ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Managing Irrigation Needs Based On Smart Decisions Using Machine Learning


Affiliations
1 Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India
     

   Subscribe/Renew Journal


Optimized utilization of water for agriculture is a big challenge in today’s world. Internet of Things (IoT) based solutions along with machine learning techniques help in achieving effective utilization of waters in farming landspace. This paper presents sensor-based acquisition of soil moisture, temperature and humidity from the farm. Data are then stored in the server and clustered into two groups. Next machine learning based classification models like Naïve Bayes (NB), K-Nearest Neighbor (K-NN) and Support Vector Machines (SVM) are applied to decide irrigation need. The performance measures of the classification models show that K-NN classifier performs better than the other two classification models considered in this study

Keywords

Classification Algorithms, Decision Support Systems, Internet of Things, Machine learning Algorithms, Smart Irrigation
Subscription Login to verify subscription
User
Notifications
Font Size

  • A. Kaloxylos, R. Eigenmann, F. Teye and Z. Politopoulou, “Farm Management Systems and the Future Internet Era”, Computers and Electronics in Agriculture, Vol. 89, pp. 130144, 2012.

  • A. Goap, D. Sharma, A.K. Shukla and C.R. Krishna, “An IoT based Smart Irrigation Management System using Machine Learning and Open Source Technologies”, Computers and Electronics in Agriculture, Vol. 155, pp. 4149, 2018.

  • J. Majumdar, S. Naraseeyappa and S. Ankalaki, “Analysis of Agriculture Data using Data Mining Techniques: Application of Big Data”, Journal of Big Data, Vol. 4, No. 1, pp. 1-20, 2017.

  • J. Chrouta, A. Zaafouri and M. Jemli, “Modeling and Identification of Irrigation Station using Fuzzy C-Mean Clustering Algorithms based on Particle Swarm Optimization”, Proceedings of International Conference on Systems and Control, pp. 58-64, 2015.

  • H.M. Al-Ghobari, M.S. El-Marazky, A.Z. Dewidar and M.A. Mattar, “Prediction of Wind Drift and Evaporation Losses from Sprinkler Irrigation using Neural Network and Multiple Regression Techniques”, Agricultural Water Management, Vol. 195, pp. 211-221, 2018.

  • J. Gubbi, R. Buyya, S. Marusic and M. Palaniswami, “Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions”, Future Generation Computer Systems, Vol. 29, No. 7, pp. 1645-1660, 2013.

  • S. Fang, L. Da Xu, Y. Zhu and Z. Liu, “An Integrated System for Regional Environmental Monitoring and Management based on Internet of Things”, IEEE Transactions on Industrial Informatics, Vol. 10, No. 2, pp.

  • -1605, 2014.

  • P. Jayaraman, A. Yavari and A. Zaslavsky, “Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt”, Sensors, Vol. 16, No. 11, pp. 1884-1895, 2016.

  • J.M. Talavera, L.E. Tobon, J.A. Gomez and L.E. Garreta, “Review of IoT Applications in Agro-Industrial and Environmental Fields”, Computers and Electronics in Agriculture, Vol. 142, pp. 283-297, 2017.

  • R.K. Kodali, N. Rawat and L. Boppana, “WSN Sensors for Precision Agriculture”, Proceedings of IEEE Symposium on Region 10, pp. 651-656, 2014 [11] S.A. Shaughnessy and S.R. Evett, “Canopy Temperature based System Effectively Schedules and Controls Center Pivot Irrigation of Cotton”, Agricultural Water Management, Vol. 97, No. 9, pp. 1310-1316, 2010.

  • S.L. Davis and M.D. Dukes, “Landscape Irrigation with Evapotranspiration Controllers in a Humid Climate”, Transactions of the ASABE, Vol. 55, No. 2, pp. 571-580, 2012.

  • J. Gutierrez, J.F. Villa-Medina, A. Nieto-Garibay and M.A. Porta-Gandara, “Automated Irrigation System using a Wireless Sensor Network and GPRS Module”, IEEE Transactions on Instrumentation and Measurement, Vol. 63, No. 1, pp. 166-176, 2013.

