Minimizing Escherichia coli O157:H7 contamination in indoor farming: effects of cultivar type and ultra-violet light quality.

BACKGROUND: Bacterial contamination of produce is a concern in indoor farming due to close plant spacing, recycling irrigation, warm temperatures, and high relative humidity during production. Cultivars that inherently resist contamination and photo-sanitization using ultraviolet (UV) radiation during the production phase can reduce bacterial contamination. However, there is limited information to support their use in indoor farming. RESULTS: Lettuce (Lactuca sativa) cultivars with varying plant architectures grown in a custom-built indoor farm exhibited differences in E. coli O157:H7 survival after inoculation. The survival of E. coli O157:H7 was lowest in the leaf cultivar (open architecture) and highest in the romaine and oakleaf cultivars (compact architecture). Of the different UV wavelengths that were tested (UV-A, UV-A + B, UV-A + C), UV A + C at an intensity of 54.5 µmol m-2 s-1 (with 3.5 µmol m-2 s-1 of UV-C), provided for 15 min every day, was found to be most efficacious in reducing the E. coli O157:H7 survival on romaine lettuce with no negative effects on plant growth and quality. CONCLUSION: Contamination of E. coli O157:H7 on lettuce plants can be reduced and the food safety levels in indoor farms can be increased by selecting cultivars with an open leaf architecture coupled with photo-sanitization using low and frequent exposure to UV A + C radiation. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Detection of Defective Lettuce Seedlings Grown in an Indoor Environment under Different Lighting Conditions Using Deep Learning Algorithms.

Sorting seedlings is laborious and requires attention to identify damage. Separating healthy seedlings from damaged or defective seedlings is a critical task in indoor farming systems. However, sorting seedlings manually can be challenging and time-consuming, particularly under complex lighting conditions. Different indoor lighting conditions can affect the visual appearance of the seedlings, making it difficult for human operators to accurately identify and sort the seedlings consistently. Therefore, the objective of this study was to develop a defective-lettuce-seedling-detection system under different indoor cultivation lighting systems using deep learning algorithms to automate the seedling sorting process. The seedling images were captured under different indoor lighting conditions, including white, blue, and red. The detection approach utilized and compared several deep learning algorithms, specifically CenterNet, YOLOv5, YOLOv7, and faster R-CNN to detect defective seedlings in indoor farming environments. The results demonstrated that the mean average precision (mAP) of YOLOv7 (97.2%) was the highest and could accurately detect defective lettuce seedlings compared to CenterNet (82.8%), YOLOv5 (96.5%), and faster R-CNN (88.6%). In terms of detection under different light variables, YOLOv7 also showed the highest detection rate under white and red/blue/white lighting. Overall, the detection of defective lettuce seedlings by YOLOv7 shows great potential for introducing automated seedling-sorting systems and classification under actual indoor farming conditions. Defective-seedling-detection can improve the efficiency of seedling-management operations in indoor farming.

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.

Lettuce Production in Intelligent Greenhouses-3D Imaging and Computer Vision for Plant Spacing Decisions.

Recent studies indicate that food demand will increase by 35-56% over the period 2010-2050 due to population increase, economic development, and urbanization. Greenhouse systems allow for the sustainable intensification of food production with demonstrated high crop production per cultivation area. Breakthroughs in resource-efficient fresh food production merging horticultural and AI expertise take place with the international competition "Autonomous Greenhouse Challenge". This paper describes and analyzes the results of the third edition of this competition. The competition's goal is the realization of the highest net profit in fully autonomous lettuce production. Two cultivation cycles were conducted in six high-tech greenhouse compartments with operational greenhouse decision-making realized at a distance and individually by algorithms of international participating teams. Algorithms were developed based on time series sensor data of the greenhouse climate and crop images. High crop yield and quality, short growing cycles, and low use of resources such as energy for heating, electricity for artificial light, and CO2 were decisive in realizing the competition's goal. The results highlight the importance of plant spacing and the moment of harvest decisions in promoting high crop growth rates while optimizing greenhouse occupation and resource use. In this paper, images taken with depth cameras (RealSense) for each greenhouse were used by computer vision algorithms (Deepabv3+ implemented in detectron2 v0.6) in deciding optimum plant spacing and the moment of harvest. The resulting plant height and coverage could be accurately estimated with an R2 of 0.976, and a mIoU of 98.2, respectively. These two traits were used to develop a light loss and harvest indicator to support remote decision-making. The light loss indicator could be used as a decision tool for timely spacing. Several traits were combined for the harvest indicator, ultimately resulting in a fresh weight estimation with a mean absolute error of 22 g. The proposed non-invasively estimated indicators presented in this article are promising traits to be used towards full autonomation of a dynamic commercial lettuce growing environment. Computer vision algorithms act as a catalyst in remote and non-invasive sensing of crop parameters, decisive for automated, objective, standardized, and data-driven decision making. However, spectral indexes describing lettuces growth and larger datasets than the currently accessible are crucial to address existing shortcomings between academic and industrial production systems that have been encountered in this work.

