ASI (Agriculture Smart Irrigation) Multiparameter Optimization System for Precision Agriculture

Authors

  • Suresh P. Associate professor, Department of Computer science and Engineering, KPR Institute of Engineering and Technology, Tamilnadu, India
  • Jenifa G. Assistant professor, Department of Computer science and Engineering, KPR Institute of Engineering and Technology, Tamilnadu, India
  • Srithar S. Associate professor, Department of Computer science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andrapradesh, India
  • Johncy G. Assistant Professor, Department of Computer science and Engineering, St.Xavier’s Catholic College of Engineering, Tamilnadu, India
  • Aswathy R. H. Assistant professor, Department of Computer science and Engineering, KPR Institute of Engineering and Technology, Tamilnadu, India

Keywords:

Artificial Neural Network, Genetic Algorithm, Multiparameter Optimization, Precision Agriculture, Heuristic approach

Abstract

Irrigation is a significant process in smart precision agriculture to manage the water utilization of crop demand. The water required for irrigation is remarkably inconsistent, and the farmer’s decision is final regarding when and how much to irrigate. The accurate decision of irrigation process occurrence in the farm field is an important cause to scale and improve the water management procedure, accordingly, the sustainability of smart irrigation. In this proposed research work, a heuristic methodology that combines an Artificial Neural Network and the Genetic Algorithm had developed to predict the optimal water demand of the crop. The proposed methodology tested with real-time agricultural data showed that developed models have been well-suitable for optimal irrigation in precision agriculture contexts.

Downloads

Download data is not yet available.

References

Kumar, P. and Reddy, S.R.N. (2016), “Design and development of M3SS: A Soil Sensor Node for precision agriculture”, In 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE), IEEE, pp. 1-10.

Unduche, F., Tolossa, H., Senbeta, D. and Zhu, E. (2018), “Evaluation of four hydrological models for operational flood forecasting in a Canadian Prairie watershed”, Hydrological sciences journal, Vol. 63, No. 8, pp.1133-1149.

Partarini, N.M.C., Suhartanto, E. and Harisuseno, D. (2019), “Validasi data curah hujan trmm (tropical rainfall measurement mission) sebagai alternatif data hidrologi di sub-das lesti”, Journal Mahasiswa Jurusan Teknik Pengairan, Vol. 2, No. 2, pp.21.

Ilie-Ablachim, D., Pătru, G.C., Florea, I.M. and Rosner, D. (2016), “Monitoring device for culture substrate growth parameters for precision agriculture: Acronym: MoniSen”, In 2016 15th RoEduNet Conference: Networking in Education and Research, IEEE, pp. 1-7.

Goap, A., Sharma, D., Shukla, A.K. and Krishna, C.R. (2018), “An IoT based smart irrigation management system using Machine learning and open source technologies”, Computers and electronics in agriculture, Vol. 155, pp.41-49.

Sorensen, C.G., Fountas, S., Nash, E., Pesonen, L., Bochtis, D., Pedersen, S.M., Basso, B. and Blackmore, S.B. (2010), “Conceptual model of a future farm management information system”, Computers and electronics in agriculture, Vol. 72, No. 1, pp.37-47.

Patrício, D.I. and Rieder, R. (2018), “Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review”, Computers and electronics in agriculture, Vol. 153, pp.69-81.

Mekonnen, Y., Namuduri, S., Burton, L., Sarwat, A. and Bhansali, S. (2019), “Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture”, Journal of the Electrochemical Society, Vol. 167, No. 3, pp.037522.

Ahmad, N., Hussain, A., Ullah, I., & Zaidi, B. H. (2019), “IOT based Wireless Sensor Network for Precision Agriculture”, In 2019 7th International Electrical Engineering Congress (iEECON), IEEE, pp. 1-4.

Ali, S.M.A.M. (2018), “A Survey on Agriculture Crop Recommender System”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 7, No. 2.

Downloads

Published

13.12.2023

How to Cite

P., S. ., G., J. ., S., S. ., G., J. ., & R. H., A. . (2023). ASI (Agriculture Smart Irrigation) Multiparameter Optimization System for Precision Agriculture. International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 326–333. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4124

Issue

Vol. 12 No. 8s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.