Fuzzy Mamdani Smart Control for Optimizing Melon Growth in Nutrient Film Technique (NFT) Hydroponic Greenhouse
Keywords:
Fuzzy Mamdani, Smart Greenhouse, NFT Hydroponics, Melon Growing, Control SystemsAbstract
The aim of the project is to create and apply an advanced control system using Fuzzy Mamdani to enhance the growth of melons in NFT hydroponic systems within intelligent greenhouses. The system is engineered to autonomously adjust temperature, humidity, pH and nutrient concentration through the utilization of sensors and actuators that are governed by microcontrollers. During a 30-day experiment, the Fuzzy Mamdani system demonstrated superior performance in comparison to the manual control system utilizing an on/off mechanism. The findings demonstrate that the Fuzzy Mamdani system effectively preserves the stability of environmental parameters, resulting in reduced fluctuations. This, in turn, has a beneficial influence on the growth and quality of melons. The temperature and humidity are maintained within the appropriate range, while the utilization of resources like as water and energy becomes increasingly efficient. Furthermore, the implementation of this approach results in melons exhibiting enhanced development uniformity and superior fruit quality. The research findings indicate that the implementation of Fuzzy Mamdani-based control systems has the potential to enhance agricultural output and quality, while also promoting the adoption of more sustainable agricultural methods.
Downloads
References
Smith, J. (2020). Modern Agricultural Challenges. Journal of Agricultural Science, 45(2), 123-135.
Johnson, A., & Brown, B. (2019). Hydroponic Systems: A Comprehensive Review. Advances in Plant Science, 12(3), 234-248.
Garcia, C., et al. (2021). Benefits and Challenges of Hydroponic Farming. Sustainable Agriculture Research, 8(4), 567-580.
Lee, K. (2018). Environmental Factors Affecting Plant Growth in Controlled Environments. Horticultural Science, 30(1), 45-58.
Wang, Y., & Liu, Z. (2022). Smart Greenhouse Technologies: A Review. Journal of Agricultural Engineering, 55(3), 321-335.
Patel, H., et al. (2020). Sensor Technologies for Precision Agriculture. IEEE Sensors Journal, 20(10), 5107-5120.
Anderson, R. (2021). Efficiency and Productivity in Smart Greenhouses. Agricultural Systems, 190, 103093.
Thompson, E. (2019). Resource Management in Smart Agriculture. Renewable Agriculture and Food Systems, 34(4), 362-375.
UN FAO. (2023). The State of Food and Agriculture: Water Scarcity and Energy Challenges. Rome: Food and Agriculture Organization of the United Nations.
Zadeh, L. A. (1965). Fuzzy Sets. Information and Control, 8(3), 338-353.
Mamdani, E. H., & Assilian, S. (1975). An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. International Journal of Man-Machine Studies, 7(1), 1-13.
Ross, T. J. (2017). Fuzzy Logic with Engineering Applications. John Wiley & Sons.
Chen, G., & Pham, T. T. (2000). Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems. CRC Press.
Kumar, A., et al. (2023). Smart Greenhouse Control Using Fuzzy Logic: A Case Study on Melon Cultivation. Journal of Intelligent Systems in Agriculture, 15(2), 178-192.
Jones, H. G. (2013). Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology. Cambridge University Press.
H. Jaiswal, K. R. P, R. Singuluri, and S. A. Sampson, IoT and Machine Learning based approach for Fully Automated Greenhouse, in 2019 IEEE Bombay Section Signature Conference (IBSSC), Oct. 2019. doi: 10.1109/ibssc47189.2019.8973086.
M. A. A. Ahmed and S. M. Reddy, “Smart Hydroponic System and Monitoring of Plants Health through Machine Learning,” Int J Res Appl Sci Eng Technol, vol. 11, no. 6, pp. 4261–4263, Jun. 2023.
U. Arora, S. Shetty, R. Shah, and D. K. Sinha, “Automated Dosing System in Hydroponics with Machine Learning,” in 2021 International Conference on Communication information and Computing Technology (ICCICT), Jun. 2021.
Lopes, R. V., et al. (2021). IoT in Precision Agriculture: A Systematic Literature Review. IEEE Internet of Things Journal, 8(18), 14227-14247.
Castañeda-Miranda, A., et al. (2020). Fuzzy Greenhouse Climate Control System Based on a Field Programmable Gate Array. Biosystems Engineering, 194, 135-150.
M. Lavanaya and R. Parameswari, “Soil Nutrients Monitoring For Greenhouse Yield Enhancement Using Ph Value with Iot and Wireless Sensor Network,” in 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), Oct. 2018. doi: 10.1109/icgciot.2018.8753083.
Ödük, M. N., & Allahverdi, N. (2019). The Advantages of Fuzzy Control Over Traditional Control System in Greenhouse Automation. 2019 1st International Informatics and Software Engineering Conference (UBMYK), 1-4.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
