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Fuzzy Based Irrigation Control System for Indian Subcontinent


Affiliations
1 Division of Avionics, Department of Aerospace Engineering, MIT, Anna University, Chennai 600 044, Tamil Nadu, India
 

Water resource usage should be optimized as there is always a scarcity. This paper aims to provide an efficient way of water using sense and weather data and implementing a fuzzy decision model. An automated intelligent watering system is proposed in this paper using the internet of things and fuzzy logic. The weather data, coupled with Temperature, Relative Humidity, and soil moisture sensor data, is used to decide whether to switch on/off the motor. In-house-created prototypes of ground-moving robots have soil moisture, digital humidity, and temperature sensors implanted in them. The soil moisture sensor is attached to the Rack and pinion mechanism. The soil moisture sensor is pushed into the soil when the pinion rotates. It minimizes the use of sensors by using a distributed sensing method. Based on data obtained from sensors and meteorological information, the system will use this information to decide whether to Switch on/off the sprinkler motor. A fuzzy logic-based system decision is implemented on the input sensor and weather data, and the model will decide to switch on/off the actuator. An accuracy of 97% is achieved. The Android app is used to visualize sensor data, based on which the farmer can manually control the motor.

Keywords

Fuzzy Systems, Internet of Things, Robot, Sensor Systems and Applications, Smart Watering.
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  • Koshy S S, SunnamV S, Rajgarhia P, Chinnusamy K, Ravulapalli D P & Chunduri S, Application of the internet of things (IoT) for smart farming: A case study on groundnut and castor pest and disease forewarning, CSI Trans, 6 (2018) 311–318.

  • Ahonen T, Virrankoski R & Elmusrati M, Greenhouse monitoring with wireless sensor network, Proc IEEE-AS:ME Int Conf Mechtro Embed Syst Appl (IEEE) 2008, 403–408.

  • Bartlett A, Andales A, Arabi M & Bauder T, A smartphone app to extend use of a cloud-based irrigation scheduling tool, Comput Electron Agric, 111 (2015) 127–130.

  • Huang X, Yi J, Chen S & Zhu X, A wireless sensor networkbased approach with decision support for monitoring lake water quality, Sensors 15(11) (2015) 29273–29296.

  • Safdar M M, Imran S B & Sehrish M C, An intelligent and secure smart watering system using fuzzy logic and blockchain, Compu & Electri Engg, 77 (2019) 109–119.

  • Krishnan R S, E. Julie G, Robinson Y H, Raja S, Kumar R, Thong P H & Son L H, Fuzzy logic based smart irrigation system using internet of things, J Cleaner Produc, 252 (2020) 1–11.

  • Rao R N & Sridhar B, Iotbased smart crop-field monitoring and automation irrigation system, 2nd Int Conf Invent Syst Control (IEEE) 2018, 478–483.

  • Paucar L G, Diaz A R, Viani F, Robol F, Polo A & Massa A, Decision support for smart irrigation by means of wireless distributed sensors, IEEE 15th Medit Micro Sym (IEEE) 2015, 1–4, doi:10.1109/MMS.2015.7375469.

  • De OcampoA L P & Dadios E P, Energy cost optimization in irrigation system of smart farm by using genetic algorithm, IEEE 9th Int Conf Humanoid, Nanotechnol, Info Technol Commun Cont Environ Manag (IEEE) 2017, 1–7, doi: 10.1109/HNICEM.2017.8269497.

  • Đoko B, Branimir J, Miloš B & Srđan J, An analysis of energy efficiency in Wireless Sensor Networks (WSNs) applied in smart agriculture, Comput Electron Agric, 156 (2019) 500–507.

  • Amarendra G, Deepak S, Shukla A K & Krishna C R, Aniotbased smart irrigation management system using Machine learning and open source technologies, Comput Electron Agric, 155 (2018) 41–49.

  • Mohammad H F Z, Neda M & Susan B, A fuzzy rule-based expert system for evaluating intellectual capital, Adv Fuzzy Syst, 2012 (2012) 1–11, https://doi.org/10.1155/2012/823052

  • Sindhwani N, Maurya V P, Patel A, Yadav R K, Krishna S & Anand R, Implementation of intelligent plantation system using virtual IoT, Internet Things Appl, (2022) 305–322.

  • Bakshi G, Shukla R, Yadav V, Dahiya A, Anand R, Sindhwani N & Singh H, Anoptimized approach for feature extraction in multi-relational statistical learning, J Sci Ind Res, 80 (2021) 537–542.

  • Sindhwani N, Anand R, Shukla R, Yadav M & Yadav V, Performance analysis of Deep Neural Networks using computer vision, EAI Endorsed Trans Ind NetwIntell Syst, 8(29) (2021) e3.


Abstract Views: 117

PDF Views: 79




  • Fuzzy Based Irrigation Control System for Indian Subcontinent

Abstract Views: 117  |  PDF Views: 79

Authors

Sudheer Kumar Nagothu
Division of Avionics, Department of Aerospace Engineering, MIT, Anna University, Chennai 600 044, Tamil Nadu, India
G Anitha
Division of Avionics, Department of Aerospace Engineering, MIT, Anna University, Chennai 600 044, Tamil Nadu, India

Abstract


Water resource usage should be optimized as there is always a scarcity. This paper aims to provide an efficient way of water using sense and weather data and implementing a fuzzy decision model. An automated intelligent watering system is proposed in this paper using the internet of things and fuzzy logic. The weather data, coupled with Temperature, Relative Humidity, and soil moisture sensor data, is used to decide whether to switch on/off the motor. In-house-created prototypes of ground-moving robots have soil moisture, digital humidity, and temperature sensors implanted in them. The soil moisture sensor is attached to the Rack and pinion mechanism. The soil moisture sensor is pushed into the soil when the pinion rotates. It minimizes the use of sensors by using a distributed sensing method. Based on data obtained from sensors and meteorological information, the system will use this information to decide whether to Switch on/off the sprinkler motor. A fuzzy logic-based system decision is implemented on the input sensor and weather data, and the model will decide to switch on/off the actuator. An accuracy of 97% is achieved. The Android app is used to visualize sensor data, based on which the farmer can manually control the motor.

Keywords


Fuzzy Systems, Internet of Things, Robot, Sensor Systems and Applications, Smart Watering.

References