Indoor farming emerges as a crucial solution to address the escalating global demand for food amidst environmental challenges and population growth. To harness the potential of indoor agriculture, this thesis delves into the integration of Artifical Intelligence (AI) Planning, aiming to automate and enhance the efficiency of indoor farming operations. Our core objective is the design and implementation of a proof of concept for an AI Planning-based system tailored to the distinctive requirements of indoor farming. In order to assess the effectiveness of our AI Planning-based approach, a comparative analysis is conducted with a conventional schedule-based system. Key performance indicators to maintain a healthy environment are evaluated. Through this comparative study, we aim to elucidate the potential benefits and drawbacks of integrating AI Planning into indoor farming practices. This research contributes to the evolving field of precision agriculture by showcasing the practical application of AI Planning to address the challenges of indoor farming. The outcomes not only enrich the academic understanding of AI in agriculture but also offer practical insights for the adoption of automated systems, thereby enhancing the sustainability and productivity of indoor farming operations.