PLNet: Light Recipe Design for Indoor Farming through Generative Deep Learning

Indoor farming has emerged as a promising solution for year-round cultivation and efficient resource utilization in crop production. Achieving optimal plant growth and quality in indoor environments requires precise control of light conditions. This study introduces PLNet, a generative deep learning method for the designing the light recipes specifically tailored for indoor farming.Leveraging the power of deep neural networks, our approach establishes complex connections between light spectra and plant growth characteristics. Initially, a biomass estimator model is trained using a diverse dataset encompassing different light recipes and corresponding plant responses. Subsequently, a generative model is trained using the estimator model as a foundation, enabling the generation of optimal light spectra to achieve desired growth outcomes. This novel generative method offers an efficient and effective approach to formulating light recipes for indoor farming. By reducing the reliance on traditional trial-and-error methods, our method saves significant time and resources.The presented generative deep learning method holds great potential for advancing the design of light recipes in indoor farming. Leveraging the capabilities of deep neural networks facilitates more targeted and efficient optimization of light conditions, resulting in improved crop yield and quality for a variety of leafy green crops. The findings of this study contribute to the ongoing efforts in enhancing productivity and sustainability in indoor cultivation practices.