Growth Light Quality Influences Leaf Surface Temperature by Regulating the Rate of Non-Photochemical Quenching Thermal Dissipation and Stomatal Conductance.

Significant efforts have been made to optimise spectrum quality in indoor farming to maximise artificial light utilisation and reduce water loss. For such an improvement, green (G) light supplementation to a red-blue (RB) background was successfully employed in our previous studies to restrict both non-photochemical quenching (NPQ) and stomatal conductance (gs). At the same time, however, the downregulation of NPQ and gs had the opposite influence on leaf temperature (Tleaf). Thus, to determine which factor plays the most prominent role in Tleaf regulation and whether such a response is temporal or permanent, we investigated the correlation between NPQ and gs and, subsequently, Tleaf. To this end, we analysed tomato plants (Solanum lycopersicum L. cv. Malinowy Ozarowski) grown solely under monochromatic LED lamps (435, 520, or 662 nm; 80 µmol m-2 s-1) or a mixed RGB spectrum (1:1:1; 180 µmol m-2 s-1) and simultaneously measured gs and Tleaf with an infrared gas analyser and a thermocouple or an infrared thermal camera (FLIR) during thermal imaging analyses. The results showed that growth light quality significantly modifies Tleaf and that such a response is not temporal. Furthermore, we found that the actual adaxial leaf surface temperature of plants is more closely related to NPQ amplitude, while the temperature of the abaxial surface corresponds to gs.

Detection of Tip-Burn Stress on Lettuce Grown in an Indoor Environment Using Deep Learning Algorithms.

Lettuce grown in indoor farms under fully artificial light is susceptible to a physiological disorder known as tip-burn. A vital factor that controls plant growth in indoor farms is the ability to adjust the growing environment to promote faster crop growth. However, this rapid growth process exacerbates the tip-burn problem, especially for lettuce. This paper presents an automated detection of tip-burn lettuce grown indoors using a deep-learning algorithm based on a one-stage object detector. The tip-burn lettuce images were captured under various light and indoor background conditions (under white, red, and blue LEDs). After augmentation, a total of 2333 images were generated and used for training using three different one-stage detectors, namely, CenterNet, YOLOv4, and YOLOv5. In the training dataset, all the models exhibited a mean average precision (mAP) greater than 80% except for YOLOv4. The most accurate model for detecting tip-burns was YOLOv5, which had the highest mAP of 82.8%. The performance of the trained models was also evaluated on the images taken under different indoor farm light settings, including white, red, and blue LEDs. Again, YOLOv5 was significantly better than CenterNet and YOLOv4. Therefore, detecting tip-burn on lettuce grown in indoor farms under different lighting conditions can be recognized by using deep-learning algorithms with a reliable overall accuracy. Early detection of tip-burn can help growers readjust the lighting and controlled environment parameters to increase the freshness of lettuce grown in plant factories.

Beyond vegetables: effects of indoor LED light on specialized metabolite biosynthesis in medicinal and aromatic plants, edible flowers, and microgreens.

Specialized metabolites from plants are important for human health due to their antioxidant properties. Light is one of the main factors modulating the biosynthesis of specialized metabolites, determining the cascade response activated by photoreceptors and the consequent modulation of expressed genes and biosynthetic pathways. Recent developments in light emitting diode (LED) technology have enabled improvements in artificial light applications for horticulture. In particular, the possibility to select specific spectral light compositions, intensities and photoperiods has been associated with altered metabolite content in a variety of crops. This review aims to analyze the effects of indoor LED lighting recipes and management on the specialized metabolite content in different groups of crop plants (namely medicinal and aromatic plants, microgreens and edible flowers), focusing on the literature from the last 5 years. The literature collection produced a total of 40 papers, which were analyzed according to the effects of artificial LED lighting on the content of anthocyanins, carotenoids, phenols, tocopherols, glycosides, and terpenes, and ranked on a scale of 1 to 3. Most studies applied a combination of red and blue light (22%) or monochromatic blue (23%), with a 16 h day-1 photoperiod (78%) and an intensity greater than 200 µmol m-2  s-1 (77%). These treatment features were often the most efficient in enhancing specialized metabolite content, although large variations in performance were observed, according to the species considered and the compound analyzed. The review aims to provide valuable indications for the definition of the most promising spectral components toward the achievement of nutrient-rich indoor-grown products. © 2021 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Waste nutrient solutions from full-scale open hydroponic cultivation: Dynamics of effluent quality and removal of nitrogen and phosphorus using a pilot-scale sequencing batch reactor.

