Vol. 1, Issue 1, Part A (2024)

The adoption of climate-smart agricultural practices in urban hydroponics farming has gained significant attention due to the need for sustainable food production systems in urban environments. However, challenges such as resource optimization, climate variability, and energy consumption continue to hinder the scalability and efficiency of these practices. The integration of Artificial Intelligence (AI) and Machine Learning (ML) has emerged as a promising solution to optimize these climate-smart practices by enhancing resource management, predicting environmental changes, and improving crop yields in urban hydroponic systems. This paper explores the potential of AI and ML in revolutionizing urban hydroponics farming by providing solutions for real-time monitoring, data-driven decision-making, and optimization of resources. Through a comprehensive review of existing literature, we highlight key advancements in AI and ML technologies, including sensor networks, deep learning algorithms, and predictive models, which are instrumental in achieving sustainable and efficient urban farming systems. Moreover, this paper discusses the challenges, opportunities, and future research directions for leveraging AI and ML in climate-smart urban agriculture. By synthesizing recent research findings and technological innovations, we aim to provide a framework for future applications of AI and ML in enhancing the resilience and sustainability of urban hydroponics farming.