  • J.G. Jaguey, J.F. Villa-Medina, A. Lopez-Guzman and M.A. Porta-Gandara, “Smartphone Irrigation Sensor”, IEEE Sensors Journal, Vol. 15, No. 9, pp. 5122-5127, 2015.

  • M. Roopaei, P. Rad and K.K.R. Choo, “Cloud of Things in Smart Agriculture: Intelligent Irrigation Monitoring by Thermal Imaging”, IEEE Cloud Computing, Vol. 4, No. 1, pp. 10-15, 2017.

  • F. Viani, M. Bertolli and A. Polo, “Low-Cost Wireless Monitoring and Decision Support for Water Saving in Agriculture”, IEEE Sensors Journal, Vol. 17, No. 13, pp. 4299-4309, 2017.

  • A. Goldstein, L. Fink, A. Meitin and S. Bohadana, “Applying Machine Learning on Sensor Data for Irrigation Recommendations: Revealing the Agronomist’s Tacit Knowledge”, Precision Agriculture, Vol. 19, No. 3, pp. 421444, 2017.

  • J. Cai, D. Xiao, L. Lv and Y. Ye, “An Early Warning Model for Vegetable Pests based on Multidimensional Data”, Computers and Electronics in Agriculture, Vol. 156, pp. 217-226, 2019.

  • S. Rajeswari and K. Suthendran, “C5. 0: Advanced Decision Tree (ADT) Classification Model for Agricultural Data Analysis on Cloud”, Computers and Electronics in Agriculture, Vol. 156, pp. 530-539, 2019.

  • R.G. Perea, E.C. Poyato, P. Montesinos and J.R. Diaz, “Prediction of Irrigation event Occurrence at Farm Level using Optimal Decision Trees”, Computers and Electronics in Agriculture, Vol. 157, pp. 173-180, 2019.

  • M. Culman, C.M. Farias, C. Bayona and J.D.C. Cruz, “Using Agrometeorological Data to Assist Irrigation Management in Oil Palm Crops: A Decision Support Method and Results from Crop Model Simulation”, Agricultural Water Management, Vol. 213, pp. 1047-1062, 2019.

  • S. Maroufpoor, J. Shiri and E. Maroufpoor, “Modeling the Sprinkler Water Distribution Uniformity by Data-Driven Methods based on Effective Variables”, Agricultural Water Management, Vol. 215, pp. 63-73,2019.

  • G. Moorthy, C. Yaashuwanth and K. Venkatesh, “A Wireless Remote Monitoring of Agriculture using Zigbee”, International Journal of Engineering and Innovative Technology, Vol. 2, No. 8, pp. 72-74, 2013.

  • J.A. Hartigan and M.A. Wong, “Algorithm AS 136: A KMeans Clustering Algorithm”, Journal of the Royal

  • Statistical Society. Series C (Applied Statistics), Vol. 28, No. 1, pp. 100-108, 1979.

  • J. Han, J. Pei and M. Kamber, “ Data Mining: Concepts and Techniques”, Elsevier, 2011.


Abstract Views: 245

PDF Views: 1




  • Managing Irrigation Needs Based On Smart Decisions Using Machine Learning

Abstract Views: 245  |  PDF Views: 1

Authors

R. Bhavani
Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India
Ajoe Thambi
Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India

Abstract


Optimized utilization of water for agriculture is a big challenge in today’s world. Internet of Things (IoT) based solutions along with machine learning techniques help in achieving effective utilization of waters in farming landspace. This paper presents sensor-based acquisition of soil moisture, temperature and humidity from the farm. Data are then stored in the server and clustered into two groups. Next machine learning based classification models like Naïve Bayes (NB), K-Nearest Neighbor (K-NN) and Support Vector Machines (SVM) are applied to decide irrigation need. The performance measures of the classification models show that K-NN classifier performs better than the other two classification models considered in this study

Keywords


Classification Algorithms, Decision Support Systems, Internet of Things, Machine learning Algorithms, Smart Irrigation

References