Smart Drip Irrigation System Based on IoT Using Fuzzy Logic
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Abstract
The absence of a water drip rate control system in drip irrigation systems has impacted water use efficiency and normalization of soil moisture. Therefore, this research aims to develop an intelligent system using the fuzzy logic method to control the rate of water droplets in a drip irrigation system and maintain soil moisture in normal conditions. The DHT22 sensor is used to obtain temperature and humidity values, which are then used as input data and processed by the ESP32 microcontroller, which includes a fuzzy system. The Internet of Things (IoT) is also used to send data from the microcontroller to the Thingspek web server. The Blynk application is used to make it easier to monitor temperature, humidity, and water droplet rate values. The results of this research show that the temperature accuracy values produced using the MSE evaluation were 6.66667 and RMSE were 2.58199, while for temperature, the values for MSE were 0.128333 and RMSE were 0.358236. The average value of soil moisture produced in the planting medium is 44.46%; this value is within normal conditions for chili plants, where normal soil moisture conditions range between 40% - 60%
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S. K. Roy, “AgriSens: IoT-Based Dynamic Irrigation Scheduling System for Water Management of Irrigated Crops,” IEEE Internet Things J., vol. 8, no. 6, pp. 5023–5030, 2021, doi: 10.1109/JIOT.2020.3036126.
F. Adenugba and S. Misra, “Smart irrigation system for environmental sustainability in Africa : An Internet of Everything ( IoE ) approach,” Math. Biosci. Eng., vol. 16, no. June, pp. 5490–5503, 2019, doi: 10.3934/mbe.2019273.
K. Obaideen et al., “An overview of smart irrigation systems using IoT,” Energy Nexus, vol. 7, no. July, p. 100124, 2022, doi: 10.1016/j.nexus.2022.100124.
A. Reghukumar and V. Vijayakumar, “Smart Plant Watering System with Cloud Analysis and Plant Health Prediction.,” Procedia Comput. Sci., vol. 165, no. 2019, pp. 126–135, 2019, doi: 10.1016/j.procs.2020.01.088.
M. Ayaz, M. Ammad-Uddin, Z. Sharif, A. Mansour, and E.-H. M. Aggoune, “Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk,” IEEE Access, vol. 7, pp. 129551–129583, 2019, doi: 10.1109/ACCESS.2019.2932609.
S. Duanghirun, B. Kwankhao, and M. O-thongkham, “Heliyon Enhancing melon growth and sustainability through spun-bonded polypropylene fabric wrapping,” Heliyon, vol. 9, no. 10, p. e20726, 2023, doi: 10.1016/j.heliyon.2023.e20726.
A. Palaniappan, R. Muthiah, and M. Tiruchi Sundaram, “ZigBee enabled IoT based intelligent lane control system for autonomous agricultural electric vehicle application,” SoftwareX, vol. 23, p. 101512, 2023, doi: 10.1016/j.softx.2023.101512.
Z. Bin, “Variable Rate Liquid Fertilizer Applicator for Deep-fertilization in Precision Farming Based on ZigBee Technology,” IFAC Pap., vol. 52, no. 30, pp. 43–50, 2019, doi: 10.1016/j.ifacol.2019.12.487.
H. Zhang, L. He, F. Di Gioia, D. Choi, A. Elia, and P. Heinemann, “LoRaWAN based internet of things (IoT) system for precision irrigation in plasticulture fresh-market tomato,” Smart Agric. Technol., vol. 2, no. March, 2022, doi: 10.1016/j.atech.2022.100053.
M. K. Saggi and S. Jain, “A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches,” Arch. Comput. Methods Eng., vol. 29, no. 6, pp. 1–24, 2022, doi: 10.1007/s11831-022-09746-3.
T. A. Shaikh, W. A. Mir, T. Rasool, and S. Sofi, Machine Learning for Smart Agriculture and Precision Farming: Towards Making the Fields Talk, vol. 29, no. 7. Springer Netherlands, 2022. doi: 10.1007/s11831-022-09761-4.
