Agri-Eco Predict: Minimizing Carbon Intensity in Ensemble Prediction Model for Agricultural Product Price
Keywords:
Agricultural Product Prices, Carbon Footprint, Ensemble Prediction Models, Green AI, Energy Consumption, SustainabilityAbstract
Computational loads of Ensemble prediction models (EPMs) are discussed in this paper with a special emphasis on their carbon footprints or carbon emissions. It is purposefully carried out to determine and reduce the carbon emissions caused by EPMs for forecasting Agricultural product prices (APP). Random Forest (RF) and Gradient Boosting (GBR), as well as combinations of both with an adaptive weighted strategy, were included in the experimental study that assessed energy consumption and carbon emissions of EPMs. Carbon emissions were significantly reduced while maintaining prediction accuracy through optimising these models on CPU and T4 GPU platforms. For instance, for optimized RF models on CPU; there was a decline in carbon emission from 3.576e-07 kgCO2e to 1.793e-07 kgCO2e, while Mean Squared Error (MSE) improved from 3.014 to 2.189 respectively. Similarly, after optimization, GBR models on GPU no longer changed their carbon footprint but changed MSE significantly. The findings indicated that it is possible to mitigate the carbon output without affecting the accuracy of predictions using hyperparameter optimization based EPM.
Downloads
References
Ali, A. H. (2023). Green AI for Sustainability: Leveraging Machine Learning to Drive a Circular Economy. Babylonian Journal of Artificial Intelligence, 2023, 15–16. https://doi.org/10.58496/BJAI/2023/004
Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research.
Breiman, L. (2001). Random forests. Machine Learning. Volume 45,Page 5-32, url={https://api.semanticscholar.org/CorpusID:89141
Castellanos-Nieves, D. (2023). Improving Automated Machine-Learning Systems through Green AI, Appl. Sci. 2023, 13, 11583. https://doi.org/10.3390/app132011583
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Ann. Statist. 29 (5) 1189 - 1232, October 2001. https://doi.org/10.1214/aos/1013203451.
Han, S., Pool, J., Tran, J., & Dally, W. (2015). Learning both weights and connections for efficient neural network. Advances in Neural Information Processing Systems.
Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020). Towards the systematic reporting of the energy and carbon footprints of machine learning. Journal of Machine Learning Research.
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., ... & Kalenichenko, D. (2018). Quantization and training of neural networks for efficient integer-arithmetic-only inference. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
Khaki, S., & Wang, L. (2019). Crop yield prediction using deep neural networks. Frontiers in Plant Science.
Pantazi, X. E., Moshou, D., Alexandridis, T., Whetton, R. L., & Mouazen, A. M. (2016). Wheat yield prediction using machine learning and advanced sensing techniques. Computers and Electronics in Agriculture.
Schmidt, D., Marques, M. R., Botti, S., & Marques, M. A. (2019). Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials.
Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian optimization of machine learning algorithms. Advances in Neural Information Processing Systems.
Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.
Downloads
Published
How to Cite
Issue
Section
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.