Proposal for a Genetic Algorithm-Based Approach to Optimize Light Spectrum in Vertical Farming

Agriculture is crucial for global food security and economic development but faces significant challenges due to resource constraints and a global population that keeps increasing. Vertical farming, with its potential to optimize food production in land-scarce and environmentally stressed regions, emerges as a viable solution. However, the high-energy demands for lighting in vertical farms significantly impact operational expenses. This study introduces a novel Genetic Algorithm (GA) designed to optimize artificial lighting in vertical farms to enhance Light Use Efficiency (LUE). The proposed GA seeks to identify the optimal spectral composition of Red, Green, and Blue (RGB) LEDs, aiming to maximize crop productivity by evaluating characteristics such as height, width, fresh weight, and leaf count. The algorithm operates through ten stages, including initialization of a population, actuation of RGB values, fitness evaluation, and iterative processes of selection, crossover, mutation, and validation. By comparing RGB treatments with a reference cold white light treatment, the algorithm refines lighting conditions to improve crop performance at different growth stages. Detailed methodologies for fitness evaluation, crossover, mutation, and validation are provided, highlighting the practical steps for implementing this approach in vertical farming environments. This research aims to contribute to more energyefficient and productive vertical farming practices, supporting the broader goal of sustainable agricultural development.