Optimizing Crop Forecasts: Leveraging Feature Selection and Ensemble Methods

Main Article Content

Jahnavi Reddy G
Sunkavalli Satwika Devi
Shreeya Dheera Parvatham
Koyyalamudi Susrutha Vishal
Sanjana chowdary M

Abstract

Agricultural research is undergoing significant advancements, particularly in the realm of crop forecasting. Historically, the success of agriculture has been deeply intertwined with understanding environmental and soil variables, such as temperature, humidity, and precipitation, as these factors play a pivotal role in crop growth and yield. Traditionally, farmers made informed decisions about which crops to plant, monitored their growth, and determined the optimal harvest time. However, predicting crop outcomes has always been a complex endeavor. To address this challenge, various models, especially Classification Techniques of Machine Learning, have been developed and tested. This study focuses on improving crop prediction accuracy by leveraging Ensemble Techniques. When comparing the Ensemble approach with existing classification methods, it was observed that algorithms like Decision Tree, Support Vector Machine, and Random Forest outperformed their counterparts and delivered superior accuracy.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
Reddy G, J. ., Devi, S. S. ., Parvatham, S. D. ., Vishal, K. S. ., & chowdary M, S. . (2023). Optimizing Crop Forecasts: Leveraging Feature Selection and Ensemble Methods. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 14(03), 1062–1071. Retrieved from https://turcomat.org/index.php/turkbilmat/article/view/14289
Section
Research Articles

References

. Aslan, O., and Samet, R., (2018)."Mitigating Cyber Security Attacks by Being Aware of Vulnerabilities

and Bugs," 2017 International Conference on Cyberworlds (CW), Chester, UK, 2017, pp. 222-225, doi:

1109/CW.2017.22.

. Algarni, A. M., Thayananthan, V., & Malaiya, Y. K. (2021). Quantitative Assessment of Cybersecurity

Risks for Mitigating Data Breaches in Business Systems. Retrieved from https://www.mdpi.com/2076-

/11/8/3678

. Bamrara, A. (2023). Cyber Attacks and Defense Strategies in India: An Empirical Assessment of Banking

Sector. Retrieved from

https://www.academia.edu/100835187/Cyber_Attacks_and_Defense_Strategies_in_India_An_Empirica

l_Assessment_of_Banking_Sector

. Boscoianu, M. (2021). Refined Concepts of Massive and Flexible Cyber Attacks with Information

Warfare Strategies. Retrieved from

https://www.academia.edu/57722212/Refined_Concepts_of_Massive_and_Flexible_Cyber_Attacks_wi

th_Information_Warfare_Strategies

. Duan, N., Yee, N., Otis, A., Joo, J. C., Stewart, E., Bayles, A., Spiers, N., & Cortez, E. (2021). Mitigation

Strategies Against Cyberattacks on Distributed Energy Resources. IEEE.

https://doi.org/10.1109/isgt49243.2021.9372173

. Gandal, N., Moore, T., Riordan, M., & Barnir, N. (2023). Empirically evaluating the effect of security

precautions on cyber incidents. Computers & Security, 133,

https://doi.org/10.1016/j.cose.2023.103380

. Hasan, M. R. (2022). Cybercrime Techniques in Online Banking. Int. J. of Aquatic Science, 13(1), 524-

Retrieved from: https://www.journal-aquaticscience.com/article_158883.html (January 2022)

. Khalel, S., & Khudher, S., (2022)."Cyber-Attacks Risk Mitigation on Power System via Artificial

Intelligence Technique," 2022 9th International Conference on Electrical and Electronics Engineering

(ICEEE), Alanya, Turkey, 2022, pp. 117-122, doi: 10.1109/ICEEE55327.2022.9772559.

. Kolesnikov, N. (2023). 50+ Cybersecurity Statistics for 2023 You Need to Know – Where, Who & What

is Targeted. Retrieved from https://www.techopedia.com/cybersecurity-statistics

. Kelemen, R. (2020). Cyber Attacks and Cyber Intelligence in the System of Cyber Warfare.

Retrieved from

https://www.academia.edu/44681867/Cyber_Attacks_and_Cyber_Intelligence_in_the_System_of_Cyb

er_Warfare

. Liu, X., Zhang, J., Zhu, P., Tan, Q., & Yin, W. (2021). Quantitative cyber-physical security

analysis methodology for industrial control systems based on incomplete information Bayesian

game. Computers & Security, 102, 102138. https://doi.org/10.1016/j.cose.2020.102138

. Hasan, M. R. (2022). Cybercrime Techniques in Online Banking. Int. J. of Aquatic Science,

(1), 524-

. 541. Retrieved from https://www.journal-aquaticscience.com/article_158883.html

. Pran, R. H. (2022). Business Impact Analysis of Cyber-attacks in Bank by Social Network

Analysis and Machine Learning. Retrieved from

https://www.academia.edu/92999479/Business_Impact_Analysis_of_Cyber_attacks_in_Bank_by_Soci

al_Network_Analysis_and_Machine_Learning

. Riggs, H., Tufail, S., Parvez, I., Tariq, M., Khan, M. A., Amir, A., … Sarwat, A. I. (2023).

Impact, Vulnerabilities, and Mitigation Strategies for Cyber-Secure Critical Infrastructure. Retrieved

from https://www.mdpi.com/1424-8220/23/8/4060

. Saad, M., Bukhari, S., and Kim, C., (2019). "A review of various modern strategies for

mitigation of cyber-attacks in smart grids," 2019 IEEE Transportation Electrification Conference and

Expo, Asia-Pacific (ITEC Asia-Pacific), Seogwipo, Korea (South), 2019, pp. 1-7, doi: 10.1109/ITECAP.2019.8903798.

. Sheehan, B., Murphy, F., Kia, A. N., & Kiely, R. (2021). A quantitative bow-tie cyber risk

classification and assessment framework. Journal of Risk Research, 24(12), 1619–1638.

doi:10.1080/13669877.2021.1900337 Statista. (2023). Cyber-attacks: most-targeted industries 2021-

https://www.statista.com/statistics/221293/cyber-crime-target-industries/

. Suzen, A. (2020). A Risk-Assessment of Cyber Attacks and Defense Strategies in Industry 4.0

Ecosystem. Retrieved from

https://www.academia.edu/42883345/A_Risk_Assessment_of_Cyber_Attacks_and_Defense_Strategies

_in_Industry_4_0_Ecosystem

. Will. (2023). Cyber Risk Evaluation and Mitigation - A Quantitative Research Analysis -.

Retrieved from https://researchpod.org/informatics-technology/cyber-risk-evaluation-mitigation