Image Based Automated Weather Analysis Using Deep Learning for Decision Support to Remote Farmers

Authors

  • Jeen Retna Kumar R. Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India
  • Berakhah F. Stanley Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India
  • Subha S. Department of ECE, KLN College of Engineering, Sivagangai, Tamil Nadu, India
  • Jim Reeves S. Department of IT, Jayaraj Annapackiam CSI College of Engineering, Nazareth, Tamil Nadu, India
  • Gold Beulah Patturose J. Department of CSE, St Josephs’s Institute of Technology, Chennai, India
  • Bini Palas P. Department of ECE, SRM Easwari Engineering College,, Chennai, India

Keywords:

Weather Analysis, Convolutional neural network, Bi-directional gated recurrent unit, Precision farming

Abstract

In farming, weather forecasting refers to the prediction of an area's atmospheric state at a specific moment to determine whether it is suitable or unsuitable for agricultural activities. Knowledge about the current weather feed good decision for farmers in crop cultivation. The numerous daily decisions can be better prepared based on weather analysis. These choices include when to fertilize, when to irrigate crops, and when to do fieldwork in the farm. Farmers' choices will determine whether or not their crop is lucrative. A farmer must be mindful of the present climate condition in order to grow a successful crop. In recent years, estimating climate has gained significant research attention. Despite the abundance of data available for weather forecasting, the majority of them require the use of a nonlinear model before forecasting. Due to the advancement in image processing, climate detection from atmosphere images provides much information for identifying weather condition and it helps the farmers choose the appropriate time to operate effectively on a daily basis. This article examines the applicability of the combined convolutional neural network (CNN) and bidirectional gated recurrent unit (Bi-GRU) approach by developing a robust and climate estimation model from real time images acquired from farm atmosphere. The proposed work offers a method for precisely and accurately detecting actual meteorological conditions using computer vision.

Downloads

Download data is not yet available.

References

J. Shivang, S. S. Sridhar, “Weather prediction for indian location using Machine learning,” International Journal of Pure and Applied Mathematics, vol. 118, no. 22 pp. 1945-1949, 2018.W.-K. Chen, Linear Networks and Systems. Belmont, CA, USA: Wadsworth, 1993, pp. 123–135.

Imran Maqsood, Muhammad Riaz Khan, and Ajith Abraham. An ensemble of neural networks for weather forecasting, Neural Comput Applic (2004) 13: 112–122.

Z. U. Khan and M. Hayat, “Hourly based climate prediction using data mining techniques by comprising entity demean algorithm” Middle-East Journal of Scientific Research 21, vol. 8, pp. 1295-1300, 2014.

Santhosh Baboo and I.Kadar Shereef. An efficient weather forecasting system using Artificial neural network. International Journal of Environmental Science and Development, 1(4):321-326, 2010.

Amanpreet Kaur, J K Sharma, and Sunil Agrawal. Artificial neural networks in forecasting maximum and minimum relative humidity. International Journal of Computer Science and Network Security, 11(5):197-199, May 2011.

Sanjay Mathur, Avinash Kumar, and Mahesh Chandra. A feature based neural network model for weather forecasting, World Academy of Science, Engineering and Technology 34 2007.

Mohsen Hayati and Zahra Mohebi. Application of artificial neural networks for temperature forecasting. World Academy of Science, Engineering and Technology, 28:275279, 2007.

S. S. Bhatkande1, R. G. Hubballi2, “Weather Prediction Based on Decision Tree Algorithm Using Data Mining Techniques.” Belgaum India: International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no.5, pp. 483-48, 2016.

Rosmina Bustami, Nabil Bessaih, Charles Bong, and Suhaila Suhaili. Artificial neural network for precipitation and water level predictions of bedup river. IAENG International Journal of Computer Science, 34(2):228-233, 2007.

Surajit Chattopadhyay. Multilayered feed forward artificial neural network model to predict the average summer-monsoon rainfall in india. Master’s thesis, Department of Mathematics, Techno Model SchoolTechno India Group, EM-4/1, Sector V, Salt Lake, Kolkata 700 091, India, 2006.

Tony Hall, Harold E. Brooks, and Charles A. Doswell III. Precipitation forecasting using a neural network, AMS Journal online, Volume 14, Issue 3 (June 1999).

G. J. Sawale, S. R. Gupta, “Use of Artificial Neural Network in Data Mining For Weather Forecasting”, International Journal Of Computer Science And Applications vol. 6, no.2, pp.383-387, 2013.

Swathi, V. N. V. L. S. ., Kumar, G. S. ., & Vathsala, A. V. . (2023). Cloud Service Selection System Approach based on QoS Model: A Systematic Review. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 05–13. https://doi.org/10.17762/ijritcc.v11i2.6104

Brown, R., Brown, J., Rodriguez, C., Garcia, J., & Herrera, J. Predictive Analytics for Effective Resource Allocation in Engineering Education. Kuwait Journal of Machine Learning, 1(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/91

Anupong, W., Yi-Chia, L., Jagdish, M., Kumar, R., Selvam, P. D., Saravanakumar, R., & Dhabliya, D. (2022). Hybrid distributed energy sources providing climate security to the agriculture environment and enhancing the yield. Sustainable Energy Technologies and Assessments, 52 doi:10.1016/j.seta.2022.102142

Downloads

Published

16.07.2023

How to Cite

Kumar R., J. R. ., Stanley, B. F. ., S., S. ., Reeves S., J. ., Patturose J., G. B. ., & Palas P., B. . (2023). Image Based Automated Weather Analysis Using Deep Learning for Decision Support to Remote Farmers. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 867–871. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3343

Issue

Vol. 11 No. 3 (2023)

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.