Developing a prototype centre using agricultural smart sensors to promote agrarian production with technology
Creators
- 1. Rajamangala University of Technology Suvarnabhumi
Description
This article presents the development of a model center using agricultural intelligent center technology. The goal of this research is 1. To develop a wireless sensor network. 2. To be a source of learning on the use of sensor technology in agriculture. For local and nearby farmers Using the Sufficiency Economy Learning Center, according to King's Science. The Rajamangala University of Technology Suvarnabhumi is a research area. With the problems faced in farming today. It found that the world's climate change whether it's drought. Rains leave ranges and toxic airborne particulate matter caused by farming to match current problem conditions. The researchers then designed a two-part system: 1. Node Moisture Sensor that measures soil moisture and commands the opening – It also controls on-off with a manual switch. Wind speed and wind direction sensors, light intensity sensors, temperature, and humidity sensors, and Particulate Matters Sensor 1.0, 2.5, 10 with environmental reports within the growing area via Wi-F signals to (Sever) Raspberry Pi record real-time data. Every 30 seconds According to research, node moisture sensors can measure soil moisture and record results, and the station measures the environment within the growing area via a Wi-F signal to (Sever) Raspberry Pi. Rainfall values measured by local rainfall sensors measuring up to 35.3 mm are within the threshold of heavy rain. The maximum wind speed measured is 8.5 km/h, the maximum temperature of 35.8 degrees Celsius, and the maximum humidity of 99.9 percent, the light intensity is up to 58,002 Lux, and the Final Particles, with pm 1.0 up to 40.1 microns, PM 2.5 up to 51.3 microns and PM 10 up to 63.5 microns. Apply agriculture to 50 interested farmers after receiving knowledge transfer of smart sensor technology. The expansion has resulted in 3 farmers and will continue to expand in the future. Promote the use of agricultural technology. Intensifying communities and supporting global climate change
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References
- Tiglao, N. M., Alipio, M., Balanay, J. V., Saldivar, E., Tiston, J. L. (2020). Agrinex: A low-cost wireless mesh-based smart irrigation system. Measurement, 161, 107874. doi: https://doi.org/10.1016/j.measurement.2020.107874
- Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A., Nillaor, P. (2019). IoT and agriculture data analysis for smart farm. Computers and Electronics in Agriculture, 156, 467–474. doi: https://doi.org/10.1016/j.compag.2018.12.011
- Thakur, D., Kumar, Y., Vijendra, S. (2020). Smart Irrigation and Intrusions Detection in Agricultural Fields Using I.o.T. Procedia Computer Science, 167, 154–162. doi: https://doi.org/10.1016/j.procs.2020.03.193
- Castañeda-Miranda, A., Castaño-Meneses, V. M. (2020). Internet of things for smart farming and frost intelligent control in greenhouses. Computers and Electronics in Agriculture, 176, 105614. doi: https://doi.org/10.1016/j.compag.2020.105614
- Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G. et al. (2022). Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things, 18, 100187. doi: https://doi.org/10.1016/j.iot.2020.100187
- Li, M., Abula, B. (2020). WITHDRAWN: Evaluation of Economic Utility of Smart Agriculture Based on 5G Network and Wireless Sensors. Microprocessors and Microsystems, 103485. doi: https://doi.org/10.1016/j.micpro.2020.103485
- Esmail Karar, M., Abdel-Aty, A.-H., Algarni, F., Fadzil Hassan, M., Abdou, M. A., Reyad, O. (2022). Smart IoT-based system for detecting RPW larvae in date palms using mixed depthwise convolutional networks. Alexandria Engineering Journal, 61 (7), 5309–5319. doi: https://doi.org/10.1016/j.aej.2021.10.050