Hydroponic cultivation is revolutionizing agricultural crop production techniques all over the world owing to its minimal environmental footprint, enhanced pest control, and high crop yield. However, waste nutrient solutions (WNS) generated from hydroponic systems contain high concentrations of N and P; moreover, they are discharged into surface and subsurface environments, leading to eutrophication and subsequent ecosystem degradation. In this study, the nutrient concentrations in WNS from 10 hydroponic indoor tomato, capsicum, and strawberry farms (greenhouses) were monitored for up to six months. The concentrations of N and P in WNS discharged from these farms were 48.0-494.0 mg L-1 and 12.7-96.9 mg L-1, respectively, which exceeded the Korean water quality guidelines (40.0 mg L-1 N and 4.0 mg L-1 P) for effluents. These concentrations were varied and dependent on the supplied nutrient concentrations, crop types, and growth stages. In general, the concentrations of N and P were in the following order: tomato > capsicum > strawberry. High N as NO3- and P as PO43- but low organic C in WNS warrant subsequent treatment before discharge. Therefore, this study tested a pilot-scale sequencing batch reactor (SBR) system as a potential technology for WNS treatment. The SBR system had BOD, COD, nitrate, and phosphate removal efficiency of 100, 100, 89.5, and 99.8%, respectively. In addition, the SBR system removed other cations such as Ca2+, dissolved Fe, K+, Mg2+, and Na+ and the removal efficiencies of those ions were 48, 67, 18, 14 and 15%, respectively. Lower methanol addition (0.63 mg L-1) and extended aeration (~30 min) improved SBR performance efficiency of C, N, and P removal. Thus, SBR showed significant promise as a treatment alternative to WNS pollutants originating from hydroponic systems.

Design, Construction and Testing of IoT Based Automated Indoor Vertical Hydroponics Farming Test-Bed in Qatar.

Growing plants in the gulf region can be challenging as it is mostly desert, and the climate is dry. A few species of plants have the capability to grow in such a climate. However, those plants are not suitable as a food source. The aim of this work is to design and construct an indoor automatic vertical hydroponic system that does not depend on the outside climate. The designed system is capable to grow common type of crops that can be used as a food source inside homes without the need of large space. The design of the system was made after studying different types of vertical hydroponic systems in terms of price, power consumption and suitability to be built as an indoor automated system. A microcontroller was working as a brain of the system, which communicates with different types of sensors to control all the system parameters and to minimize the human intervention. An open internet of things (IoT) platform was used to store and display the system parameters and graphical interface for remote access. The designed system is capable of maintaining healthy growing parameters for the plants with minimal input from the user. The functionality of the overall system was confirmed by evaluating the response from individual system components and monitoring them in the IoT platform. The system was consuming 120.59 and 230.59 kWh respectively without and with air conditioning control during peak summer, which is equivalent to the system running cost of 13.26 and 25.36 Qatari Riyal (QAR) respectively. This system was circulating around 104 k gallons of nutrient solution monthly however, only 8-10 L water was consumed by the system. This system offers real-time notifications to alert the hydroponic system user when the conditions are not favorable. So, the user can monitor several parameters without using laboratory instruments, which will allow to control the entire system remotely. Moreover, the system also provides a wide range of information, which could be essential for plant researchers and provides a greater understanding of how the key parameters of hydroponic system correlate with plant growth. The proposed platform can be used both for quantitatively optimizing the setup of the indoor farming and for automating some of the most labor-intensive maintenance activities. Moreover, such a monitoring system can also potentially be used for high-level decision making, once enough data will be collected. This work presents significant opportunities for the people who live in the gulf region to produce food as per their requirements.

Cherry Tomato Production in Intelligent Greenhouses-Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality.

