Sustaining crop production and food security are threatened by a burgeoning world population and adverse environmental conditions. Traditional breeding methods for vegetable crops are time-consuming, laborious, and untargeted, often taking several years to develop new and improved varieties. The challenges faced by a long breeding cycle need to be overcome. The speed breeding (SB) approach is broadly employed in crop breeding, which greatly shortens breeding cycles and facilities plant growth to obtain new, better-adapted crop varieties as quickly as possible. Potential opportunities are offered by SB in plant factories, where optimal photoperiod, light quality, light intensity, temperature, CO2 concentration, and nutrients are precisely manipulated to enhance the growth of horticultural vegetable crops, holding promise to surmount the long-standing problem of lengthy crop breeding cycles. Additionally, integrated with other breeding technologies, such as genome editing, genomic selection, and high-throughput genotyping, SB in plant factories has emerged as a smart and promising platform to hasten generation turnover and enhance the efficiency of breeding in vegetable crops. This review considers the pivotal opportunities and challenges of SB in plant factories, aiming to accelerate plant generation turnover and improve vegetable crops with precision and efficiency.

This study examined how the nutrient flow environment affects lettuce root morphology in hydroponics using multi-omics analysis. The results indicate that increasing the nutrient flow rate initially increased indicators such as fresh root weight, root length, surface area, volume, and average diameter before declining, which mirrors the trend observed for shoot fresh weight. Furthermore, a high-flow environment significantly increased root tissue density. Further analysis using Weighted Gene Co-expression Network Analysis (WGCNA) and Weighted Protein Co-expression Network Analysis (WPCNA) identified modules that were highly correlated with phenotypes and hormones. The analysis revealed a significant enrichment of hormone signal transduction pathways. Differences in the expression of genes and proteins related to hormone synthesis and transduction pathways were observed among the different flow conditions. These findings suggest that nutrient flow may regulate hormone levels and signal transmission by modulating the genes and proteins associated with hormone biosynthesis and signaling pathways, thereby influencing root morphology. These findings should support the development of effective methods for regulating the flow of nutrients in hydroponic contexts.

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.

Anthocyanins, natural pigments known for their vibrant hues and beneficial properties, undergo intricate genetic control. However, red vegetables grown in plant factories frequently exhibit reduced anthocyanin synthesis compared to those in open fields due to factors like inadequate light, temperature, humidity, and nutrient availability. Comprehending these factors is essential for optimizing plant factory environments to enhance anthocyanin synthesis. This review insights the impact of physiological and genetic factors on the production of anthocyanins in red lettuce grown under controlled conditions. Further, we aim to gain a better understanding of the mechanisms involved in both synthesis and degradation of anthocyanins. Moreover, this review summarizes the identified regulators of anthocyanin synthesis in lettuce, addressing the gap in knowledge on controlling anthocyanin production in plant factories, with potential implications for various crops beyond red lettuce.

Medicinal plants (MPs) are valued for their contributions to human health. However, the growing demand for MPs and the concerns regarding their quality and sustainability have prompted the reassessment of conventional production practices. Controlled environment cropping systems, such as vertical farms, offer a transformative approach to MP production. By enabling precise control over environment factors, such as light, carbon dioxide, temperature, humidity, nutrients, and airflow, controlled environments can improve the consistency, concentration, and yield of bioactive phytochemicals in MPs. This review explores the potential of controlled environment systems for enhancing MP production. First, we describe how controlled environments can overcome the limitations of conventional production in improving the quality of MP. Next, we propose strategies based on plant physiology to manipulate environment conditions for enhancing the levels of bioactive compounds in plants. These strategies include improving photosynthetic carbon assimilation, light spectrum signalling, purposeful stress elicitation, and chronoculture. We describe the underlying mechanisms and practical applications of these strategies. Finally, we highlight the major knowledge gaps and challenges that limit the application of controlled environments, and discuss future research directions.

Plant factories offer a promising solution to some of the challenges facing traditional agriculture, allowing for year-round rapid production of plant-derived foods. However, the effects of conditions in plant factories on metabolic nutrients remain to be explored. In this study, we used three rice accessions (KongYu131, HuangHuaZhan, and Kam Sweet Rice) as objectives, which were planted in a plant factory with strict photoperiods that are long-day (12 h light/12 h dark) or short-day (8 h light/16 h dark). A total of 438 metabolites were detected in the harvested rice grains. The difference in photoperiod leads to a different accumulation of metabolites in rice grains. Most metabolites accumulated significantly higher levels under the short-day condition than the long-day condition. Differentially accumulated metabolites were enriched in the amino acids and vitamin B6 pathway. Asparagine, pyridoxamine, and pyridoxine are key metabolites that accumulate at higher levels in rice grains harvested from the short-day photoperiod. This study reveals the photoperiod-dependent metabolomic differences in rice cultivated in plant factories, especially the metabolic profiling of taste- and nutrition-related compounds.

