Regional conditions shape the food-energy-land nexus of low-carbon indoor farming

Artificial intelligence can regulate light and climate systems to reduce energy use in plant factories and support sustainable food production

Plant factories with artificial lighting (PFALs) can boost food production per unit area but require resources such as carbon dioxide and energy to maintain optimal plant growth conditions. Here we use computational modelling and artificial intelligence (AI) to examine plant-environment interactions across ten diverse global locations with distinct climates. AI reduces energy use by optimizing lighting and climate regulation systems, with energy use in PFALs ranging from 6.42 kWh kg-1 in cooler climates to 7.26 kWh kg-1 in warmer climates, compared to 9.5-10.5 kWh kg-1 in PFALs using existing, non-AI-based technology. Outdoor temperatures between 0 °C and 25 °C favour ventilation-related energy use reduction, with outdoor humidity showing no clear pattern or effect on energy use. Ventilation-related energy savings negatively impact other resource utilization such as carbon dioxide use. AI can substantially enhance energy savings in PFALs and support sustainable food production.

Assessing environmental sustainability by combining product service systems and life cycle perspective: A case study of hydroponic urban farming models in India

Hydroponics technology offers an environmentally sustainable alternative to conventional farming for urban food needs. It attracts technologists, non-farmers, retailers, restaurants, and consumers. However, the environmental impact of hydroponics-based urban farming models is yet to be quantified. This study assesses the environmental impact of hydroponics-based urban farming models and makes suggestions to improve their adoption. The methodology involves the use of the Product-Service Systems perspective to categorise the hydroponics-based urban farming models and the Life Cycle Assessment (LCA) method to quantify their environmental impact from a cradle-to-gate perspective. The analysis focuses on the lettuce crop in the state of Tamil Nadu, India. The results from the study suggest that that greenhouse farming (BM1) is more environmentally sustainable than indoor farming (BM2), Cabinet selling and remote monitoring (BM3), and conventional farming. It outperforms other models in terms of GHG emissions, Human Toxicity, and fossil fuels per unit of product, with BM3 having high environmental impacts.

Testing the effect of semi-transparent spectrally selective thin film photovoltaics for agrivoltaic application: A multi-experimental and multi-specific approach

The integration of photovoltaic technologies within the agricultural framework, known as agrivoltaics, emerges as a promising and sustainable solution to meet the growing global demands for energy and food production. This innovative technology enables the simultaneous utilization of sunlight for both photovoltaics (PV) and photosynthesis. A key challenge in agrivoltaic research involves identifying technologies applicable to a wide range of plant species and diverse geographic regions. To address this challenge, we adopt a multi-experimental and multi-species approach to assess the viability of semi-transparent, spectrally selective thin-film silicon PV technology. Our findings demonstrate compatibility with crop production in controlled environments for both plants and algae. Notably, selective thin-film PV exhibits the potential to enhance crop yields and serves as a photo-protectant. We observe that plant and algal growth increases beneath the selective PV film when supplemented with appropriate diffuse light in the growth environment. Conversely, in situations where light intensity exceeds optimal levels for plant growth, the selective PV film provides a photo-protective effect. These results suggest potential supplementary benefits of employing this technology in regions characterized by excessive light irradiation, where it can contribute to healthy plant growth.

Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture

Indoor agriculture is emerging as a promising approach for increasing the efficiency and sustainability of agri-food production processes. It is currently evolving from a small-scale horticultural practice to a large-scale industry as a response to the increasing demand. This led to the appearance of plant factories where agri-food production is automated and continuous and the plant environment is fully controlled. While plant factories improve the productivity and sustainability of the process, they suffer from high energy consumption and the difficulty of providing the ideal environment for plants. As a small step to address these limitations, in this article we propose to use internet of things (IoT) technologies and automatic control algorithms to construct an energy-efficient remote control architecture for grow lights monitoring in indoor farming. The proposed architecture consists of using a master-slave device configuration in which the slave devices are used to control the local light conditions in growth chambers while the master device is used to monitor the plant factory through wireless communication with the slave devices. The devices all together make a 6LoWPAN network in which the RPL protocol is used to manage data transfer. This allows for the precise and centralized control of the growth conditions and the real-time monitoring of plants. The proposed control architecture can be associated with a decision support system to improve yields and quality at low costs. The developed method is evaluated in emulation software (Contiki-NG v4.7),its scalability to the case of large-scale production facilities is tested, and the obtained results are presented and discussed. The proposed approach is promising in dealing with control, cost, and scalability issues and can contribute to making smart indoor agriculture more effective and sustainable.

A systematic scoping review of the sustainability of vertical farming, plant-based alternatives, food delivery services and blockchain in food systems

Food system technologies (FSTs) are being developed to accelerate the transformation towards sustainable food systems. Here we conducted a systematic scoping review that accounts for multiple dimensions of sustainability to describe the extent, range and nature of peer-reviewed literature that assesses the sustainability performance of four FSTs: plant-based alternatives, vertical farming, food deliveries and blockchain technology. Included literature had a dominant focus on environmental sustainability and less on public health and socio-economic sustainability. Gaps in the literature include empirical assessments on the sustainability of blockchain technology, plant-based seafood alternatives, public health consequences of food deliveries and socio-economic consequences of vertical farming. The development of a holistic sustainability assessment framework that demonstrates the impact of deploying FSTs is needed to guide investments in and the development of sustainable food innovation.