Greenhouses and indoor farming systems play an important role in providing fresh and nutritious food for the growing global population. Farms are becoming larger and greenhouse growers need to make complex decisions to maximize production and minimize resource use while meeting market requirements. However, highly skilled labor is increasingly lacking in the greenhouse sector. Moreover, extreme events such as the COVID-19 pandemic, can make farms temporarily less accessible. This highlights the need for more autonomous and remote-control strategies for greenhouse production. This paper describes and analyzes the results of the second "Autonomous Greenhouse Challenge". In this challenge, an experiment was conducted in six high-tech greenhouse compartments during a period of six months of cherry tomato growing. The primary goal of the greenhouse operation was to maximize net profit, by controlling the greenhouse climate and crop with AI techniques. Five international teams with backgrounds in AI and horticulture were challenged in a competition to operate their own compartment remotely. They developed intelligent algorithms and use sensor data to determine climate setpoints and crop management strategy. All AI supported teams outperformed a human-operated greenhouse that served as reference. From the results obtained by the teams and from the analysis of the different climate-crop strategies, it was possible to detect challenges and opportunities for the future implementation of remote-control systems in greenhouse production.

Remote Control of Greenhouse Vegetable Production with Artificial Intelligence-Greenhouse Climate, Irrigation, and Crop Production.

The global population is increasing rapidly, together with the demand for healthy fresh food. The greenhouse industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) has reached breakthroughs in several areas, however, not yet in horticulture. An international competition on "autonomous greenhouses" aimed to combine horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams, consisting of scientists, professionals, and students with different backgrounds in horticulture and AI, participated in a greenhouse growing experiment. Each team had a 96 m2 modern greenhouse compartment to grow a cucumber crop remotely during a 4-month-period. Each compartment was equipped with standard actuators (heating, ventilation, screening, lighting, fogging, CO2 supply, water and nutrient supply). Control setpoints were remotely determined by teams using their own AI algorithms. Actuators were operated by a process computer. Different sensors continuously collected measurements. Setpoints and measurements were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a manually operated reference. Detailed results on cucumber yield, resource use, and net profit obtained by teams are explained in this paper. We can conclude that in general AI performed well in controlling a greenhouse. One team outperformed the manually-grown reference.

Light Flux Density and Photoperiod Affect Growth and Secondary Metabolism in Fully Expanded Basil Plants.

Indoor production of basil (Ocimum basilicum L.) is influenced by light spectrum, photosynthetic photon flux density (PPFD), and the photoperiod. To investigate the effects of different lighting on growth, chlorophyll content, and secondary metabolism, basil plants were grown from seedlings to fully expanded plants in microcosm devices under different light conditions: (a) white light at 250 and 380 µmol·m-2·s-1 under 16/8 h light/dark and (b) white light at 380 µmol·m-2·s-1 under 16/8 and 24/0 h light/dark. A higher yield was recorded under 380 µmol·m-2·s-1 compared to 250 µmol·m-2·s-1 (fresh and dry biomasses 260.6 ± 11.3 g vs. 144.9 ± 14.6 g and 34.1 ± 2.6 g vs. 13.2 ± 1.4 g, respectively), but not under longer photoperiods. No differences in plant height and chlorophyll content index were recorded, regardless of the PPFD level and photoperiod length. Almost the same volatile organic compounds (VOCs) were detected under the different lighting treatments, belonging to terpenes, aldehydes, alcohols, esters, and ketones. Linalool, eucalyptol, and eugenol were the main VOCs regardless of the lighting conditions. The multivariate data analysis showed a sharp separation of non-volatile metabolites in apical and middle leaves, but this was not related to different PPFD levels. Higher levels of sesquiterpenes and monoterpenes were detected in plants grown under 250 µmol·m-2·s-1 and 380 µmol·m-2·s-1, respectively. A low separation of non-volatile metabolites based on the photoperiod length and VOC overexpression under longer photoperiods were also highlighted.

Effect of red and blue light versus white light on fruit biomass radiation-use efficiency in dwarf tomatoes.

