Complex Interactions of Applied Artificial Intelligence, Machine Learning and Plant Science in Space Food Production

Dear Colleagues,

The rapid advancement of Industrial Revolution 4.0 technologies has paved the way for groundbreaking innovations in agricultural engineering. As humanity embarks on bold space exploration missions, ensuring sustainable and efficient food production becomes a critical aspect of long-term space habitation. Are terrestrial agricultural engineering practices and technologies still applicable for space food production? Plant science (microalgae and higher plants) can benefit from the application of AI to help create self-sustaining ecosystems by optimizing resource efficiency, lowering dependency on outside sources, recycling nutrients or materials within the system, and continuing to produce a consistent amount of fresh food for human consumption. The goal of AI in plant science for space food production is to develop long-term space missions or extraterrestrial colonization sustainable systems. By improving resource efficiency, lowering energy and water consumption, and minimizing environmental effects, AI-driven agriculture can enhance sustainability and the long-term viability of space-based food production. The objective is to develop resilient and sustainable agricultural systems that can support human life in space while reducing ecological footprints and optimizing resource efficiency by incorporating AI into plant science for space food production. Connecting these ideas highlights how crucial it is to use AI in plant science to promote sustainable technologies in space exploration and habitation, in addition to effective food production. Hence, this Special Issue in AgriEngineering explores the intersection of applied artificial intelligence (AAI), machine learning (ML), and plant science within space food production, aiming to achieve SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production) by leveraging agricultural engineering principles and advanced intelligent technologies. By integrating AI-guided environmental controls and optimizing plant growth, this research seeks to establish self-sustaining ecosystems, aligning with SDG 9 (Industry, Innovation, and Infrastructure) for long-term space habitation, fostering sustainable food systems beyond Earth's confines.

Given the above rationale, this Special Issue, titled “Complex Interactions of Applied Artificial Intelligence, Machine Learning and Plant Science in Space Food Production”, aims to collect scientific contributions addressing these particular questions:

• What are the key biological and physiological responses of microalgae and higher plants to artificial intelligence-driven environmental controls in space habitats? How do complex interactions between AI-controlled environmental variables impact the resilience and adaptability of plant species in space?
• How does the application of AI influence the nutrient composition and growth rate of microalgae and higher plants cultivated in space environments?
• What are the optimal machine learning algorithms for predicting microalgae and higher plant growth patterns and maximizing yield in controlled space agriculture?
• What are the long-term effects of AI-driven agricultural practices on genetic expression and evolution in plants grown for space food production?
• How can AI-assisted closed-loop systems minimize resource utilization and waste generation in space agriculture? How does AI integration affect the sustainability and overall ecological footprint of space-based food production systems?
• What environmental impacts, such as energy consumption and waste management at the least, are associated with implementing AI-guided agriculture in space habitats, and how can they be mitigated?
• What agricultural engineering techniques, classical, advanced, and hybrid, can induce the growth of higher plants and microalgae for immediate human consumption?
• Can AI-driven plant cultivation techniques in space lead to a reduction in water usage and recycling, enhancing sustainability within closed-loop ecosystems?
• What are the potential risks and benefits of introducing AI-controlled agricultural practices to extraterrestrial environments?
• How can AI be optimized to regulate and maintain environmental conditions, such as temperature, humidity, and light intensity, for optimal plant growth in space habitats?
• What are the challenges and opportunities in designing and implementing AI-driven autonomous robotic systems for planting, monitoring, and harvesting crops in confined space environments?
• How can AI be integrated into smart sensor networks to monitor and manage plant health and growth parameters in real-time in space-based agricultural settings?
• What engineering solutions are required to ensure the reliability and safety of AI-driven systems in space agriculture, considering the harsh conditions and limited resources?
• How can AI-assisted modeling and simulation be employed to design and iterate efficient space-based food production systems considering space and resource constraints?

Scope of this Special Issue:

The scope of this topic includes, but is not limited to, the following areas:

• Optimizing AI algorithms for controlled space agriculture (CSA), involving the investigation and refinement of machine learning algorithms to enhance the predictability and control of plant growth parameters in space environments.
• Closed-loop systems and resource efficiency examining the design and implementation of closed-loop systems to minimize resource consumption and waste generation in space-based plant cultivation.
• AI-driven robotic systems for space agriculture that explore engineering challenges and opportunities in developing autonomous robotic systems guided by AI for planting, monitoring, and harvesting crops in confined space habitats.
• Environmental impact assessment (EIA) of AI in space agriculture (including Earth-based hypergravity and microgravity cultivation systems). This involves analyzing the ecological footprint and energy consumption associated with implementing AI-driven agriculture in space and proposing strategies for minimizing environmental impact.
• Sustainable nutrient management which investigates how AI can optimize nutrient cycling and recycling within space-based plant cultivation systems.
• Genetic expression and evolution in controlled environments to understand the long-term effects of AI-driven agricultural practices on the genetic expression and evolution of plants intended for space food production.
• Human–plant interaction (HPI) studies in closed environments which explore the psychological and physiological impacts of AI-assisted plant cultivation on astronauts in space habitats to ensure holistic wellbeing.
• Integration of AI into smart sensor networks (SSN) to investigate the incorporation of AI into sensor networks to monitor and manage plant health and environmental conditions in real-time, enabling dynamic adjustments for optimal growth.
• AI modeling and simulation for space-based agriculture (SBA) utilizing AI-assisted modeling and simulation to design and iterate efficient space-based food production systems, considering space and resource constraints.
• Intelligent bioregenerative life support systems (BLSSs) to examine the integration of AI in the development of bioregenerative life support systems, aiming for sustainable food production in prolonged space missions.
• Investigating the social and ethical considerations surrounding the use of AI in plant science for space food production, considering its impact on equitable access and ethical food production.
• Risk assessment and safety (RAS) in AI-driven space agriculture.
• AI-enhanced crop diversity and adaptability by mainly using optimization algorithms and advanced metaheuristics, such as, but not limited to, evolutionary computing and physics- and biology-inspired models.

Dr. Ronnie S. Concepcion II
Dr. João Luiz Junho Pereira
Prof. Dr. D. Marshall Marshall Porterfield
Dr. Jane Roche
Dr. Colin P. S. Kruse
Guest Editors

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• agricultural life-cycle
• AgTech
• AI in agriculture
• AI modeling and simulation
• Algal production and technology
• applied artificial intelligence
• bioastronautics
• biochemical engineering and analytics
• bioenergy
• bioregenerative life support
• biophysics
• biosystems and bioeconomy
• biotechnology in space
• clean energy production
• computer vision
• design for environment
• design for optimized future food system
• ecodesign
• environmental impact assessment
• evolutionary computing
• future food systems
• genetic expression
• gravitational biology
• green intelligent system
• greenhouse gas emissions
• microalgal technology
• nutrient management
• plant science
• postharvest system
• resource efficiency
• smart sensor networks
• space agriculture and space farming
• space biology
• space habitats
• sustainable agriculture and sustainable food production
• sustainable energy resource
• sustainable production and consumption
• sustainable technology