IJCOPE Journal

UGC Logo DOI / ISO Logo

International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 8

Published on: August 2026

MACHINE LEARNING-BASED PREDICTION OF COCONUT AGRICULTURAL PRODUCTIVITY UNDER CLIMATE CHANGE DYNAMICS

R. KANIMOHZI

Dr. V. MANIRAJ

Department of Computer Science, A.V.V.M Sri Pushpam College (Autonomous), Poondi, Thanjavur (Dt), Affiliated to Bharathidasan University, Tiruchirappalli, Tamil Nadu, India

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Abstract

Coconut trees are often called the 'Tree of Life' because they provide food, oil, and income for millions of small farmers. However, global climate change—such as rising temperatures, long droughts, unpredictable rains, and dry air—is hurting coconut farms. Traditional math models struggle to predict yields because coconut trees take nearly 3.5 years (44 months) to grow nuts, meaning bad weather today causes low yields over a year later. In this study, we built smart computer programs (Machine Learning models like XGBoost, LightGBM, Random Forest, and SVR) using 25 years of weather and soil data (2000–2024). Our best combined AI model achieved an outstanding 94.2% accuracy in predicting nut yields. We discovered that high summer heat during flowering and long dry spells are the main reasons coconut production drops. By 2050, coconut yields could fall by up to 18.4% if farmers do not adopt smart watering and soil care methods.

Keywords: Coconut Production, Simple AI Models, Climate Change Effects, Yield Prediction, XGBoost, Easy Research Guide.

How to Cite this Paper

KANIMOHZI, R. (2026). Machine Learning-Based Prediction of Coconut Agricultural Productivity Under Climate Change Dynamics. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(8), 1-9. https://doi.org/10.55041/ijcope.v2i8.170

KANIMOHZI, R.. "Machine Learning-Based Prediction of Coconut Agricultural Productivity Under Climate Change Dynamics." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 8, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i8.170.

KANIMOHZI, R.. "Machine Learning-Based Prediction of Coconut Agricultural Productivity Under Climate Change Dynamics." International Journal of Creative and Open Research in Engineering and Management 02, no. 8 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i8.170.

Search & Index

References

[1] Hebbar, K. B., et al. (2020). Impact of climate change on coconut productivity in India and adaptation strategies. Regional Environmental Change, 20(4), 112.

[2] Kumar, S. N., & Aggarwal, P. K. (2013). Climate change and coconut plantation in India: Impacts and adaptation strategies. Agricultural Forest Meteorology, 180, 157-172.

[3] Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765-4774.

[4] Voora, V., et al. (2020). Global Market Report: Coconut. International Institute for Sustainable Development (IISD).

[5] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.

Ethical Compliance & Review Process

  • All submissions are screened under plagiarism detection.
  • Review follows editorial policy.
  • Authors retain copyright.
  • Peer Review Type: Double-Blind Peer Review
  • Published on: Aug 22 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Scroll to Top