The effect of the ratio of red and blue light on fruit biomass radiation-use efficiency (FBRUE) in dwarf tomatoes has not been well studied. Additionally, whether white light offers a greater advantage in improving radiation-use efficiency (RUE) and FBRUE over red and blue light under LED light remains unknown. In this study, two dwarf tomato cultivars ('Micro-Tom' and 'Rejina') were cultivated in three red-blue light treatments (monochromatic red light, red/blue light ratio = 9, and red/blue light ratio = 3) and a white light treatment at the same photosynthetic photon flux density of 300 µmol m-2 s-1. The results evidently demonstrated that the red and blue light had an effect on FBRUE by affecting RUE rather than the fraction of dry mass partitioned into fruits (Ffruits). The monochromatic red light increased specific leaf area, reflectance, and transmittance of leaves but decreased the absorptance and photosynthetic rate, ultimately resulting in the lowest RUE, which induced the lowest FBRUE among all treatments. A higher proportion of blue light (up to 25%) led to a higher photosynthetic rate, resulting in a higher RUE and FBRUE in the three red-blue light treatments. Compared with red and blue light, white light increased RUE by 0.09-0.38 g mol-1 and FBRUE by 0.14-0.25 g mol-1. Moreover, white light improved the Ffruits in 'Rejina' and Brix of fruits in 'Micro-Tom' and both effects were cultivar-specific. In conclusion, white light may have greater potential than mixed red and blue light for enhancing the dwarf tomato FBRUE during their reproductive growth stage.

Applications of optical sensing and imaging spectroscopy in indoor farming: A systematic review.

As demand for food continues to rise, innovative methods are needed to sustainably and efficiently meet the growing pressure on agriculture. Indoor farming and controlled environment agriculture have emerged as promising approaches to address this challenge. However, optimizing fertilizer usage, ensuring homogeneous production, and reducing agro-waste remain substantial challenges in these production systems. One potential solution is the use of optical sensing technology, which can provide real-time data to help growers make informed decisions and enhance their operations. optical sensing can be used to analyze plant tissues, evaluate crop quality and yield, measure nutrients, and assess plant responses to stress. This paper presents a systematic literature review of the current state of using spectral-optical sensors and hyperspectral imaging for indoor farming, following the PRISMA 2020 guidelines. The study surveyed existing studies from 2017 to 2023 to identify gaps in knowledge, provide researchers and farmers with current trends, and offer recommendations and inspirations for possible new research directions. The results of this review will contribute to the development of sustainable and efficient methods of food production.

Development of a machine vision-based weight prediction system of butterhead lettuce (Lactuca sativa L.) using deep learning models for industrial plant factory.

Introduction: Indoor agriculture, especially plant factories, becomes essential because of the advantages of cultivating crops yearly to address global food shortages. Plant factories have been growing in scale as commercialized. Developing an on-site system that estimates the fresh weight of crops non-destructively for decision-making on harvest time is necessary to maximize yield and profits. However, a multi-layer growing environment with on-site workers is too confined and crowded to develop a high-performance system.This research developed a machine vision-based fresh weight estimation system to monitor crops from the transplant stage to harvest with less physical labor in an on-site industrial plant factory. Methods: A linear motion guide with a camera rail moving in both the x-axis and y-axis directions was produced and mounted on a cultivating rack with a height under 35 cm to get consistent images of crops from the top view. Raspberry Pi4 controlled its operation to capture images automatically every hour. The fresh weight was manually measured eleven times for four months to use as the ground-truth weight of the models. The attained images were preprocessed and used to develop weight prediction models based on manual and automatic feature extraction. Results and discussion: The performance of models was compared, and the best performance among them was the automatic feature extraction-based model using convolutional neural networks (CNN; ResNet18). The CNN-based model on automatic feature extraction from images performed much better than any other manual feature extraction-based models with 0.95 of the coefficients of determination (R2) and 8.06 g of root mean square error (RMSE). However, another multiplayer perceptron model (MLP_2) was more appropriate to be adopted on-site since it showed around nine times faster inference time than CNN with a little less R2 (0.93). Through this study, field workers in a confined indoor farming environment can measure the fresh weight of crops non-destructively and easily. In addition, it would help to decide when to harvest on the spot.

Between eustress and distress: UVB induced changes in carotenoid accumulation in halophytic Salicornia europaea.

Halophytes are potential future crops with a valuable nutritional profile. Produced in indoor farming, they are considered to contribute to sustainable and resilient food systems. Indoor farms operate using artificial light. In this context narrowband and low dose UVB radiation can be used to increase plant secondary metabolites, such as carotenoids, and provide an improved nutritional profile for a human diet. UVB radiation can cause eustress or distress in the plant depending on the lighting situation. The aim of this study was to identify the doses of UVB that lead to either eustress or distress and to analyze these responses in Salicornia europaea. Therefore, S. europaea plants were exposed to different UVB radiation levels, low, medium and high, and analyzed for reactive oxygen species (ROS), plant hormones, amino acids, and photosynthetic pigments. High UVB treatment was found to affect phenotype and growth, and the metabolite profile was affected in a UVB dose-dependent manner. Specifically, medium UVB radiation resulted in an increase in carotenoids, whereas high UVB resulted in a decrease. We also observed an altered oxidative stress status and increased SA and decreased ABA contents in response to UVB treatment. This was supported by the results of menadione treatment that induces oxidative stress in plants, which also indicated an altered oxidative stress status in combination with altered carotenoid content. Thus, we show that a moderate dose of UVB can increase the carotenoid content of S. europaea. Furthermore, the UVB stress-dependent response led to a better understanding of carotenoid accumulation upon UVB exposure, which can be used to improve lighting systems and in turn the nutritional profile of future crops in indoor farming.

