Engineering of Information Monitoring System Sensor Reading Data Based on Smart Wireless using NVDIA Jetson Nano and Arduino Mega on Agricultural Spraying Machines
DOI:
https://doi.org/10.23960/jtep-l.v12i4.921-936Abstract
The focus of the research is monitoring data from sensors on the agricultural sprayer. The monitoring system support by some sensors in camera, tank capacity, boom sprayer balance and battery capacity. The research method was carried out using the waterfall model, because according to the needs that require a sequential flow in the process. This model is divided into four parts, namely analysis (to identify problems and needs), design (plans to solve problems to be solved), implementation (implementation of plans that have been made), and testing. Engineering of Information Monitoring System Sensor Reading Data Based on Smart Wireless using NVDIA Jetson Nano and Arduino Uno on Agricultural Spraying Machines. The test results for the CNN model for the detection of the Jajar Legowo object were carried out to obtain 90% accuracy, 82.35% precision and 100% recall. Tests an accuracy value cappacity tank of 100%. Testing the balance sensor, if rotates clockwise on the Y axis the output voltage decreases, and vice versa. However, if the sensor at rest, the output voltage will same as the offset value. Besides that, testing the optimum PWM value fuzzy approach is carried out with aim that the droplets hit the target zone when sprayer is working. The result are Arduino IDE and Matlab produce same value, which is 42 for the optimum PWM value. Testing the battery capacity sensor get accuracy value of 100% by difference in the voltage increase of 0.5 volts is equivalent to increase of 10%. All information read by the sensors is displayed on the LCD using WMS-2000 (smart wireless).
Keyword: Fuzzy, Microcontroller, Monitoring, Sensor data, Smart wireless
References
Anastasiou, E., Balafoutis, A.T., & Fountas, S. (2023). Smart agricultural technology trends in remote sensing technologies in olive cultivation. Smart Agricultural Technology, 3, 100103. https://doi.org/10.1016/j.atech.2022.100103.
Assunção, E., Gaspar, P.D., Mesquita, R., Simões, M.P., Alibabaei, K., Veiros, A., & Proença, H. (2022). Real-time weed control application using a Jetson nano edge device and a spray mechanism. Remote Sensing, 14(17), 4217. https://doi.org/10.3390/rs14174217.
of a 6u cubesat with a 5-meter resolution for wildfire image classification using convolution neural network approach. Remote Sensing, 14(8), 1874. doi: https://dx.doi.org/10.3390/rs14081874.
Bafdal, N., Ardiansah, I., & Asmara, S. (2022). Application of Internet of Things (IoT) on microclimate monitoring system in the ALG Unpad greenhouse based on Raspberry Pi. Jurnal Teknik Pertanian Lampung, 11(3), 518–530. http://dx.doi.org/10.23960/jtep-l.v11i3.518-530.
Botero-Valencia, J.S., Mejia-Herrera, M., & Pearce, J.M. (2022). Low cost climate station for smart agriculture applications with photovoltaic energy and wireless communication. HardwareX, 11(2022), e00296. https://doi.org/10.1016/j.ohx.2022.e00296.
Bwambale, E., Abagale, F.K., & Anornu, G.K. (2022). Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture : A review. Agricultural Water Management, 260, 107324. https://doi.org/10.1016/j.agwat.2021.107324.
Cheng, Y. (2020). Research on intelligent control of an agricultural greenhouse based on fuzzy PID control. Journal of Environmental Engineering and Science, 15(3), 113–118. https://doi.org/10.1680/jenes.19.00054.
Doan, T.N. (2022). An efficient system for real-time mobile smart device-based insect detection. International Journal of Advanced Computer Science and Applications, 13(6), 30–36. https://dx.doi.org/10.14569/IJACSA.2022.0130605.
Fang, L., Wu, Y., Li, Y., Guo, H., Zhang, H., Wang, X., Xi, R., & Hou, J. (2021). Using channel and network layer pruning based on deep learning for real-time detection of ginger images. Agriculture, 11(12), pp. 1–18. https://doi.org/10.3390/agriculture11121190.