While it is commonly understood that air temperature can greatly affect the process of photosynthesis and the growth of higher plants, the impact of root zone temperature (RZT) on plant growth, metabolism, essential elements, as well as key metabolites like chlorophyll and carotenoids, remains an area that necessitates extensive research. Therefore, this study aimed to investigate the impact of raising the RZT on the growth, metabolites, elements, and proteins of red leaf lettuce. Lettuce was hydroponically grown in a plant factory with artificial light at four different air temperatures (17, 22, 27, and 30°C) and two treatments with different RZTs. The RZT was raised 3°C above the air temperature in one group, while it was not in the other group. Increasing the RZT 3°C above the air temperature improved plant growth and metabolites, including carotenoids, ascorbic acids, and chlorophyll, in all four air temperature treatments. Moreover, raising the RZT increased Mg, K, Fe, Cu, Se, Rb, amino acids, and total soluble proteins in the leaf tissue at all four air temperatures. These results showed that raising the RZT by 3°C improved plant productivity and the metabolites of the hydroponic lettuce by enhancing nutrient uptake and activating the metabolism in the roots at all four air temperatures. Overall, this research demonstrates that plant growth and metabolites can be improved simultaneously with an increased RZT relative to air temperature. This study serves as a foundation for future research on optimizing RZT in relation to air temperature. Further recommended studies include investigating the differential effects of multiple RZT variations relative to air temperature for increased optimization, examining the effects of RZT during nighttime versus daytime, and exploring the impact of stem heating. This research has the potential to make a valuable contribution to the ongoing growth and progress of the plant factory industry and fundamental advancements in root zone physiology. Overall, this research demonstrates that plant growth and metabolites can be improved simultaneously with an increased RZT relative to air temperature. This study serves as a foundation for future research on optimizing RZT in relation to air temperature. Further recommended studies include investigating the differential effects of multiple RZT variations relative to air temperature for increased optimization, examining the effects of RZT during nighttime versus daytime, and exploring the impact of stem heating. This research has the potential to make a valuable contribution to the ongoing growth and progress of the plant factory industry and fundamental advancements in root zone physiology.

Unable to move on their own, plants have acquired the ability to produce a wide variety of low molecular weight compounds to survive against various stresses. It is estimated that there are as many as one million different kinds. Plants also have the ability to accumulate high levels of proteins. Although plant-based bioproduction has traditionally relied on classical tissue culture methods, the attraction of bioproduction by plants is increasing with the development of omics and bioinformatics and other various technologies, as well as synthetic biology. This review describes the current status and prospects of these plant-based bioproduction from five advanced research topics, (i) de novo production of plant-derived high value terpenoids in engineered yeast, (ii) biotransformation of plant-based materials, (iii) genome editing technology for plant-based bioproduction, (iv) environmental effect of metabolite production in plant factory, and (v) molecular pharming.

In blueberry (Vaccinium corymbosum L.), a perennial shrub, flower bud initiation is mediated by a short-day (SD) photoperiod and buds bloom once the chilling requirement is satisfied. A plant factory with artificial lighting (PFAL) is a planting system that can provide a stable and highly efficient growing environment for blueberry production. However, the characteristics of bud differentiation of blueberry plants inside PFAL systems are poorly understood. To better understand flower bud initiation and the flowering mechanism of blueberry in PFAL systems, the anatomical structure of apical buds under SD conditions in a PFAL system was observed using the southern highbush cultivar \'Misty\' and a transcriptomic analysis was performed to identify the candidate flowering genes. The results indicated that the apical bud of \'Misty\' differentiated gradually along with SD time course and swelled obviously when chilling was introduced. A total of 39.28 Gb clean data were generated, and about 20.31-24.11 Mb high-quality clean reads were assembled, yielding a total of 17370 differentially expressed genes (DEGs), of which 9637 were up-regulated and 7733 were down-regulated. Based on the functional annotation, 26 DEGs were identified including 20 flowering-related and 6 low-temperature DEGs, out of which the expressive level of four flowering-related DEGs (VcFT2, VcFPA, VcFMADS1, and VcCOP1) and two low-temperature-induced DEGs (VcTIL-1 and VcLTI 65-like) were confirmed by qRT-PCR with a good consistency with the pattern of transcriptome. Functional analysis indicated that VcFT2 was highly conserved with nuclear and cytoplasmic subcellular localization and was expressed mainly in blueberry leaf tissue. In Arabidopsis, ectopic overexpression of VcFT2 results in an early flowering phenotype, indicating that VcFT2 is a vital regulator of the SD-mediated flowering pathway in blueberry. These results contribute to the investigation of photoperiod-mediated flowering mechanisms of blueberry in PFAL systems.

Simultaneous Localization and Mapping (SLAM), as one of the core technologies in intelligent robotics, has gained substantial attention in recent years. Addressing the limitations of SLAM systems in dynamic environments, this research proposes a system specifically designed for plant factory transportation environments, named GY-SLAM. GY-SLAM incorporates a lightweight target detection network, GY, based on YOLOv5, which utilizes GhostNet as the backbone network. This integration is further enhanced with CoordConv coordinate convolution, CARAFE up-sampling operators, and the SE attention mechanism, leading to simultaneous improvements in detection accuracy and model complexity reduction. While mAP@0.5 increased by 0.514% to 95.364, the model simultaneously reduced the number of parameters by 43.976%, computational cost by 46.488%, and model size by 41.752%. Additionally, the system constructs pure static octree maps and grid maps. Tests conducted on the TUM dataset and a proprietary dataset demonstrate that GY-SLAM significantly outperforms ORB-SLAM3 in dynamic scenarios in terms of system localization accuracy and robustness. It shows a remarkable 92.59% improvement in RMSE for Absolute Trajectory Error (ATE), along with a 93.11% improvement in RMSE for the translational drift of Relative Pose Error (RPE) and a 92.89% improvement in RMSE for the rotational drift of RPE. Compared to YOLOv5s, the GY model brings a 41.5944% improvement in detection speed and a 17.7975% increase in SLAM operation speed to the system, indicating strong competitiveness and real-time capabilities. These results validate the effectiveness of GY-SLAM in dynamic environments and provide substantial support for the automation of logistics tasks by robots in specific contexts.