A Smart Irrigation and Monitoring System
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10.5120/ijca2017913688 |
Dishay Kissoon, Hinouccha Deerpaul and Avinash Mungur. A Smart Irrigation and Monitoring System. International Journal of Computer Applications 163(8):39-45, April 2017. BibTeX
@article{10.5120/ijca2017913688,
author = {Dishay Kissoon and Hinouccha Deerpaul and Avinash Mungur},
title = {A Smart Irrigation and Monitoring System},
journal = {International Journal of Computer Applications},
issue_date = {April 2017},
volume = {163},
number = {8},
month = {Apr},
year = {2017},
issn = {0975-8887},
pages = {39-45},
numpages = {7},
url = {http://www.ijcaonline.org/archives/volume163/number8/27418-2017913688},
doi = {10.5120/ijca2017913688},
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
Abstract
Internet of Things, commonly known as IoT is a promising area in technology that is growing day by day. It is a concept whereby devices connect with each other or to living things. Internet of Things has shown its great benefits in today’s life. Agriculture is one amongst the sectors which contributes a lot to the economy of Mauritius and to get quality products, proper irrigation has to be performed. Hence proper water management is a must because Mauritius is a tropical island that has gone through water crisis since the past few years. With the concept of Internet of Things and the power of the cloud, it is possible to use low cost devices to monitor and be informed about the status of an agricultural area in real time. Thus, this paper provides the design and implementation of a Smart Irrigation and Monitoring System which makes use of Microsoft Azure machine learning to process data received from sensors in the farm and weather forecasting data to better inform the farmers on the appropriate moment to start irrigation. The Smart Irrigation and Monitoring System is made up of sensors which collect data such as air humidity, air temperature, and most importantly soil moisture data. These data are used to monitor the air quality and water content of the soil. The raw data are transmitted to the cloud platform, Microsoft Azure cloud platform, and are processed through a machine learning operation which had to be trained beforehand. The farmer is then informed through either a web app or mobile app as to when to irrigate. The Smart Irrigation and Monitoring System proposed in this paper allows the farmer, through both the mobile app and web app to send command to start the irrigation process.
References
- Travel Guide, Mauritius Travel Guide, [Online] http://www.mauritiusholidaystips.com/wp-content/uploads/2011/04/Mauritius-Activity-guide.pdf
- Human Resource Development Council, 2012. A Study on Labour Shortage in the Agricultural Sector in Mauritius, October 2012.
- Mauritius Meteorological Services 2017, Climate of Mauritius, [Online] http://metservice.intnet.mu/climate-services/climate-of-mauritius.php
- P. Narayut, P. Sasimanee, C.-I. Anupong, P. Phond and A. Khajonpong, 2016. A Control System in an Intelligent Farming by using Arduino Technology. Student Project Conference (ICT-ISPC), 2016 Fifth ICT International, pp. 53-56, 2016.
- K. Benahmed, A. Douli, A. Bouzekri, M. Chabane and T. Benahmed, 2015. Smart Irrigation Using Internet of Things. Fourth International Conference on Future Generation Communication Technology (FGCT), 2015.
- A. A. N. and K. D, 2016. Experimental investigation of remote control via Android smart phone of arduino-based automated irrigation system using moisture sensor. 3rd International Conference on Electrical Energy Systems (ICEES), 2016.
- T. Baranwal, N. and P. K. Pateriya, 2016. Development of IoT based Smart Security and Monitoring Devices for Agriculture. 6th International Conference - Cloud System and Big Data Engineering (Confluence), 2016.
- G. M.K., J. J. and A. M. G.S, 2015. Providing Smart Agriculture Solutions to Farmers for better yielding using IoT. IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR), 2015.
- Openweathermap.com 2012-2016, [Online] http://openweathermap.org/api
- Elementzonline 2016. Programming ESP8266 WeMos – D1 R2 using Arduino IDE, Random Codes - Elementz Tech, 2016
- Adafruit, DHT22 temperature-humidity sensor + extras, [Online] https://www.adafruit.com/product/385
- Super Electronics, 2014. Moisture Sensor YL-100 | Sensor Kelembapan Tanah Murah, [Online] http://tokosuperelectronics.com/moisture-sensor-yl-100-sensor-kelembapan-tanah-murah/
- Microsoft, 2017. Machine learning algorithm cheat sheet, Microsoft Azure, [Online] https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet
- Dynamax, 2017. Soil Moisture Range Chart, Turf Irrigation [Online] ftp://ftp.dynamax.com/turf_irrigation/Soil%20Moisture%20Range%20Chart.pdf
- A. Salam and M. C. Vuran, 2016. Impacts of Soil Type and Moisture on the Capacity of Multi-Carrier Modulation in Internet of Underground Things. 25th International Conference on Computer Communication and Networks (ICCCN), 2016.
- GitHub 2017. Azure/azure-iot-sdk-csharp, [Online] https://github.com/Azure/azure-iot-sdk-csharp/tree/master/tools/DeviceExplorer
- V. Proag, 2006. Water Resources Management in Mauritius, European Water, E.W. Publications, 2006.
- L. Ronald Ng Cheong and Gunshiam Umrit, 2015. Changes in Soil Properties with Sugarcane Cropping in Mauritius, in Land-Use Change Impacts on Soil Processes: Tropical and Savannah Ecosystems, Editor: Francis Q. Brearley, 2015
Keywords
Internet of Things, a Smart Irrigation and Monitoring System, Microsoft Azure Cloud, Azure Machine Learning