Consumer attitudes to vertical farming (indoor plant factory with artificial lighting) in China, Singapore, UK, and USA: A multi-method study

Fresh bell peppers consumed in cities: Unveiling the environmental impact of urban and rural food supply systems

Agriculture and its supply chain pose significant environmental threats. This study employs Life Cycle Assessment (LCA) to explore the environmental impact of fresh bell pepper production and distribution, comparing Urban and Peri-Urban Agriculture (UPA) with Rural Long-Distance Food Supply Systems (RLDFS). Four UPA scenarios (hydroponics, soil-based greenhouse, open-field conventional, and organic) and two RLDFS scenarios (soil-based greenhouse and open-field conventional) are evaluated using SimaPro, incorporating inputs from UPA practitioners and rural farmers. Results reveal an energy demand range of 0.011 to 5.5 kWh/kg eq., with urban greenhouses exhibiting the lowest consumption and hydroponics the highest due to lighting, ventilation, and irrigation. Hydroponics exhibits a global warming potential of 7.24 kg of CO2 eq·kg-1, with energy demand contributing over 95 %, surpassing other scenarios by 7-25 times, necessitating reduction for sustainability. RLDFS's environmental impact is dominated by transportation (over 70 %), meanwhile other UPA systems are influenced by irrigation, infrastructure, and fertilizers. Despite challenges, UPA-hydroponics proves to be 1.7 to 4.3 times more land-use-efficient than other scenarios, emphasizing its potential. The study highlights the need to address electricity usage in UPA-hydroponics for carbon footprint reduction. Despite challenges, hydroponics could contribute to sustainable food security, and RLDFS does not significantly lag in environmental performance compared to UPA other than Ozone layer depletion criteria attributed to fossil fuel usage in transportation. These insights offer valuable guidance for urban development and policy formulation, promoting sustainable agricultural practices and supporting policies for agronomic and supply chain sustainability.

Estimation of rice seedling growth traits with an end-to-end multi-objective deep learning framework

In recent years, rice seedling raising factories have gradually been promoted in China. The seedlings bred in the factory need to be selected manually and then transplanted to the field. Growth-related traits such as height and biomass are important indicators for quantifying the growth of rice seedlings. Nowadays, the development of image-based plant phenotyping has received increasing attention, however, there is still room for improvement in plant phenotyping methods to meet the demand for rapid, robust and low-cost extraction of phenotypic measurements from images in environmentally-controlled plant factories. In this study, a method based on convolutional neural networks (CNNs) and digital images was applied to estimate the growth of rice seedlings in a controlled environment. Specifically, an end-to-end framework consisting of hybrid CNNs took color images, scaling factor and image acquisition distance as input and directly predicted the shoot height (SH) and shoot fresh weight (SFW) after image segmentation. The results on the rice seedlings dataset collected by different optical sensors demonstrated that the proposed model outperformed compared random forest (RF) and regression CNN models (RCNN). The model achieved R² values of 0.980 and 0.717, and normalized root mean square error (NRMSE) values of 2.64% and 17.23%, respectively. The hybrid CNNs method can learn the relationship between digital images and seedling growth traits, promising to provide a convenient and flexible estimation tool for the non-destructive monitoring of seedling growth in controlled environments.

Consumer reaction to indoor farming using LED lighting technology and the effects of providing information thereon

