Design and Build an Automatic Spraying System for Shallot Plants using Soil Moisture and Air Temperature Sensors with the Fuzzy Method

Abdul Rachman Manga'; Dedy Atmajaya; Amaliah Faradibah

doi:10.56705/ijodas.v6i2.213

Authors

Abdul Rachman Manga' Universitas Muslim Indonesia
Dedy Atmajaya Universitas Muslim Indonesia
Amaliah Faradibah Universitas Muslim Indonesia

DOI:

https://doi.org/10.56705/ijodas.v6i2.213

Keywords:

Automatic Sprinkler System, Soil Moisture Sensor, Sensor Selenoide, ADC Signal

Abstract

Agriculture utilizes biological resources to produce food, industrial raw materials, energy sources, and manage the environment. This sector plays a strategic role in national economic development. This research aims to design an automatic spraying system for shallot plants based on soil moisture using soil moisture sensors. This system utilizes soil moisture sensors to detect the water content in the soil as well as soil moisture sensors to measure the air humidity around the plants. Data from both sensors are processed by the microcontroller to regulate the timing and duration of the spraying. The prototype of this system was built using soil moisture sensors, soil moisture sensors, microcontrollers, water pumps, solenoid valves, and other supporting components. Testing was conducted in the field with red onion plants as the test subjects. The results show that the system is capable of functioning effectively in watering plants based on soil moisture levels. The sensor works accurately in measuring water content, while the microcontroller successfully controls the spraying optimally. The implementation of this system has proven to increase water usage efficiency and support better growth of red onion plants. Thus, this automatic spraying system offers an environmentally friendly and efficient solution for irrigation based on soil moisture and soil moisture sensors.

Downloads

Download data is not yet available.

References

H. Oumarou and N. Rismayanti, “Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm,” Indones. J. Data …, 2023.

A. Vinothini, “Transfer learning based deep learning model for classifying tomato plant leaf diseases,” Eng. Res. Express, vol. 7, no. 2, 2025, doi: 10.1088/2631-8695/add6f5.

F. Solimani et al., “Optimizing tomato plant phenotyping detection: Boosting YOLOv8 architecture to tackle data complexity,” Comput. Electron. Agric., vol. 218, p. 108728, Mar. 2024, doi: 10.1016/j.compag.2024.108728.

Z. Zhu, F. K. S. Chan, G. Li, M. Xu, M. Feng, and Y. G. Zhu, “Implementing urban agriculture as nature-based solutions in China: Challenges and global lessons,” Soil Environ. Heal., vol. 2, no. 1, p. 100063, 2024, doi: 10.1016/j.seh.2024.100063.

K. Sudharson, “Efficient Soil Condition Monitoring with IoT Enabled Intelligent Farming Solution,” 2023 IEEE International Students’ Conference on Electrical, Electronics and Computer Science, SCEECS 2023. 2023, doi: 10.1109/SCEECS57921.2023.10063050.

N. Mahmoudi, “Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation,” Agric. Water Manag., vol. 261, 2022, doi: 10.1016/j.agwat.2021.107342.

…, H. Haviluddin, and P. Purnawansyah, “Decision Making Of Banana Varieties Based On Land in Samarinda Using Electre Method,” … Educ. …, 2017.

M. Crenganiș, “Fuzzy Logic-Based Driving Decision for an Omnidirectional Mobile Robot Using a Simulink Dynamic Model,” Appl. Sci., vol. 14, no. 7, 2024, doi: 10.3390/app14073058.

W. F. Mahmudy, A. P. Wibawa, N. R. Sari, and ..., “Genetic algorithmised neuro fuzzy system for forecasting the online journal visitors,” Int. J …, 2021.

S. Maurya and V. K. Jain, “Fuzzy based energy efficient sensor network protocol for precision agriculture,” Comput. Electron. Agric., vol. 130, pp. 20–37, 2016, doi: 10.1016/j.compag.2016.09.016.

E. F. A. Sihotang, “Fuzzy Logic based Decision Support Model for Determining the Subject of Online Course Materials,” J. Syst. Manag. Sci., vol. 12, no. 5, pp. 169–192, 2022, doi: 10.33168/JSMS.2022.0511.

N. T. Singh, “An Innovative URL-Based System Approach with ML Based Prevention,” Proceedings - International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024. pp. 1498–1503, 2024, doi: 10.1109/IC2PCT60090.2024.10486712.

Đ. Banđur, B. Jakšić, M. Banđur, and S. Jović, “An analysis of energy efficiency in Wireless Sensor Networks (WSNs) applied in smart agriculture,” Comput. Electron. Agric., vol. 156, no. November 2018, pp. 500–507, 2019, doi: 10.1016/j.compag.2018.12.016.

P. Indira, I. S. Arafat, R. Karthikeyan, S. Selvarajan, and P. K. Balachandran, “Fabrication and investigation of agricultural monitoring system with IoT & AI,” SN Appl. Sci., vol. 5, no. 12, 2023, doi: 10.1007/s42452-023-05526-1.

M. Dutta et al., “Internet of Things-Based Smart Precision Farming in Soilless Agriculture: Opportunities and Challenges for Global Food Security,” IEEE Access, vol. 13, no. February, pp. 34238–34268, 2025, doi: 10.1109/ACCESS.2025.3540317.

T. Karanisa, Y. Achour, A. Ouammi, and S. Sayadi, “Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar,” Environ. Syst. Decis., vol. 42, no. 4, pp. 521–546, 2022, doi: 10.1007/s10669-022-09862-2.

S. A. Pasaribu and M. T. Unggul, “Comparison Analysis of Load Balance Performance Per Connection Classifier ( Pcc ) And Equal Cost Multi-Path ( Ecmp ) Networks for Multiple Path Networks,” vol. 1, no. 2, pp. 11–20, 2022.