International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

AI-Driven Automation for Green Buildings and Sustainable Agriculture: Enhancing Efficiency, Scalability, and Resource Management

R CharmikaDepartment of Artificial Intelligence and Data Science, Mother Theresa Institute of Engineering and Technology, Palamaner, Chittoor, Andhra Pradesh-517408, IndiaR NavyaDepartment of Artificial Intelligence and Data Science, Mother Theresa Institute of Engineering and Technology, Palamaner, Chittoor, Andhra Pradesh-517408, IndiaC VijayDepartment of Artificial Intelligence and Data Science, Mother Theresa Institute of Engineering and Technology, Palamaner, Chittoor, Andhra Pradesh-517408, IndiaG MadhusudhanDepartment of Artificial Intelligence and Data Science, Mother Theresa Institute of Engineering and Technology, Palamaner, Chittoor, Andhra Pradesh-517408, IndiaL BhavanaDepartment of Artificial Intelligence and Data Science, Mother Theresa Institute of Engineering and Technology, Palamaner, Chittoor, Andhra Pradesh-517408, IndiaM MaheshkumarDepartment of Artificial Intelligence and Data Science, Mother Theresa Institute of Engineering and Technology, Palamaner, Chittoor, Andhra Pradesh-517408, India

Vol 9 No 25 (2025): Volume 9, Special Issue of INSPIRE’25 April 2025 | Pages: 97-103

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 23-04-2025

doi Logo doi.org/10.47001/IRJIET/2025.INSPIRE16

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Abstract

The integration of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) is transforming green buildings and sustainable agriculture by enhancing energy efficiency, predictive maintenance, and resource optimization. This study presents an AI-driven framework incorporating Ensemble Learning, Transfer Learning, and Federated Learning to improve decision-making while ensuring privacy and scalability. Edge AI and IoT enable real-time automation, reducing cloud dependency and operational costs. AI-powered HVAC systems optimize energy use in smart buildings, while AI-IoT synergy improves plant disease detection and precision farming. Explainable AI techniques like LIME and SHAP enhance transparency, making AI-driven insights more interpretable for stakeholders. With a 94% accuracy improvement over traditional methods, this approach minimizes energy wastage, enhances decision-making, and supports sustainable, cost-effective smart environments. The study demonstrates the potential of AI to drive eco-friendly advancements in smart buildings and precision agriculture.

Keywords

AI in Smart Buildings, IoT in Agriculture, Predictive Maintenance, Machine Learning for Automation, Energy Management Systems, Plant Disease Detection, Sustainable AI Solutions


Citation of this Article

R Charmika, R Navya, C Vijay, G Madhusudhan, L Bhavana, & M Maheshkumar. (2025). AI-Driven Automation for Green Buildings and Sustainable Agriculture: Enhancing Efficiency, Scalability, and Resource Management. In proceeding of International Conference on Sustainable Practices and Innovations in Research and Engineering (INSPIRE'25), published by IRJIET, Volume 9, Special Issue of INSPIRE’25, pp 97-103. Article DOI https://doi.org/10.47001/IRJIET/2025.INSPIRE16

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