In the heart of Kuwait, a researcher is tackling a global challenge: making artificial intelligence (AI) more sustainable for agriculture. Ersin Elbasi, from the College of Engineering and Technology at the American University of the Middle East, has published a groundbreaking study in *IEEE Access* that could redefine the future of smart farming. The research introduces a “Green AI” framework designed to reduce the energy consumption and carbon emissions associated with AI-driven agricultural technologies.
AI and machine learning (ML) have revolutionized farming techniques, enabling precise crop yield forecasting, efficient irrigation, and optimized resource management. However, the computational power required for these technologies has led to increased energy consumption and carbon emissions, posing a significant environmental challenge. Elbasi’s research addresses this issue head-on by developing energy-efficient predictive models that support sustainable agriculture.
The Green AI framework presented in the study employs six different methods to create systems that are both energy-efficient and environmentally responsible. These methods include using lighter algorithms, model compression, federated learning, edge computing, and integrating clean energy solutions. By adopting these practices, the framework aims to reduce the carbon footprint of AI applications in agriculture significantly.
“When technological efficiency is combined with environmental friendliness, this kind of work is an example of how Green AI could make the next generation of farming systems possible, which would be climate-resilient, low-carbon, and efficient,” Elbasi explains. This innovative approach not only supports sustainable farming practices but also has the potential to drive commercial impacts across the agriculture sector.
The study highlights the importance of sustainable AI practices in the context of smart agriculture. By reducing the energy consumption of AI systems, farmers can lower their operational costs while minimizing their environmental impact. This dual benefit makes Green AI an attractive proposition for both small-scale farmers and large agricultural enterprises.
Looking ahead, the research suggests several areas for future exploration, including the integration of IoT and blockchain technologies, the development of explainable AI models, and the establishment of sustainability standards driven by policies. These advancements could further enhance the efficiency and environmental responsibility of AI-driven agricultural systems.
Elbasi’s work, published in *IEEE Access*, represents a significant step forward in the quest for sustainable AI in agriculture. By combining cutting-edge technology with environmental consciousness, the Green AI framework offers a blueprint for the future of smart farming. As the agriculture sector continues to evolve, the principles outlined in this research could shape the development of climate-resilient, low-carbon farming systems that benefit both farmers and the planet.