Smart Agriculture through IoT and Machine Learning for Analyzing Carbon Footprints

DOI

10.2991/978-94-6463-858-5_273How to use a DOI?

Keywords

Carbon Footprint; Smart Agriculture; Internet of Things; Machine Learning; Emission Prediction; Sustainability

Abstract

The agriculture sector has become a focal point in addressing global greenhouse gas emissions due to its substantial contribution to the carbon footprint. As population growth and resource consumption accelerate, there is an urgent need for sustainable solutions to mitigate environmental impacts without compromising food security. This research aims to fill the knowledge gap in understanding and quantifying the carbon footprint in agriculture, focusing on sustainable practices enabled by Internet of Things (IoT) and machine learning (ML) technologies. The study employs a range of regression models, including Decision Tree, Random Forest, Support Vector Regressor, AdaBoost, and K-Nearest Neighbors, to predict carbon emissions based on agricultural and environmental data collected via IoT sensors. The novelty lies in the integrated use of these algorithms, each contributing to a robust framework that captures complex, non-linear relationships within the data. This approach allows for a comprehensive analysis of carbon footprint dynamics, enhancing prediction accuracy and supporting proactive decision-making for emission reduction. Key findings indicate that the Random Forest model outperformed others, achieving the highest accuracy in predicting agricultural carbon footprints. These results suggest that the proposed method is not only effective in estimating emissions but also valuable in identifying high-impact areas for sustainable interventions. By applying this model, policymakers and farmers can make data-driven decisions that support environmental goals. The significance of these findings lies in the potential for smart agriculture to drive a sustainable transition in food production, emphasizing IoT and ML as pivotal tools in managing carbon footprints.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ravi Kumar Banoth
AU  - B. V. Ramana Murthy
PY  - 2025
DA  - 2025/11/04
TI  - Smart Agriculture through IoT and Machine Learning for Analyzing Carbon Footprints
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3274
EP  - 3288
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_273
DO  - 10.2991/978-94-6463-858-5_273
ID  - Banoth2025
ER  -