He, R., Luo, X., Zhang, Z., Zhang, W., Jiang, C., & Yuan, B. (2022). Identification method of rice seedlings rows based on Gaussian heatmap. Agriculture, 12(10), 1736. https://doi.org/10.3390/agriculture12101736.
Hidayat, R., Muhaimin, & Aidi, F. (2019). Rancang bangun prototype drone penyemprot pestisida untuk pertanian padi secara otomatis. Jurnal Tektro, 3(2), 86–94.
Indraswira, R., Poningsih, P., Suhada, S., Gunawan, I., & Nasution, Z.M. (2021). Penerapan Arduino Uno Atmega 328P dalam membangun alat penyemprot cairan pestisida otomatis. INTEK : Jurnal Informatika Dan Teknologi Informasi, 4(2),86–90.
Istiqomah, H., Ariyanti, D., & Supraptiningsih, L.K. (2022). Prototipe sistem pengendali penyiraman air dan penyemprotan. Jurnal Ilmiah Ilmu-Ilmu Teknik, 12(2), 89–96. https://doi.org/10.51747/energy.v12i2.1185
Kusuma, R.R., Alawiyah, N., & Anwar, M. (2021). Rancang bangun smart field system berbasis IoT sebagai alat pemantau dan pengontrol keadaan sawah dengan aplikasi smartphone melalui internet. Proceedings National Conference PKM Center, Sebelas Maret University: 377–382.
Paul, K., Chatterjee, S.S., Pai, P., Varshney, A., Juikar, S., Prasad, V., Bhadra, B., & Dasgupta, S. (2022). Viable smart sensors and their application in data driven agriculture. Computers and Electronics in Agriculture, 198, 107096. https://doi.org/10.1016/j.compag.2022.107096.
Putri, A.R., Suroso, & Nasron (2019). Perancangan alat penyiram tanaman otomatis pada miniatur greenhouse berbasis IOT. Seminar Nasional Inovasi dan Aplikasi Teknologi di Industri 2019: 155–159.
RadoÄaj, D., JuriÅ¡ić, M., & GaÅ¡parović, M. (2022). The role of remote sensing data and methods in a modern approach to fertilization in precision agriculture. Remote Sensing, 14(3), 778. https://doi.org/10.3390/rs14030778
Siskandar, R., Fadhil, M.A., & Kusumah, B.R. (2020). Internet of things : Automatic plant watering system using. Jurnal Teknik Pertanian Lampung, 9(4), 297–310. http://dx.doi.org/10.23960/jtep-l.v9i4.297-310.
Siskandar, R., & Kusumah, B.R. (2019). Design and construction of control devices for aquaponic monitoring management. Aquacultura Indonesiana, 20(2), 72–79. http://dx.doi.org/10.21534/ai.v20i2.151
Font, G., Jordi, P-P., Javier, M., & La Rivera, M.M. (2023). Simulation of an automated tow tractor operation for first person training. Journal Of Latex Class Files, 18(9), 1–12. https://dx.doi.org/10.36227/techrxiv.21936891.v1.
Wahyudi, D.A., Wibowo, S.A., & Primaswara, P.R. (2021). Rancang bangun sistem padi aquaponic berbasis IoT (Internet of Things). JATI (Jurnal Mahasiswa Teknik Informatika), 5(1), 108–114. https://doi.org/10.36040/jati.v5i1.3271.
Wijayanti, R.R., Nugroho, F.E., Faridi, F., Robby, M.N., & Abdurrasyid, A. (2023). Implementasi Internet of Things Pada Monitoring Kesuburan Tanaman Cabai. Jurnal Informatika, 7(1), 97–103. http://dx.doi.org/10.31000/jika.v7i1.7279
Yağ, İ., & Altan, A. (2022). Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments. Biology, 11(12), 1732. https://doi.org/10.3390/biology11121732.
Zhang, Y., Yu, J., Chen, Y., Yang, W., Zhang, W., & He, Y. (2022). Real-time strawberry detection using deep neural networks on embedded system (rtsd-net): An edge AI application. Computers and Electronics in Agriculture, 192, 106586. https://doi.org/10.1016/j.compag.2021.106586.
Downloads
Published
Issue
Section
License
- Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International Lice that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Jurnal Teknik Pertanian Lampung
JTEPL is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.