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.

Effects of White and Blue-Red Light on Growth and Metabolism of Basil Grown under Microcosm Conditions.

Indoor farming of basil (Ocimum basilicum L.) under artificial lighting to support year-round produce demand is an area of increasing interest. Literature data indicate that diverse light regimes differently affect downstream metabolic pathways which influence basil growth, development and metabolism. In this study, basil was grown from seedlings to fully developed plants in a microcosm, an innovative device aimed at growing plants indoor as in natural conditions. Specifically, the effects of white (W) and blue-red (BR) light under a photosynthetic photon flux density of 255 µmol m-2 s-1 on plant growth, photochemistry, soluble nutrient concentration and secondary metabolism were investigated. Plants grew taller (41.8 ± 5.0 vs. 28.4 ± 2.5 cm) and produced greater biomass (150.3 ± 24.2/14.7 ± 2.0 g vs. 116.2 ± 28.3/12.3 ± 2.5 g fresh/dry biomass) under W light compared to BR light. The two lighting conditions differently influenced the soluble nutrient concentration and the translocation rate. No photosynthetic stress was observed under the two lighting regimes, but leaves grown under W light displayed higher levels of maximum quantum yield of PSII and electron transport rate. Sharp differences in metabolic patterns under the two lighting regimes were detected with higher concentrations of phenolic compounds under the BR light.

Photosynthetic photon flux density affects fruit biomass radiation-use efficiency of dwarf tomatoes under LED light at the reproductive growth stage.

This study aimed to analyze the effects of photosynthetic photon flux density (PPFD) on fruit biomass radiation-use efficiency (FBRUE) of the dwarf tomato cultivar 'Micro-Tom' and to determine the suitable PPFD for enhancing the FBRUE under LED light at the reproductive growth stage. We performed four PPFD treatments under white LED light: 200, 300, 500, and 700 µmol m-2 s-1. The results demonstrated that a higher PPFD led to higher fresh and dry weights of the plants and lowered specific leaf areas. FBRUE and radiation-use efficiency (RUE) were the highest under 300 µmol m-2 s-1. FBRUE decreased by 37.7% because RUE decreased by 25% and the fraction of dry mass portioned to fruits decreased by 16.9% when PPFD increased from 300 to 700 µmol m-2 s-1. Higher PPFD (500 and 700 µmol m-2 s-1) led to lower RUE owing to lower light absorptance, photosynthetic quantum yield, and photosynthetic capacity of the leaves. High source strength and low fruit sink strength at the late reproductive growth stage led to a low fraction of dry mass portioned to fruits. In conclusion, 300 µmol m-2 s-1 PPFD is recommended for 'Micro-Tom' cultivation to improve the FBRUE at the reproductive growth stage.

Identification and Characterization of Mercury Contamination in Vegetables and Herbs Cultivated on a Commercial Vertical Indoor Farming System with Light-Emitting Diode Lighting: Unveiling an Unusual Food Safety Risk of Some Improperly Manufactured High-Density Agricultural Production Systems.

Artificial grow lights, such as light-emitting diodes (LEDs) and fluorescent grow lights, are commonly used in modern day indoor farming, citing advantages in energy efficiency and a higher controlled environment. However, the use of LEDs poses a risk in mercury contaminations as a result of its production process, specifically LEDs with polyurethane encapsulates that were traditionally produced using mercury resins as a catalyst. A total of 10.0 ppm of mercury was detected in a curly kale sample harvested from an indoor hydroponic vegetable farm, exceeding Singapore Food Regulation's limit of 0.05 ppm. Vegetables, farming inputs, and surface swabs from the affected farm were analyzed using wet acid digestion followed by cold vapor atomic absorption spectroscopy analysis. The investigation found high concentrations of mercury in the LED encapsulant, and the encapsulant material was identified to be polyurethane by Fourier transform infrared spectroscopy and pyrolysis-gas chromatography-mass spectrometry analysis, indicating the source of mercury contamination to be the LED polyurethane encapsulant.