N. Singh, K. Ajaykumar, L. K. Dhruw, and B. U. Choudhury, “Optimization of irrigation timing for sprinkler irrigation system using convolutional neural network-based mobile application for sustainable agriculture,” Smart Agric. Technol., vol. 5, no. June, p. 100305, 2023, doi: 10.1016/j.atech.2023.100305.
D. Bhavsar, B. Limbasia, Y. Mori, M. Imtiyazali Aglodiya, and M. Shah, “A comprehensive and systematic study in smart drip and sprinkler irrigation systems,” Smart Agric. Technol., vol. 5, no. April, p. 100303, 2023, doi: 10.1016/j.atech.2023.100303.
B. Babu and K. Singh, “Evaluation of IoT based smart drip irrigation and ETc based system for,” Smart Agric. Technol., vol. 5, no. April, p. 100248, 2023, doi: 10.1016/j.atech.2023.100248.
A. E. Abioye et al., “Model based predictive control strategy for water saving drip irrigation,” Smart Agric. Technol., vol. 4, no. October 2021, p. 100179, 2023, doi: 10.1016/j.atech.2023.100179.
A. Yudhana, U. A. Dahlan, and U. A. Dahlan, “Impact of Fuzzy Tsukamoto in Controlling Room Temperature and Humidity,” vol. 7, no. 2, 2023.
C. Hairu, M. Hanafi, T. Z. Hang, S. Mashohor, and W. F. F. Ilahi, “Fuzzy-based Nutrient System for Chili Cultivation in Urban Area,” Indones. J. Electr. Eng. Informatics, vol. 10, no. 2, pp. 366–374, 2022, doi: 10.52549/ijeei.v10i2.3485.
S. Ali, “Parametric Estimation and Optimization of Automatic Drip Irrigation Control System using Fuzzy Logic,” 2022 Int. Conf. Emerg. Trends Electr. Control. Telecommun. Eng. ETECTE 2022 - Proc., pp. 1–6, 2022, doi: 10.1109/ETECTE55893.2022.10007188.
S. Wahjuni, W. Wulandari, and M. Kholili, “Development of Fuzzy-Based Smart Drip Irrigation System for Chili Cultivation,” JUITA J. Inform., vol. 10, no. 1, p. 115, 2022, doi: 10.30595/juita.v10i1.12998.
M. F. S. Munandar, L. Nurpulaela, and I. A. Bangsa, “IMPLEMENTASI PENYIRAMAN OTOMATIS DENGAN SENSOR GY-302 DAN YL- 69 PADA ALAT PENYIRAM TANAMAN,” Electron. Control. Telecomunication, Comput. Inf. Power Syst., vol. 7, no. 1, pp. 1–4, 2022, doi: 10.30736/je-unisla.v7i1.750.
P. S. Macheso and D. Thotho, “ESP32 Based Electric Energy Consumption Meter,” Int. J. Comput. Commun. Informatics, vol. 4, no. 1, pp. 23–35, 2022, doi: 10.34256/ijcci2213.
I. Plauska and A. Liutkeviˇ, “Performance Evaluation of C / C ++ , MicroPython , Rust and TinyGo Programming Languages on ESP32 Microcontroller,” 2023.
S. Rumalutur and A. Mappa, “Temperature and Humidity Moisture Monitoring System With Arduino R3 and Dht 11,” Electro Luceat, vol. 5, no. 2, pp. 40–47, 2019, doi: 10.32531/jelekn.v5i2.154.
Y. S. Al-Nahhas, L. A. Hadidi, M. S. Islam, M. Skitmore, and Z. Abunada, “Modified Mamdani-fuzzy inference system for predicting the cost overrun of construction projects,” Appl. Soft Comput., vol. 151, no. December 2023, p. 111152, 2024, doi: 10.1016/j.asoc.2023.111152.
M. Alinuha, “kelembaban tanah idea -untuk pertanian,” Alat Ukur Indoensia. Accessed: Nov. 11, 2023. [Online]. Available: https://alat-ukur-indonesia.com/kelembaban-tanah-ideal-untuk-pertanian/
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