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Year 2022, Volume: 9 Issue: 4, 378 - 391, 31.12.2022

Aamo Iorliam Sylvester Bum Iember S. Aondoakaa Iveren Blessing Iorlıam Yahaya Shehu

https://doi.org/10.54287/gujsa.1141575
Cited By: 3

Abstract

Keywords

IoT Agriculture IoT Agriculture Smart Irrigation Machine Learning Techniques

References

  • Adam, M. S. A., Osman, A. A., Omer, E. A., & Abdallah, A. M. B. (2020). Automatic Irrigation Implementation. PhD Thesis, Supervised by Ust. Jafer Babiker, Sudan University of Science & Technology.
  • Bhowmik, A., Ramasubramanian, V., & Kumar, A. (2011). Logistic regression for classification in agricultural ergonomics. Advances in Applied Science Research, 3(2):163-170.
  • Çetin, M., & Beyhan, S. (2022). Smart Irrigation Systems Using Machine Learning and Control Theory. In: R. Bhatnagar, N. K. Tripathi, N. Bhatnagar, & C. K. Panda (Eds.), The Digital Agricultural Revolution: Innovations and Challenges in Agriculture through Technology Disruptions (pp. 57-85). Scrivener Publishing LLC. doi:10.1002/9781119823469.ch3
  • Cheng, W., Ma, T., Wang, X., & Wang, G. (2022). Anomaly Detection for Internet of Things Time Series Data Using Generative Adversarial Networks with Attention Mechanism in Smart Agriculture. Frontiers in Plant Science, 13. doi:10.3389/fpls.2022.890563
  • Dhasaradhan, K., Jaichandran, R., Shunmuganathan, K. L., Usha Kiruthika, S., & Rajaprakash, S. (2021). Hybrid machine learning model using decision tree and support vector machine for diabetes identification. In: V. Bhateja, S. C. Satapathy, C. M. Travieso-González, V. N. M. Aradhya (Eds.), Data Engineering and Intelligent Computing (Proceedings of ICICC 2020) (pp. 293-305). Springer. doi:10.1007/978-981-16-0171-2_28
  • Dholu, M., & Ghodinde, K. A. (2018, May). Internet of things (IoT) for precision agriculture application. In: Proceedings of the 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 339-342). IEEE. doi:10.1109/ICOEI.2018.8553720
  • Fan, S. (2018, May 7). Understanding the mathematics behind Support Vector Machines. (Accessed: 30/06/2022) URL (https://shuzhanfan.github.io/2018/05/understanding-mathematics-behind-support-vector-machines/)
  • Fernández-Ahumada, L. M., Ramírez-Faz, J., Torres-Romero, M., & López-Luque, R. (2019). Proposal for the design of monitoring and operating irrigation networks based on IoT, cloud computing and free hardware technologies. Sensors, 19(10), 2318. doi:10.3390/s19102318
  • Goap, A., Sharma, D., Shukla, A. K., & Krishna, C. R. (2018). An IoT based smart irrigation management system using Machine learning and open source technologies. Computers and Electronics in Agriculture, 155, 41-49. doi:10.1016/j.compag.2018.09.040
  • Gondchawar, N., & Kawitkar, R. S. (2016). IoT based smart agriculture. International Journal of Advanced Research in Computer and Communication Engineering, 5(6), 838-842.
  • Iorliam, A., Adeyelu, A., Otor, S., Okpe, I., & Iorliam, I. B. (2020). A Novel Classification of IoT-Enabled Soil Nutrients Data Using Artificial Neural Networks. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 8(4), 103-109. doi:10.17148/IJIREEICE.2020.8418
  • Iorliam, A., Iorliam, I. B., & Blum, S. (2021). Internet of Things for Smart Agriculture in Nigeria and Africa: A Review. International Journal of Latest Technology in Engineering, Management & Applied Science, 10(2), 7-13.
  • Jagtap, S. T., Phasinam, K., Kassanuk, T., Jha, S. S., Ghosh, T., & Thakar, C. M. (2022). Towards application of various machine learning techniques in agriculture. Materials Today: Proceedings, 51(1), 793-797. doi:10.1016/j.matpr.2021.06.236
  • Janani, M., & Jebakumar, R. (2019). A study on smart irrigation using machine learning. Cell & Cellular Life Sciences Journal, 4(1), 1-8. doi:10.23880/cclsj-16000141
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386
  • Kumar, M., Sahni, R. K., Waghaye, A. M., Nayak, A. K., & Kumar, D. (2018). Automated Irrigation System for Rice: A Review. The Andhra Agric. Journal, 65 (spl), 324-329.
  • Mittal, R. (2020). Automatic Irrigation Data for Rice Crop. (Accessed: 30/06/2022) URL (http://autoirrigationdataforricecrop.herokuapp.com/)
