IoT-Based Irrigation System Using Machine Learning for Precision Shallot Farming
Abstract
Despite the massive production of shallots in Enrekang Regency, South Sulawesi Province, Indonesia, the cultivation method is still very conventional. Shallot cultivation is very challenging because it requires precision irrigation and pest prevention. In this research, we proposed a smart irrigation system to help farmers manage irrigation with more efficient water usage without hampering their pest prevention. The results of the system were three options: 1) no water needed, 2) water is required and is efficient for irrigation, and 3) water is required but it is not efficient for irrigation. We used Wireless Sensor Networks and IoT to collect yield parameters, designed a firebase database, and developed a mobile application and a web service embedded with a machine learning application. All applications interacted using the representational state transfer application programming interface. The proposed system architecture successfully gathered cropland data and distributed them to all applications within the system. Furthermore, we analyzed four supervised learning algorithms (decision trees, random forest, gradient boosting, and K-Nearest neighbor), and the random forest was deployed in the web service because it outperformed other algorithms with a precision of 94% and an AUC score of 0.90.
Downloads
References
Badan Pusat Statistik Provinsi Sulawesi Selatan, “Statistik Tanaman Hortikultura Provinsi Sulawesi Selatan 2022,” 2022.
N. Sumarni and A. Hidayat, Budidaya Bawang Merah. Balai Penelitian Tanaman Sayuran, 2005.
K. S. Pratyush Reddy, Y. M. Roopa, K. Rajeev L. N., and N. S. Nandan, “IoT based Smart Agriculture using Machine Learning,” in Proceedings of the 2nd International Conference on Inventive Research in Computing Applications (ICIRCA 2020), 2020, pp. 130–134. https://doi.org/10.1109/ICIRCA48905.2020.9183373.
Akshay S and T. K. Ramesh, “Efficient Machine Learning Algorithm For Smart Irrigation,” in Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing (ICCSP) : 28th - 30th July 2020, Melmaruvathur, India., 2020, pp. 867–870. https://doi.org/10.1109/ICCSP48568.2020.9182215.
V. W. Samawi, “Smcsis: An iot based secure multi-crop irrigation system for smart farming,” International Journal of Innovative Computing, Information and Control, vol. 17, no. 4, pp. 1225–1241, 2021. https://doi.org/10.24507/ijicic.17.04.1225.
C. J. H. Pornillos et al., “Smart Irrigation Control System Using Wireless Sensor Network Via Internet-of-Things,” in 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), IEEE, Dec. 2020, pp. 1–6. https://doi.org/10.1109/HNICEM51456.2020.9399995
D. Thakur, Y. Kumar, and S. Vijendra, “Smart Irrigation and Intrusions Detection in Agricultural Fields Using I.o.T.,” Procedia Comput Sci, vol. 167, pp. 154–162, 2020. https://doi.org/10.1016/j.procs.2020.03.193
C. Jamroen, P. Komkum, C. Fongkerd, and W. Krongpha, “An Intelligent Irrigation Scheduling System Using Low-Cost Wireless Sensor Network Toward Sustainable and Precision Agriculture,” IEEE Access, vol. 8, pp. 172756–172769, 2020. https://doi.org/10.1109/ACCESS.2020.3025590
M. Agus et al., “Perancangan Sistem Pemantauan Kelembaban Tanah,Udara dan Suhu pada Tanaman Bawang Merah Menggunakan IoT,” in Prosiding Seminar Nasional Sistem Informasi dan Teknologi (SISFOTEK) ke 4, 2023, pp. 102–108.
K. Obaideen et al., “An overview of smart irrigation systems using IoT,” Energy Nexus, vol. 7, p. 100124, Sep. 2022. https://doi.org/10.1016/j.nexus.2022.100124.
F. Pedregosa et al., “Scikit-learn: Machine Learning in Python” 2011. [Online]. Available: http://scikit-learn.sourceforge.net.
C. Patil, S. Aghav, S. Sangale, S. Patil, and J. Aher, “Smart Irrigation Using Decision Tree,” in Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. Advances in Intelligent Systems and Computing, pp. 737–744. October 2021. https://doi.org/10.1007/978-981-15-7234-0_70.
L. Breiman, “Random Forests,” 2001.
R. Katarya, A. Raturi, A. Mehndiratta, and A. Thapper, “Impact of Machine Learning Techniques in Precision Agriculture,” in 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), IEEE, pp. 1–6, Feb. 2020. https://doi.org/10.1109/ICETCE48199.2020.9091741.
V. Geetha, A. Punitha, M. Abarna, M. Akshaya, S. Illakiya, and A. P. Janani, “An Effective Crop Prediction Using Random Forest Algorithm,” in 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), IEEE, pp. 1–5, Jul. 2020. https://doi.org/10.1109/ICSCAN49426.2020.9262311.
K. Pavithra and M. Jayalakshmi, “Analysis of Precision Agriculture based on Random Forest Algorithm by using Sensor Networks,” in 2020 International Conference on Inventive Computation Technologies (ICICT), IEEE, pp. 496–499, Feb. 2020. https://doi.org/10.1109/ICICT48043.2020.9112407.
J. H. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine,” The Annals of Statistics, vol. 29 No 5, pp. 1189–232, 2001, Accessed: Dec. 01, 2023. [Online]. Available: http://www.jstor.org/stable/2699986
Y. Tace, M. Tabaa, S. Elfilali, C. Leghris, H. Bensag, and E. Renault, “Smart irrigation system based on IoT and machine learning,” Energy Reports, vol. 8, pp. 1025–1036, Nov. 2022. https://doi.org/10.1016/j.egyr.2022.07.088
A. K. Pandey and Y. Patel, “IoT and ML based Irrigation System using KNN Algorithm,” in 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), IEEE, pp. 779–784, Dec. 2022. https://doi.org/10.1109/IC3I56241.2022.10072613
S. R et al., “A novel autonomous irrigation system for smart agriculture using AI and 6G enabled IoT network,” Microprocess Microsyst, vol. 101, p. 104905, Sep. 2023. https://doi.org/10.1016/j.micpro.2023.104905
R. Veerachamy, R. Ramar, S. Balaji, and L. Sharmila, “Autonomous Application Controls on Smart Irrigation,” Computers and Electrical Engineering, vol. 100, p. 107855, May 2022. https://doi.org/10.1016/j.compeleceng.2022.107855
R. S. Krishnan et al., “Fuzzy Logic based Smart Irrigation System using Internet of Things,” J Clean Prod, vol. 252, p. 119902, Apr. 2020. https://doi.org/10.1016/j.jclepro.2019.119902
Copyright (c) 2024 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