- Paul, K., Chatterjee, S. S., Pai, P., Varshney, A., Juikar, S., Prasad, V. et al. (2022). Viable smart sensors and their application in data driven agriculture. Computers and Electronics in Agriculture, 198, 107096. doi: https://doi.org/10.1016/j.compag.2022.107096
- Said Mohamed, E., Belal, A. A., Kotb Abd-Elmabod, S., El-Shirbeny, M. A., Gad, A., Zahran, M. B. (2021). Smart farming for improving agricultural management. The Egyptian Journal of Remote Sensing and Space Science, 24 (3), 971–981. doi: https://doi.org/10.1016/j.ejrs.2021.08.007
- Moreira, R., Rodrigues Moreira, L. F., Munhoz, P. L. A., Lopes, E. A., Ruas, R. A. A. (2022). AgroLens: A low-cost and green-friendly Smart Farm Architecture to support real-time leaf disease diagnostics. Internet of Things, 19, 100570. doi: https://doi.org/10.1016/j.iot.2022.100570
- Pramanik, M., Khanna, M., Singh, M., Singh, D. K., Sudhishri, S., Bhatia, A., Ranjan, R. (2022). Automation of soil moisture sensor-based basin irrigation system. Smart Agricultural Technology, 2, 100032. doi: https://doi.org/10.1016/j.atech.2021.100032
- Hamami, L., Nassereddine, B. (2020). Application of wireless sensor networks in the field of irrigation: A review. Computers and Electronics in Agriculture, 179, 105782. doi: https://doi.org/10.1016/j.compag.2020.105782
- Yin, H., Zhai, X., Ning, Y., Li, Z., Ma, Z., Wang, X., Li, A. (2022). Online monitoring of PM2.5 and CO2 in residential buildings under different ventilation modes in Xi'an city. Building and Environment, 207, 108453. doi: https://doi.org/10.1016/j.buildenv.2021.108453
- Koval, L., Vaňuš, J., Bilík, P. (2016). Distance Measuring by Ultrasonic Sensor. IFAC-PapersOnLine, 49 (25), 153–158. doi: https://doi.org/10.1016/j.ifacol.2016.12.026
- Devaraju, J. T., Suhas, K. R., Mohana, H. K., Patil, V. A. (2015). Wireless Portable Microcontroller based Weather Monitoring Station. Measurement, 76, 189–200. doi: https://doi.org/10.1016/j.measurement.2015.08.027
- Azouzoute, A., Merrouni, A. A., Bennouna, E. G., Gennioui, A. (2019). Accuracy Measurement of Pyranometer vs Reference cell for PV resource assessment. Energy Procedia, 157, 1202–1209. doi: https://doi.org/10.1016/j.egypro.2018.11.286
- Haselow, L., Meissner, R., Rupp, H., Miegel, K. (2019). Evaluation of precipitation measurements methods under field conditions during a summer season: A comparison of the standard rain gauge with a weighable lysimeter and a piezoelectric precipitation sensor. Journal of Hydrology, 575, 537–543. doi: https://doi.org/10.1016/j.jhydrol.2019.05.065
- Zhao, Y., Fu, L., Wang, L. (2018). Design of PM2.5 Monitoring System Under the Human Micro-Environment. 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE). doi: https://doi.org/10.1109/isie.2018.8433771
- Wall, D., McCullagh, P., Cleland, I., Bond, R. (2021). Development of an Internet of Things solution to monitor and analyse indoor air quality. Internet of Things, 14, 100392. doi: https://doi.org/10.1016/j.iot.2021.100392
- Sadowski, S., Spachos, P. (2020). Wireless technologies for smart agricultural monitoring using internet of things devices with energy harvesting capabilities. Computers and Electronics in Agriculture, 172, 105338. doi: https://doi.org/10.1016/j.compag.2020.105338
- Ahmedi, F., Ahmedi, L. (2022). Dataset on water quality monitoring from a wireless sensor network in a river in Kosovo. Data in Brief, 44, 108486. doi: https://doi.org/10.1016/j.dib.2022.108486
- Villa-Henriksen, A., Edwards, G. T. C., Pesonen, L. A., Green, O., Sørensen, C. A. G. (2020). Internet of Things in arable farming: Implementation, applications, challenges and potential. Biosystems Engineering, 191, 60–84. doi: https://doi.org/10.1016/j.biosystemseng.2019.12.013