  • Monaco, F., Sali, G., Ben Hassen, M., Facchi, A., Romani, M., & Valè, G. (2016). Water management options for rice cultivation in a temperate area: A multi-objective model to explore economic and water saving results. Water, 8(8), 336. doi:10.3390/w8080336
  • Nawandar, N. K., & Satpute, V. R. (2019). IoT based low cost and intelligent module for smart irrigation system. Computers and Electronics in Agriculture, 162, 979-990. doi:10.1016/j.compag.2019.05.027
  • Neforawati, I., Herman, N. S., & Mohd, O. (2019, April). Precision agriculture classification using convolutional neural networks for paddy growth level. Journal of Physics: Conference Series, 1193, 012026. doi:10.1088/1742-6596/1193/1/012026
  • Nindam, S., Sung, T. L., Manmai, T.-O., & Lee, H. J. (2019, June). Collection and Classification of Jasmine Rice Germination Using Convolutional Neural Networks. In: Proc. International Symposium on Information Technology Convergence (ISITC 2019) (pp. 105-108).
  • Ok, A. O., Akar, O., & Gungor, O. (2012). Evaluation of random forest method for agricultural crop classification. European Journal of Remote Sensing, 45(1), 421-432. doi:10.5721/EuJRS20124535
  • Pfitscher, L. L., Bernardon, D. P., Kopp, L. M., Ferreira, A. A. B., Heckler, M. V. T., Thome, B. A., Montani, P. D. B., & Fagundes, D. R. (2011, May). An automated irrigation system for rice cropping with remote supervision. In: J. A. Aguado, & A. Pires (Eds.), 2011 International Conference on Power Engineering, Energy and Electrical Drives. 2011 International Conference on Power Engineering, Energy and Electrical Drives (POWERENG). IEEE. doi:10.1109/PowerEng.2011.6036452
  • Pluchinotta, I., Pagano, A., Giordano, R., & Tsoukiàs, A. (2018). A system dynamics model for supporting decision-makers in irrigation water management. Journal of Environmental Management, 223, 815-824. doi:10.1016/j.jenvman.2018.06.083
  • Qi, Y. (2012). Random Forest for Bioinformatics. In: C. Zhang, & Y. Ma (Eds.), Ensemble Machine Learning Methods and Application (pp. 307-323). Springer, Boston, MA. doi:10.1007/978-1-4419-9326-7_11
  • Raghuvanshi, A., Singh, U. K., Sajja, G. S., Pallathadka, H., Asenso, E., Kamal, M., Singh, A., & Phasinam, K. (2022). Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming. In: M. F. Manzoor, A. Hussain, & R. M. Aadil (Eds.), Journal of Food Quality, 2022 (Special Issue), 3955514. doi:10.1155/2022/3955514
  • Romero, M., Luo, Y., Su, B., & Fuentes, S. (2018). Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management. Computers and Electronics in Agriculture, 147, 109-117. doi:10.1016/j.compag.2018.02.013
  • Roy, S. K., Misra, S., Raghuwanshi, N. S., & Das, S. K. (2020). AgriSens: IoT-based dynamic irrigation scheduling system for water management of irrigated crops. IEEE Internet of Things Journal, 8(6), 5023-5030. doi:10.1109/JIOT.2020.3036126
  • Sanjeevi, P., Prasanna, S., Siva Kumar, B., Gunasekaran, G., Alagiri, I., & Vijay Anand, R. (2020). Precision agriculture and farming using Internet of Things based on wireless sensor network. Transactions on Emerging Telecommunications Technologies, 31(12), e3978. doi:10.1002/ett.3978
  • Shekhar, Y., Dagur, E., Mishra, S., Tom, R. J., Veeramanikandan, M., & Sankaranarayanan, S. (2017). Intelligent IoT based automated irrigation system. International Journal of Applied Engineering Research, 12(18), 7306-7320.
  • Surendran, U., Sushanth, C. M., Mammen, G., & Joseph, E. J. (2015). Modelling the crop water requirement using FAO-CROPWAT and assessment of water resources for sustainable water resource management: A case study in Palakkad district of humid tropical Kerala, India. Aquatic Procedia, 4, 1211-1219. doi:10.1016/j.aqpro.2015.02.154
  • Torres-Sanchez, R., Navarro-Hellin, H., Guillamon-Frutos, A., San-Segundo, R., Ruiz-Abellón, M. C., & Domingo-Miguel, R. (2020). A decision support system for irrigation management: Analysis and implementation of different learning techniques. Water, 12(2), 548. doi:10.3390/w12020548
  • Tyagi, A., Gupta, N., Navani, J. P., Tiwari, R., & Gupta, A. (2017). Smart irrigation system. International Journal for Innovative Research in Science & Technology, 3(10).

