Green Artificial Intelligence Framework for EnergyEfficient Smart Agriculture in Tropical Environments
Abstract
The increasing demand for sustainable agricultural production in tropical environments has encouraged the integration of environmentally responsible digital technologies to address energy consumption and resource inefficiency in smart farming systems. This study proposes a Green Artificial Intelligence framework designed to enhance energy efficiency, optimize agricultural resource utilization, and support sustainable farming practices in tropical regions characterized by high climate variability and ecological sensitivity. The research aims to develop an intelligent and adaptive agricultural model capable of reducing computational energy usage while improving environmental monitoring and decision making processes in precision agriculture. The proposed framework integrates Internet of Things sensors, machine learning algorithms, and energyaware data processing techniques to monitor soil moisture, temperature, humidity, and crop health in real time. A quantitative experimental approach was employed using simulated agricultural datasets and smart sensor data collected from tropical farming environments. The results demonstrate that the proposed Green AI framework significantly reduces energy consumption in data processing operations while maintaining high predictive accuracy and operational efficiency in smart irrigation and crop management systems. Furthermore, the framework improves environmental sustainability by minimizing excessive water and energy usage in agricultural activities. In conclusion, the study high lights the potential of Green Artificial Intelligence as an innovative solution for developing sustainable, resilient, and energyefficient smart agriculture systems that support longterm environmental conservation and food security in tropical regions.
