Fuzzy Logic based Smart Irrigation System using Internet of Things

Abstract Traditional agricultural systems require huge amount of power for field watering. This paper proposes a smart irrigation system that helps farmers water their agricultural fields using Global System for Mobile Communication (GSM). This system provides acknowledgement messages about the job’s statuses such as humidity level of soil, temperature of surrounding environment, and status of motor regarding main power supply or solar power. Fuzzy logic controller is used to compute input parameters (e.g. soil moisture, temperature and humidity) and to produce outputs of motor status. In addition, the system also switches off the motor to save the power when there is an availability of rain and also prevents the crop using panels from unconditional rain. The comparison is made between the proposed system, drip irrigation and manual flooding. The comparison results prove that water and power conservation are obtained through the proposed smart irrigation system.

[1]  Ngoc Thanh Nguyen,et al.  A fast and accurate approach for bankruptcy forecasting using squared logistics loss with GPU-based extreme gradient boosting , 2019, Inf. Sci..

[2]  Nilanjan Dey,et al.  A survey of the state-of-the-arts on neutrosophic sets in biomedical diagnoses , 2019, Int. J. Mach. Learn. Cybern..

[3]  Mohamed Abdel-Basset,et al.  A New Representation of Intuitionistic Fuzzy Systems and Their Applications in Critical Decision Making , 2020, IEEE Intelligent Systems.

[4]  Zhang Bing,et al.  Study on Corn Water Saving Irrigation Decision-making Model , 2015 .

[5]  Michael G Williams,et al.  A Risk Assessment on Raspberry PI using NIST Standards , 2018 .

[6]  Akshi Kumar,et al.  Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network , 2019, IEEE Access.

[7]  T. Ross Fuzzy Logic with Engineering Applications , 1994 .

[8]  Sung Wook Baik,et al.  A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis , 2019, Comput. Intell. Neurosci..

[9]  S. Arivazhagan,et al.  Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features , 2013 .

[10]  Florentin Smarandache,et al.  Dynamic interval valued neutrosophic set: Modeling decision making in dynamic environments , 2019, Comput. Ind..

[11]  Abraham Kandel,et al.  Representing complex intuitionistic fuzzy set by quaternion numbers and applications to decision making , 2020, Appl. Soft Comput..

[12]  Ajay K. Singh Environmental problems of salinization and poor drainage in irrigated areas: Management through the mathematical models , 2019, Journal of Cleaner Production.

[13]  Panos J. Antsaklis,et al.  Advances in control of agriculture and the environment , 2001 .

[14]  Asghar Ali,et al.  Impact of climate smart agriculture (CSA) through sustainable irrigation management on Resource use efficiency: A sustainable production alternative for cotton , 2019, Land Use Policy.

[15]  Mohammad Hossein Ahmadi,et al.  Technical, economic, and environmental modeling of solar water pump for irrigation of rice in Mazandaran province in Iran: A case study , 2019 .

[16]  Mohamed Abdel-Basset,et al.  A Novel Neutrosophic Data Analytic Hierarchy Process for Multi-Criteria Decision Making Method: A Case Study in Kuala Lumpur Stock Exchange , 2019, IEEE Access.

[17]  Le Hoang Son,et al.  Towards granular calculus of single-valued neutrosophic functions under granular computing , 2019, Multimedia Tools and Applications.

[18]  Munagala Manoj Venkata Sai,et al.  Iot Based Smart Agriculture , 2018 .

[19]  T. Ahonen,et al.  Greenhouse Monitoring with Wireless Sensor Network , 2008, 2008 IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications.

[20]  Wei Lin Real time monitoring of electrocardiogram through IEEE802.15.4 network , 2011, 2011 8th International Conference & Expo on Emerging Technologies for a Smarter World.

[21]  Sotiris E. Nikoletseas,et al.  Keeping data at the edge of smart irrigation networks: A case study in strawberry greenhouses , 2020, Comput. Networks.

[22]  E. M. Barnes,et al.  Development and assessment of a smartphone application for irrigation scheduling in cotton , 2016, Comput. Electron. Agric..

[23]  Oana Geman,et al.  An Intelligent IoT-Based Food Quality Monitoring Approach Using Low-Cost Sensors , 2019, Symmetry.

[24]  K. Lakshmisudha,et al.  Smart Precision based Agriculture using Sensors , 2016 .

[25]  Sudan Jha,et al.  Neutrosophic image segmentation with Dice Coefficients , 2019, Measurement.

[26]  M. K. Rowshon,et al.  Modeling climate-smart decision support system (CSDSS) for analyzing water demand of a large-scale rice irrigation scheme , 2019, Agricultural Water Management.

[27]  Miguel Ángel Porta-Gándara,et al.  Automated Irrigation System Using a Wireless Sensor Network and GPRS Module , 2014, IEEE Transactions on Instrumentation and Measurement.

[28]  Mohamed Abdel-Basset,et al.  Novel Incremental Algorithms for Attribute Reduction From Dynamic Decision Tables Using Hybrid Filter–Wrapper With Fuzzy Partition Distance , 2020, IEEE Transactions on Fuzzy Systems.

[29]  Søren Marcus Pedersen,et al.  Farm and operator characteristics affecting adoption of precision agriculture in Denmark and Germany , 2018 .