Machine Learning Techniques for the Classification of IoT-Enabled Smart Irrigation Data for Agricultural Purposes

Year 2022, Volume: 9 Issue: 4, 378 - 391, 31.12.2022

Aamo Iorliam Sylvester Bum Iember S. Aondoakaa Iveren Blessing Iorlıam Yahaya Shehu

https://doi.org/10.54287/gujsa.1141575
Cited By: 3

Abstract

To support farming year-round, a variety of smart IoT irrigation devices have recently been developed. It is crucial to forecast the soil moisture of agricultural farms so as to produce high yields since the high yields depends on the efficiency of water supply on farmlands. In smart irrigation, anytime water is needed on the farms, the smart pumps switch on to pump the required water so as to prevent the crops from drying up. The smart pumps also shut down if the farms have the ideal level of soil moisture, preventing over-flooding of the fields. Data is generated when the smart pumps are ON or OFF at any given time. Therefore, it is crucial to classify the data produced by smart IoT-enabled irrigation devices when these devices are ON or OFF. In this paper, the soil moisture, temperature, humidity, and time are used as inputs into machine learning techniques for classification. These machine learning techniques include logistic regression, random forest, support vector machine, and convolutional neural network. According to experimental findings, the accuracy of the logistic regression was 71.76%, that of the random forest was 99.98%, that of the support vector machine was 90.21%, and that of the convolutional neural network was 98.23. Based on the high accuracy that the random forest attained, it has more potential to help in assessing smart irrigation conditions (wet or dry) in an optimized manner.

