahmood, T., Ullah, K., Son, L. H., ... & Priyadarshini, I. (2019). Multi-attribute multi-perception decision-making based on generalized T-spherical fuzzy weighted aggregation operators on neutrosophic sets. Mathematics, 7(9), 780.
Gholami, R., & Fakhari, N. (2017). Support vector machine: principles, parameters, and applications. In Handbook of Neural Computation (pp. 515-535). Academic Press.
Martínez-Ramón, M., & Christodoulou, C. (2005). Support vector machines for antenna array processing and electromagnetics. Synthesis Lectures on Computational Electromagnetics, 1(1), 1-120.
Kecman, V., & Wang, L. (2005). Support vector machines: theory and applications.
Tuan, T. A., Long, H. V., Kumar, R., Priyadarshini, I., & Son, N. T. K. (2019). Performance evaluation of Botnet DDoS attack detection using machine learning. Evolutionary Intelligence, 1-12.
Gay, W. (2018). DHT11 sensor. In Advanced Raspberry Pi (pp. 399-418). Apress, Berkeley, CA.
Singh, P., & Saikia, S. (2016, December). Arduino-based smart irrigation using water flow sensor, soil moisture sensor, temperature sensor and ESP8266 WiFi module. In 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 1-4). IEEE.
Maureira, M. A. G., Oldenhof, D., & Teernstra, L. (2011). ThingSpeak–an API and Web Service for the Internet of Things. World Wide Web.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 International Journal of Machine Learning and Networked Collaborative Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
https://creativecommons.org/licenses/by/4.0/legalcode
