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As the global population grows, achieving Zero Hunger by 2030 presents a significant challenge. Vertical farming technology offers a potential solution, making the path planning of agricultural robots in vertical farms a research priority. This study introduces the Vertical Farming System Multi-Robot Trajectory Planning (VFSMRTP) model. To optimize this model, we propose the Elitist Preservation Differential Evolution Grey Wolf Optimizer (EPDE-GWO), an enhanced version of the Grey Wolf Optimizer (GWO) incorporating elite preservation and differential evolution. The EPDE-GWO algorithm is compared with Genetic Algorithm (GA), Simulated Annealing (SA), Dung Beetle Optimizer (DBO), and Particle Swarm Optimization (PSO). The experimental results demonstrate that EPDE-GWO reduces path length by 24.6%, prevents premature convergence, and exhibits strong global search capabilities. Thanks to the DE and EP strategies, the EPDE-GWO requires fewer iterations to reach the optimal solution, offers strong stability and robustness, and consistently finds the optimal solution at a high frequency. These attributes are particularly significant in the context of vertical farming, where optimizing robotic path planning is essential for maximizing operational efficiency, reducing energy consumption, and improving the scalability of farming operations. [ABSTRACT FROM AUTHOR]

Vertical farming systems, such as sky farms, are a potential type of agricultural system for stable and effective food production. Here, we highlight the potential of the sky farm, denoted as the “skyscraper crop factory” (SCF), for cereal crop production and discuss some nascent technologies that would be applied in this production system. SCFs are ideal crop-production systems for increasing the effective arable area for crops and ensuring food security in times of crises that cause a shock in global trade. They can also provide food in urban areas to meet producers’ and consumers’ demands for the increased nutrition, taste, and safe production of cereal crops. Moreover, as their use can reduce greenhouse gas emissions, SCFs could be a sustainable addition to conventional agricultural crop production.

As the global population grows, achieving Zero Hunger by 2030 presents a significant challenge. Vertical farming technology offers a potential solution, making the path planning of agricultural robots in vertical farms a research priority. This study introduces the Vertical Farming System Multi-Robot Trajectory Planning (VFSMRTP) model. To optimize this model, we propose the Elitist Preservation Differential Evolution Grey Wolf Optimizer (EPDE-GWO), an enhanced version of the Grey Wolf Optimizer (GWO) incorporating elite preservation and differential evolution. The EPDE-GWO algorithm is compared with Genetic Algorithm (GA), Simulated Annealing (SA), Dung Beetle Optimizer (DBO), and Particle Swarm Optimization (PSO). The experimental results demonstrate that EPDE-GWO reduces path length by 24.6%, prevents premature convergence, and exhibits strong global search capabilities. Thanks to the DE and EP strategies, the EPDE-GWO requires fewer iterations to reach the optimal solution, offers strong stability and robustness, and consistently finds the optimal solution at a high frequency. These attributes are particularly significant in the context of vertical farming, where optimizing robotic path planning is essential for maximizing operational efficiency, reducing energy consumption, and improving the scalability of farming operations.

A plant factory has been developed at Horticultural Research Centre, MARDI Serdang. The cultivation approach under the plant factory consists of nine units planting racks with each rack having seven tiers or levels that can cultivate up to 900 crops per rack. Each unit of planting racks has been installed with electrical conductivity (EC) and pH monitoring system. EC is a meaningful indicator of water quality, soil salinity and fertilizer concentration. In this study, the data for EC and temperature were taken for each level different locations (three points along the rack) to evaluate the fertilizer quality distribution. The objective of the study was to evaluate the effectiveness of EC and temperature distribution on the planting rack of vertical farming system under the plant factory. From the study, it was found that EC and temperature parameters were not significantly different at each level and the point location of the vertical farming system. EC and temperature parameters were significantly different with the time (week) and point location from week to week. The effect of the interaction between time (week) and level on EC and temperature parameters were not statistically different. Therefore, it can be concluded that the effect of EC and temperature distribution at different levels of the vertical farming system did not depend on the time.