Keywords

IoT Agriculture Smart Irrigation Machine Learning Techniques

References

  • Adam, M. S. A., Osman, A. A., Omer, E. A., & Abdallah, A. M. B. (2020). Automatic Irrigation Implementation. PhD Thesis, Supervised by Ust. Jafer Babiker, Sudan University of Science & Technology.
  • Bhowmik, A., Ramasubramanian, V., & Kumar, A. (2011). Logistic regression for classification in agricultural ergonomics. Advances in Applied Science Research, 3(2):163-170.
  • Çetin, M., & Beyhan, S. (2022). Smart Irrigation Systems Using Machine Learning and Control Theory. In: R. Bhatnagar, N. K. Tripathi, N. Bhatnagar, & C. K. Panda (Eds.), The Digital Agricultural Revolution: Innovations and Challenges in Agriculture through Technology Disruptions (pp. 57-85). Scrivener Publishing LLC. doi:10.1002/9781119823469.ch3
  • Cheng, W., Ma, T., Wang, X., & Wang, G. (2022). Anomaly Detection for Internet of Things Time Series Data Using Generative Adversarial Networks with Attention Mechanism in Smart Agriculture. Frontiers in Plant Science, 13. doi:10.3389/fpls.2022.890563
  • Dhasaradhan, K., Jaichandran, R., Shunmuganathan, K. L., Usha Kiruthika, S., & Rajaprakash, S. (2021). Hybrid machine learning model using decision tree and support vector machine for diabetes identification. In: V. Bhateja, S. C. Satapathy, C. M. Travieso-González, V. N. M. Aradhya (Eds.), Data Engineering and Intelligent Computing (Proceedings of ICICC 2020) (pp. 293-305). Springer. doi:10.1007/978-981-16-0171-2_28
  • Dholu, M., & Ghodinde, K. A. (2018, May). Internet of things (IoT) for precision agriculture application. In: Proceedings of the 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 339-342). IEEE. doi:10.1109/ICOEI.2018.8553720
  • Fan, S. (2018, May 7). Understanding the mathematics behind Support Vector Machines. (Accessed: 30/06/2022) URL (https://shuzhanfan.github.io/2018/05/understanding-mathematics-behind-support-vector-machines/)
  • Fernández-Ahumada, L. M., Ramírez-Faz, J., Torres-Romero, M., & López-Luque, R. (2019). Proposal for the design of monitoring and operating irrigation networks based on IoT, cloud computing and free hardware technologies. Sensors, 19(10), 2318. doi:10.3390/s19102318
  • Goap, A., Sharma, D., Shukla, A. K., & Krishna, C. R. (2018). An IoT based smart irrigation management system using Machine learning and open source technologies. Computers and Electronics in Agriculture, 155, 41-49. doi:10.1016/j.compag.2018.09.040
  • Gondchawar, N., & Kawitkar, R. S. (2016). IoT based smart agriculture. International Journal of Advanced Research in Computer and Communication Engineering, 5(6), 838-842.
  • Iorliam, A., Adeyelu, A., Otor, S., Okpe, I., & Iorliam, I. B. (2020). A Novel Classification of IoT-Enabled Soil Nutrients Data Using Artificial Neural Networks. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 8(4), 103-109. doi:10.17148/IJIREEICE.2020.8418
  • Iorliam, A., Iorliam, I. B., & Blum, S. (2021). Internet of Things for Smart Agriculture in Nigeria and Africa: A Review. International Journal of Latest Technology in Engineering, Management & Applied Science, 10(2), 7-13.
  • Jagtap, S. T., Phasinam, K., Kassanuk, T., Jha, S. S., Ghosh, T., & Thakar, C. M. (2022). Towards application of various machine learning techniques in agriculture. Materials Today: Proceedings, 51(1), 793-797. doi:10.1016/j.matpr.2021.06.236
  • Janani, M., & Jebakumar, R. (2019). A study on smart irrigation using machine learning. Cell & Cellular Life Sciences Journal, 4(1), 1-8. doi:10.23880/cclsj-16000141
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386
  • Kumar, M., Sahni, R. K., Waghaye, A. M., Nayak, A. K., & Kumar, D. (2018). Automated Irrigation System for Rice: A Review. The Andhra Agric. Journal, 65 (spl), 324-329.
  • Mittal, R. (2020). Automatic Irrigation Data for Rice Crop. (Accessed: 30/06/2022) URL (http://autoirrigationdataforricecrop.herokuapp.com/)
  • Monaco, F., Sali, G., Ben Hassen, M., Facchi, A., Romani, M., & Valè, G. (2016). Water management options for rice cultivation in a temperate area: A multi-objective model to explore economic and water saving results. Water, 8(8), 336. doi:10.3390/w8080336
  • Nawandar, N. K., & Satpute, V. R. (2019). IoT based low cost and intelligent module for smart irrigation system. Computers and Electronics in Agriculture, 162, 979-990. doi:10.1016/j.compag.2019.05.027
