An IoT based smart irrigation management system using Machine learning and open source technologies
By Amarendra Goap, Deepak Sharma, A. K. Shukla, C. Krishna. Computers and Electronics in Agriculture, 2018
549 citations recorded by the source corpus.
Abstract The scarcity of clean water resources around the globe has generated a need for their optimum utilization. Internet of Things (IoT) solutions, based on the application specific sensors’ data acquisition and intelligent processing, are bridging the gaps between the cyber and physical worlds. IoT based smart irrigation management systems can help in achieving optimum water-resource utilization in the precision farming landscape. This paper presents an open-source technology based smart system to predict the irrigation requirements of a field using the sensing of ground parameter like soil moisture, soil temperature, and environmental conditions along with the weather forecast data from the Internet. The sensing nodes, involved in the ground and environmental sensing, consider soil moisture, soil temperature, air temperature, Ultraviolet (UV) light radiation, and relative humidity of the crop field. The intelligence of the proposed system is based on a smart algorithm, which considers sensed data along with the weather forecast parameters like precipitation, air temperature, humidity, and UV for the near future. The complete system has been developed and deployed on a pilot scale, where the sensor node data is wirelessly collected over the cloud using web-services and a web-based information visualization and decision support system provides the real-time information insights based on the analysis of sensors data and weather forecast data. The system has a provision for a closed-loop control of the water supply to realize a fully autonomous irrigation scheme. The paper describes the system and discusses in detail the information processing results of three weeks data based on the proposed algorithm. The system is fully functional and the prediction results are very encouraging.
Subfields: Information Systems, Plant Science, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/dc367cca9a8c687edb8aa09b7ac3fafc802d0d38
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In Information Systems
- Angappa Gunasekaran
- Lei Shu
- Vikas Kumar
- Rohit Sharma
- Mohamed Amine Ferrag
- Leandros A. Maglaras
- Sachin S. Kamble
In Plant Science
Citations in the corpus
- Role of IoT Technology in Agriculture: A Systematic Literature Review (2020, unknown): “[73] Journal 2018 Proposed System Monitoring A smart irrigation system is developed by using open source technologies and machine learning.”
- A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming (2019, methodology): “In [70] an intelligent irrigation management system has been presented by using Maching learning and open source technologies which sense different soil and weather parametrs.”
- A systematic literature review on machine learning applications for sustainable agriculture supply chain performance (2020, methodology): “The analysis of data collected from sensors (Kale and Sonavane, 2018; Goap et al. 2018; Liakos et al. 2018) and drones (Fernández et al. 2018; Rehman et al. 2019; Rico-Fernández et al. 2019) deliver insights that enhances farm productivity and farmers profitability.”
- IoT in Smart Cities: A Survey of Technologies, Practices and Challenges (2021, methodology): “This problem has been devised both as a classification as well as a regression problem as in [88], who develop a closed loop water irrigation system using support vector regression and K-Means clustering.”
- Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies (2021, background): “[169] designed, implemented, and validated an intelligent decentralized irrigation solution for strawberry greenhouses, which was tested in Greece.”
- IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture (2020, unknown): “, Riverton, UT, USA), is utilized in [37,40,89,96,119,120].”