Indoor vertical farming using artificial light has gained popularity as one solution to food problems. However, prior studies have shown that some consumers have a negative impression that crops are grown in an artificial environment. The increased use of purple Light-Emitting Diode (LED) lighting, which would make the growing environment look more artificial, may exacerbate that negative perception, leading to low acceptance of vertically farmed produce. Given that consumers are increasingly seeing indoor vertical farming directly, for example, in supermarkets and office buildings, it is important to understand how they perceive the use of purple LED lighting to grow crops and whether these perceptions can be improved by learning more about the scientific basis for artificial light cultivation. This study aimed to determine whether purple LED lighting reduces consumers' perceptions of indoor vertical farming compared to traditional white lighting, and to examine whether providing information on plant growth and artificial light changes those perceptions. We administered a web-based questionnaire to 961 Japanese respondents, and analyzed the response data using analysis of variance and an ordered probit model to explore the factors that define the likability for indoor vertical farming. The results revealed that the color of LED lighting had a limited influence on consumers' perceptions of indoor vertical farming, whereas explaining the principle of plant growth under artificial light improves their perceptions. Additionally, personal factors, such as resistance to novel food technology, trust in food safety, and awareness of indoor vertical farming, had a significant impact on the perceptions. It is crucial to expand opportunities for people to interact with artificial light cultivation and disseminate information about its scientific mechanisms.

Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory

Due to their rapid development and wide application in modern agriculture, robots, mobile terminals, and intelligent devices have become vital technologies and fundamental research topics for the development of intelligent and precision agriculture. Accurate and efficient target detection technology is required for mobile inspection terminals, picking robots, and intelligent sorting equipment in tomato production and management in plant factories. However, due to the limitations of computer power, storage capacity, and the complexity of the plant factory (PF) environment, the precision of small-target detection for tomatoes in real-world applications is inadequate. Therefore, we propose an improved Small MobileNet YOLOv5 (SM-YOLOv5) detection algorithm and model based on YOLOv5 for target detection by tomato-picking robots in plant factories. Firstly, MobileNetV3-Large was used as the backbone network to make the model structure lightweight and improve its running performance. Secondly, a small-target detection layer was added to improve the accuracy of small-target detection for tomatoes. The constructed PF tomato dataset was used for training. Compared with the YOLOv5 baseline model, the mAP of the improved SM-YOLOv5 model was increased by 1.4%, reaching 98.8%. The model size was only 6.33 MB, which was 42.48% that of YOLOv5, and it required only 7.6 GFLOPs, which was half that required by YOLOv5. The experiment showed that the improved SM-YOLOv5 model had a precision of 97.8% and a recall rate of 96.7%. The model is lightweight and has excellent detection performance, and so it can meet the real-time detection requirements of tomato-picking robots in plant factories.

CEA Systems: the Means to Achieve Future Food Security and Environmental Sustainability?

As demand for food production continues to rise, it is clear that in order to meet the challenges of the future in terms of food security and environmental sustainability, radical changes are required throughout all levels of the global food system. Controlled Environment Agriculture (CEA) (a.k.a. indoor farming) has an advantage over conventional farming methods in that production processes can be largely separated from the natural environment, thus, production is less reliant on environmental conditions, and pollution can be better restricted and controlled. While output potential of conventional farming at a global scale is predicted to suffer due to the effects of climate change, technological advancements in this time will drastically improve both the economic and environmental performance of CEA systems. This article summarizes the current understanding and gaps in knowledge surrounding the environmental sustainability of CEA systems, and assesses whether these systems may allow for intensive and fully sustainable agriculture at a global scale. The energy requirements and subsequent carbon footprint of many systems is currently the greatest environmental hurdle to overcome. The lack of economically grown staple crops which make up the majority of calories consumed by humans is also a major limiting factor in the expansion of CEA systems to reduce the environmental impacts of food production at a global scale. This review introduces the concept of Integrated System CEA (ISCEA) in which multiple CEA systems can be deployed in an integrated localized fashion to increase efficiency and reduce environmental impacts of food production. We conclude that it is feasible that with sufficient green energy, that ISCEA systems could largely negate most forms of environmental damage associated with conventional farming at a global scale (e.g., GHGs, deforestation, nitrogen, phosphorus, pesticide use, etc.). However, while there is plenty of research being carried out into improving energy efficiency, renewable energy and crop diversification in CEA systems, the circular economy approach to waste is largely ignored. We recommend that industries begin to investigate how nutrient flows and efficiencies in systems can be better managed to improve the environmental performance of CEA systems of the future.