  • Neforawati, I., Herman, N. S., & Mohd, O. (2019, April). Precision agriculture classification using convolutional neural networks for paddy growth level. Journal of Physics: Conference Series, 1193, 012026. doi:10.1088/1742-6596/1193/1/012026
  • Nindam, S., Sung, T. L., Manmai, T.-O., & Lee, H. J. (2019, June). Collection and Classification of Jasmine Rice Germination Using Convolutional Neural Networks. In: Proc. International Symposium on Information Technology Convergence (ISITC 2019) (pp. 105-108).
  • Ok, A. O., Akar, O., & Gungor, O. (2012). Evaluation of random forest method for agricultural crop classification. European Journal of Remote Sensing, 45(1), 421-432. doi:10.5721/EuJRS20124535
  • Pfitscher, L. L., Bernardon, D. P., Kopp, L. M., Ferreira, A. A. B., Heckler, M. V. T., Thome, B. A., Montani, P. D. B., & Fagundes, D. R. (2011, May). An automated irrigation system for rice cropping with remote supervision. In: J. A. Aguado, & A. Pires (Eds.), 2011 International Conference on Power Engineering, Energy and Electrical Drives. 2011 International Conference on Power Engineering, Energy and Electrical Drives (POWERENG). IEEE. doi:10.1109/PowerEng.2011.6036452
  • Pluchinotta, I., Pagano, A., Giordano, R., & Tsoukiàs, A. (2018). A system dynamics model for supporting decision-makers in irrigation water management. Journal of Environmental Management, 223, 815-824. doi:10.1016/j.jenvman.2018.06.083
  • Qi, Y. (2012). Random Forest for Bioinformatics. In: C. Zhang, & Y. Ma (Eds.), Ensemble Machine Learning Methods and Application (pp. 307-323). Springer, Boston, MA. doi:10.1007/978-1-4419-9326-7_11
  • Raghuvanshi, A., Singh, U. K., Sajja, G. S., Pallathadka, H., Asenso, E., Kamal, M., Singh, A., & Phasinam, K. (2022). Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming. In: M. F. Manzoor, A. Hussain, & R. M. Aadil (Eds.), Journal of Food Quality, 2022 (Special Issue), 3955514. doi:10.1155/2022/3955514
  • Romero, M., Luo, Y., Su, B., & Fuentes, S. (2018). Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management. Computers and Electronics in Agriculture, 147, 109-117. doi:10.1016/j.compag.2018.02.013
  • Roy, S. K., Misra, S., Raghuwanshi, N. S., & Das, S. K. (2020). AgriSens: IoT-based dynamic irrigation scheduling system for water management of irrigated crops. IEEE Internet of Things Journal, 8(6), 5023-5030. doi:10.1109/JIOT.2020.3036126
  • Sanjeevi, P., Prasanna, S., Siva Kumar, B., Gunasekaran, G., Alagiri, I., & Vijay Anand, R. (2020). Precision agriculture and farming using Internet of Things based on wireless sensor network. Transactions on Emerging Telecommunications Technologies, 31(12), e3978. doi:10.1002/ett.3978
  • Shekhar, Y., Dagur, E., Mishra, S., Tom, R. J., Veeramanikandan, M., & Sankaranarayanan, S. (2017). Intelligent IoT based automated irrigation system. International Journal of Applied Engineering Research, 12(18), 7306-7320.
  • Surendran, U., Sushanth, C. M., Mammen, G., & Joseph, E. J. (2015). Modelling the crop water requirement using FAO-CROPWAT and assessment of water resources for sustainable water resource management: A case study in Palakkad district of humid tropical Kerala, India. Aquatic Procedia, 4, 1211-1219. doi:10.1016/j.aqpro.2015.02.154
  • Torres-Sanchez, R., Navarro-Hellin, H., Guillamon-Frutos, A., San-Segundo, R., Ruiz-Abellón, M. C., & Domingo-Miguel, R. (2020). A decision support system for irrigation management: Analysis and implementation of different learning techniques. Water, 12(2), 548. doi:10.3390/w12020548
  • Tyagi, A., Gupta, N., Navani, J. P., Tiwari, R., & Gupta, A. (2017). Smart irrigation system. International Journal for Innovative Research in Science & Technology, 3(10).
There are 33 citations in total.

Details

Primary Language English
Journal Section Computer Science
Authors

Aamo Iorliam 0000-0001-8238-9686

Sylvester Bum This is me 0000-0003-3342-6457

Iember S. Aondoakaa This is me 0000-0002-0812-0109

Iveren Blessing Iorlıam This is me 0000-0002-9973-6151

Yahaya Shehu This is me 0000-0001-8924-9344

Publication Date December 31, 2022
Submission Date July 6, 2022
Published in Issue Year 2022 Volume: 9 Issue: 4

Cite

APA Iorliam, A., Bum, S., Aondoakaa, I. S., Iorlıam, I. B., et al. (2022). Machine Learning Techniques for the Classification of IoT-Enabled Smart Irrigation Data for Agricultural Purposes. Gazi University Journal of Science Part A: Engineering and Innovation, 9(4), 378-391. https://doi.org/10.54287/gujsa.1141575

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