DEVELOPMENT OF A SMART IOT-BASED MONITORING SYSTEM FOR FERTIGATION AND SEED WEIGHT DETECTION IN SACHA INCHI
DOI:
https://doi.org/10.33480/jitk.v11i2.6968Keywords:
Fertigation, Internet of Things (IoT), Monitoring, Sacha Inchi, SensorsAbstract
This research focuses on designing a fertilization monitoring system based on the Internet of Things (IoT) and detecting the weight of Sacha Inchi plant seeds. The two tools are integrated with IoT platforms, enabling remote monitoring and control via the Simosachi app. Test results indicate that the system provides accurate data on soil and plant conditions, allowing farmers to make informed decisions on fertilization and irrigation. The seed weight detection tool also functions well, with a minor error margin still within acceptable limits. With improved monitoring and control of the fertilization process, as well as accurate monitoring of crop yields, the system is expected to help farmers achieve more optimal harvests. The seed weight detection tool achieved an accuracy of 97.94%, surpassing similar prior systems in terms of real-time data integration and multi-parameter monitoring. Future research may focus on enhancing the accuracy of the seed weight detection tool and developing advanced fertigation control algorithms
Downloads
References
A. S. Ningrum and E. Halimah, “Narrative Review: Kandungan Kimia Dan Aktivitas Farmakologi Tanaman Sacha Inchi (Plukenetia Volubilis L.),” Farmaka, vol. 20, no. 3, pp. 112–122, 2022.
S. Kittibunchakul, C. Hudthagosol, P. Sanporkha, S. Sapwarobol, P. Temviriyanukul, and U. Suttisansanee, “Evaluation of Sacha Inchi (Plukenetia volubilis L.) By-Products as Valuable and Sustainable Sources of Health Benefits,” Horticulturae, vol. 8, no. 4, pp. 1–12, 2022, doi: 10.3390/horticulturae8040344.
P. Istiandari and A. Faizal, “Integrating In Vitro Cultivation and Sustainable Field Practices of Sacha Inchi (Plukenetia volubilis L.) for Enhanced Oil Yield and Quality: A Review,” Feb. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/horticulturae11020194.
S. Supriyanto, Z. Imran, R. Ardiansyah, B. Auliyai, A. Pratama, and F. Kadha, “The Effect of Cultivation Conditions on Sacha Inchi (Plukenetia volubilis L.) Seed Production and Oil Quality (Omega 3, 6, 9),” 2022, doi: 10.3390/agronomy.
S. Safiera Wahono, S. Ayu Andayani, S. Umyati, and M. Dendi Purwanto, “Production Optimization of Sacha Inchi Oil Products in Achieving Maximum Profit at IKM Quilla Herbal Indonesia Sejahtera,” Mimbar Agribisnis : Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis, 2024.
S. A. Andayani et al., “MENGGALI POTENSI EKONOMI PENGEMBANGAN TANAMAN SACHA INCHI,” Abdimas Galuh, vol. 5, no. 2, p. 1655, Sep. 2023, doi: 10.25157/ag.v5i2.11930.
Á. M. R. del-Castillo, G. Gonzalez-Aspajo, M. de Fátima Sánchez-Márquez, and N. Kodahl, “Ethnobotanical Knowledge in the Peruvian Amazon of the Neglected and Underutilized Crop Sacha Inchi (Plukenetia volubilis L.),” Econ Bot, vol. 73, no. 2, pp. 281–287, 2019, doi: 10.1007/s12231-019-09459-y.
S. A. Andayani et al., “MENGGALI POTENSI EKONOMI PENGEMBANGAN TANAMAN SACHA INCHI,” Abdimas Galuh, vol. 5, no. 2, p. 1655, Sep. 2023, doi: 10.25157/ag.v5i2.11930.
I. Setiawan, J. Junaidi, F. Fadjryani, and F. R. Amaliah, “Internet of Things (IoT) for Soil Moisture Detection Using Time Series Model,” Jurnal Online Informatika, vol. 7, no. 2, pp. 236–243, 2022, doi: 10.15575/join.v7i2.951.
S. S. U. Sutjipto, S. Cahyadi, A. Sukamto, and D. Dolok, “Permodelan Efisiensi Smart Home Menggunakan Mobile Programming,” Jurnal Informatika Kesatuan, vol. 1, no. 1, pp. 91–100, Aug. 2021, doi: 10.37641/jikes.v1i1.776.
A. Imran, M. Yantahin, A. M. Mappalotteng, and M. Arham, “Development of Monitoring Tower Using Gyroscope Sensor Based on Esp32 Microcontroller,” Journal of Applied Engineering and Technological Science, vol. 4, no. 1, pp. 405–414, 2022, doi: 10.37385/jaets.v4i1.1327.
D. Hercog, T. Lerher, M. Truntič, and O. Težak, “Design and Implementation of ESP32-Based IoT Devices,” Sensors, vol. 23, no. 15, Aug. 2023, doi: 10.3390/s23156739.
F. Idris, A. A. Latiff, M. A. Buntat, Y. Lecthmanan, and Z. Berahim, “IoT-based fertigation system for agriculture,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 3, pp. 1574–1581, Jun. 2024, doi: 10.11591/eei.v13i3.6829.
M. F. M.F., M. K. Nordin, A. I. Mohd Yassin, and N. Md Tahir, “Automated Fertilizer Mixer System for Fertigation Farming,” Journal of Electrical & Electronic Systems Research, vol. 18, no. APR2021, pp. 18–23, Apr. 2021, doi: 10.24191/jeesr.v18i1.003.
P. P. Jayaraman, A. Yavari, D. Georgakopoulos, A. Morshed, and A. Zaslavsky, “Internet of things platform for smart farming: Experiences and lessons learnt,” Sensors (Switzerland), vol. 16, no. 11, Nov. 2016, doi: 10.3390/s16111884.
J. Morales-García, F. Terroso-Sáenz, and J. M. Cecilia, “A multi-model deep learning approach to address prediction imbalances in smart greenhouses,” Comput Electron Agric, vol. 216, p. 108537, Jan. 2024, doi: 10.1016/J.COMPAG.2023.108537.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Tri Ferga Prasetyo, Muhamad Dendi Purwanto, Harun Sujadi, Sri Andayani

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.






-a.jpg)
-b.jpg)











