Optimizing Indoor Farming: Deep Learning for Predicting Plant Growth under LED Light Treatments

Indoor farming has emerged as a rapidly growing industry that harnesses controlled environmental conditions to cultivate crops. A key component of indoor farming is the utilization of LED lighting, which serves as the primary source of light for plant growth. In this study, our objective is to optimize indoor farming practices through the application of deep learning techniques, specifically by predicting the growth of plants under different LED light treatments in controlled agricultural environments. To achieve this goal, we employed existing machine learning methods and proposed a novel deep learning approach that incorporates the effects of LED light spectrum on plant growth to estimate plant biomass. Our deep learning model, BioNet, utilizes 1D convolutional neural network (CNN) to extract spatial features from the light spectrum data. Through extensive experimentation and analysis, we demonstrate that our deep learning method outperforms other conventional methods, showcasing its potential to enhance our understanding of the impact of LED light on plant growth.Our research provides valuable insights into optimizing indoor farming by uncovering the relationship between LED light treatments and plant biomass. BioNet serves as a valuable tool for farmers, enabling informed decisions on LED light selection, leading to improved efficiency and productivity in indoor farming. This study contributes to advancing indoor farming techniques through deep learning, opening new avenues for exploration and highlighting potential improvements in the field.