[30]  Mohamed Abdel-Basset,et al.  A novel group decision making model based on neutrosophic sets for heart disease diagnosis , 2019, Multimedia Tools and Applications.

[31]  Mohamed Abdel-Basset,et al.  Linguistic Approaches to Interval Complex Neutrosophic Sets in Decision Making , 2019, IEEE Access.

[32]  Mumtaz Ali,et al.  A novel approach for fuzzy clustering based on neutrosophic association matrix , 2019, Comput. Ind. Eng..

[33]  C. Rama Krishna,et al.  An IoT based smart irrigation management system using Machine learning and open source technologies , 2018, Computers and Electronics in Agriculture.

[34]  Manju Khari,et al.  Neutrosophic soft set decision making for stock trending analysis , 2018, Evol. Syst..

[35]  Tandra Pal,et al.  A genetic algorithm for total graph coloring , 2019, J. Intell. Fuzzy Syst..

[36]  Mark Brindal,et al.  Use of Variable Rate Application in Soil Fertility Management by Small Farmers: Status, Issues, and Prospects , 2015 .

[37]  Hamido Fujita,et al.  Neural-fuzzy with representative sets for prediction of student performance , 2018, Applied Intelligence.

[38]  Trang Nguyen,et al.  A hybrid framework for smile detection in class imbalance scenarios , 2019, Neural Computing and Applications.

[39]  Tran Manh Tuan,et al.  Fuzzy and neutrosophic modeling for link prediction in social networks , 2018, Evol. Syst..

[40]  David C. Rose,et al.  Decision support tools for agriculture: Towards effective design and delivery , 2016 .

[41]  G. Bosworth,et al.  Rural development in the digital age: A systematic literature review on unequal ICT availability, adoption, and use in rural areas , 2017 .

[42]  Sung Wook Baik,et al.  A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset , 2018, Symmetry.

[43]  B. Moaveni,et al.  Simultaneous Estimation of State and Packet-Loss Occurrences in Networked Control Systems , 2020, ISA transactions.

[44]  Yunseop Kim,et al.  Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network , 2008, IEEE Transactions on Instrumentation and Measurement.

[45]  Raghvendra Kumar,et al.  Probability-based cluster head selection and fuzzy multipath routing for prolonging lifetime of wireless sensor networks , 2019, Peer-to-Peer Netw. Appl..

[46]  Suporn Pongnumkul,et al.  Applications of Smartphone-Based Sensors in Agriculture: A Systematic Review of Research , 2015, J. Sensors.

[47]  Stefano Marsili-Libelli,et al.  A Fuzzy Decision Support System for irrigation and water conservation in agriculture , 2015, Environ. Model. Softw..

[48]  Daran R. Rudnick,et al.  Evaluation of variable rate irrigation using a remote-sensing-based model , 2018 .

[49]  Fan Zhang,et al.  An interval multiobjective approach considering irrigation canal system conditions for managing irrigation water , 2019, Journal of Cleaner Production.

[50]  Lia Puspitasari,et al.  Digital divides and mobile Internet in Indonesia: Impact of smartphones , 2016, Telematics Informatics.

[51]  Le Hoang Son,et al.  Prediction of Air Pollution Index in Kuala Lumpur using fuzzy time series and statistical models , 2019, Air Quality, Atmosphere & Health.

[52]  Mahesh Manik Kumbhar,et al.  Grape Leaf Diseases Detection & Analysisusing SGDM Matrix Method , 2014 .

[53]  Tahir Mahmood,et al.  Multi-Attribute Multi-Perception Decision-Making Based on Generalized T-Spherical Fuzzy Weighted Aggregation Operators on Neutrosophic Sets , 2019, Mathematics.

[54]  Arindam Dey,et al.  Fuzzy minimum spanning tree with interval type 2 fuzzy arc length: formulation and a new genetic algorithm , 2020, Soft Comput..

[55]  Konstantinos Kokkinos,et al.  Fuzzy Sets in Agriculture , 2016, Fuzzy Logic in Its 50th Year.

[56]  Jesús Martínez del Rincón,et al.  A decision support system for managing irrigation in agriculture , 2016, Comput. Electron. Agric..

[57]  I. Fernández García,et al.  Coupling irrigation scheduling with solar energy production in a smart irrigation management system , 2018 .

[58]  Le Hoang Son,et al.  Linear quadratic regulator problem governed by granular neutrosophic fractional differential equations. , 2020, ISA transactions.

[59]  Amit Verma,et al.  Mixed Pixel Decomposition Based on Extended Fuzzy Clustering for Single Spectral Value Remote Sensing Images , 2019, Journal of the Indian Society of Remote Sensing.

[60]  J. Farrington,et al.  The digital divide: Patterns, policy and scenarios for connecting the ‘final few’ in rural communities across Great Britain , 2017 .

[61]  Tuong Le,et al.  A Novel Framework for Trash Classification Using Deep Transfer Learning , 2019, IEEE Access.

[62]  Sudan Jha,et al.  Neutrosophic approach for enhancing quality of signals , 2019, Multimedia Tools and Applications.

[63]  Nguyen Trung Thang,et al.  Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction , 2019, Applied Sciences.