The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
IoT and agriculture data analysis for smart farm
Abstract
Abstract In this paper, we propose developing a system optimally watering agricultural crops based on a wireless sensor network. This work aimed to design and develop a control system using node sensors in the crop field with data management via smartphone and a web application. The three components are hardware, web application, and mobile application. The first component was designed and implemented in control box hardware connected to collect data on the crops. Soil moisture sensors are used to monitor the field, connecting to the control box. The second component is a web-based application that was designed and implemented to manipulate the details of crop data and field information. This component applied data mining to analyze the data for predicting suitable temperature, humidity, and soil moisture for optimal future management of crops growth. The final component is mainly used to control crop watering through a mobile application in a smartphone. This allows either automatic or manual control by the user. The automatic control uses data from soil moisture sensors for watering. However, the user can opt for manual control of watering the crops in the functional control mode. The system can send notifications through LINE API for the LINE application. The system was implemented and tested in Makhamtia District, Suratthani Province, Thailand. The results showed the implementation to be useful in agriculture. The moisture content of the soil was maintained appropriately for vegetable growth, reducing costs and increasing agricultural productivity. Moreover, this work represents driving agriculture through digital innovation.
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Cites
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A review on the practice of big data analysis in agriculture
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Review of IoT applications in agro-industrial and environmental fields
Talavera, J. M., Tobón, L. E., Gómez, J. A., Culman, M. A., Aranda, J. M., Parra, D. T., Quiroz, L. A., Hoyos, A., & Garreta, L. E. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2017.09.015 · Cited by 529
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Kaewmard, N., & Saiyod, S. (2014) · IEEE Conference on Wireless Sensors (ICWiSe) · doi:10.1109/icwise.2014.7042670 · Cited by 138
Real-time environmental sensor data: An application to water quality using web services
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A Wireless Sensor Network for Greenhouse Climate Control
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Design of Monitoring System for Multilayer Soil Temperature and Moisture Based on WSN
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Cited by
García, L., Parra, L., Jimenez, J. M., Lloret, J., & Lorenz, P. (2020) · Sensors · doi:10.3390/s20041042 · Cited by 758
Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture
Mekonnen, Y., Namuduri, S., Burton, L., Sarwat, A., & Bhansali, S. (2020) · Journal of The Electrochemical Society · doi:10.1149/2.0222003jes · Cited by 282
Mana, A. A., Allouhi, A., Hamrani, A., Rehman, S., el Jamaoui, I., & Jayachandran, K. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100416 · Cited by 245
Improved Internet of Things (IoT) monitoring system for growth optimization of Brassica chinensis
Harun, A. N., Mohamed, N., Ahmad, R., Rahim, A. R. A., & Ani, N. N. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.05.045 · Cited by 67
Design and Evaluation of a Smart Ex Vitro Acclimatization System for Tissue Culture Plantlets
Mohammed, M., Munir, M., & Ghazzawy, H. S. (2022) · Agronomy · doi:10.3390/agronomy13010078 · Cited by 35
Metadata
- Authors
- Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A., & Nillaor, P.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 156
- Pages
- 467-474
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2904462474
- Cited by
- 767
- References
- 11
- FWCI
- 55.43
- Type
- journal article
- Language
- en
- Open access
- closed
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- 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. https://doi.org/10.1016/j.compag.2018.12.011
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
