Research revealed that the abaxial leafy supplemental lighting (AB) can significantly improve the net photosynthetic rate and stomatal conductance in the leaves of tomato plants compare to the adaxial leafy supplemental lighting (AD) method. However, the underlying regulatory mechanisms are still poorly understood. Here, we conducted AB and AD on tomato and assessed transcriptomic, and proteomic changes in leaves. The result showed that under the two supplemental lighting methods, a total of 7352 genes and 152 proteins were differentially expressed. Significant differences were observed in genes expression levels and proteins abundances across multiple pathways, mainly including cell process, metabolism process, biological regulation, environment information processing, genetic information processing, metabolism, and organismal systems. Additionally, we also found that some key genes that plant hormone signaling, light perception, photosynthesis, plant fitness, and promoting fruit ripening, have increased significantly, which can explain the effect of AB on plant growth and development. Finally, through the qPCR, we determined that AB mainly up-regulate a series of auxin-responsive genes or factors, auxin polarity transport genes, gibberellin synthesis genes, cell cycle regulator genes, sugar transporters, and fleshy fruit ripening genes. These results help us to understand plant light response mechanism and discover genes which contribute to efficient light energy utilization.

Plant seedlings are sensitive to cultivation environment factors and highly susceptible to pathogenic infections under adverse conditions such as inappropriate light environment. In this study, five kinds of LED lighting sources with different red (R) and blue (B) light combinations were set up: R10B0, R7B3, R5B5, R2B8 and R0B10 (with R:B ratios of 10:0, 7:3, 5:5, 2:8 and 0:10, respectively) to explore their effects on tomato seedlings’ growth, AsA-GSH cycle, endogenous hormones, and resistance to Phytophthora infestans, providing a basis for factory seedling light-quality selection. The results showed that with the increase in the proportion of blue light in the composite light, the growth indicators, photosynthetic characteristic parameters and enzyme activities of tomato seedlings generally increased. The contents of AsA, reduced glutathione, and oxidized glutathione all reached the maximum under high-proportion blue-light treatments (R2B8 and R0B10). The high-blue-light groups (R2B8 and R0B10) had the highest AsA and glutathione contents. The red–blue combinations reduced inhibitory ABA and increased growth-promoting hormones (e.g., melatonin), while monochromatic light increased ABA to inhibit growth. After inoculation with P. infestans, the apoplastic glucose content was the highest under the red–blue-combined treatments (R5B5 and R2B8), while the total glucose content in leaves was the highest under the combined light R2B8 treatment. In conclusion, high-proportion blue-light treatment can greatly promote the photosynthetic process of tomato, enhance the AsA-GSH cycle, and achieve the best effect in improving the resistance of tomatoes to P. infestans. Given these, the optimal light environment setting was R:B = 2:8.

Kiwifruit bacterial canker, caused by Pseudomonas syringae pv. actinidiae (PSA), poses a grave threat to the global kiwifruit industry. In this study, we examined the role of microRNAs (miRNAs) in kiwifruit’s response to PSA. Kiwifruit seedlings subjected to PSA treatment showed significant changes in both miRNA and gene expression compared to the control group. We identified 364 differentially expressed miRNAs (DEMs) and 7170 differentially expressed genes (DEGs). Further analysis revealed 180 miRNAs negatively regulating 641 mRNAs. Notably, two miRNAs from the miRNA482 family, miRNA-215-3p and miRNA-29-3p, were found to increase kiwifruit’s sensitivity to PSA when overexpressed. These miRNAs were linked to the regulation of NBS-LRR target genes, shedding light on their role in kiwifruit’s defence against PSA. This study offers insights into the miRNA482-NBS-LRR network as a crucial component in enhancing kiwifruit bioresistance to PSA infestation and provides promising candidate genes for further research.

With the development of modern agricultural technology, plant factories have become an important way to achieve efficient and sustainable crop production. Accurate understanding of the light received by plants is the key to improving the light energy utilization efficiency of lamps and ensuring the benefits of plant factories. Ray tracing technology, as one of the key technologies in plant factories, is of great significance to analyze the growing light environment of vegetables. Spinach has high nutritional value and is loved by the public and is one of the main crops grown in plant factories. In this paper, LightTools, TracePro, and Ansys Lumerical FDTD Solution, which are currently mature light environment tracking software in the field of lighting, are selected as the research objects to investigate their performance in simulating the light environment of spinach leaf surfaces under different planting arrangements and different lamp source distances. The results show as follows: Under the rectangular planting arrangement, the leaves received more light, and the plants grew faster. Different planting arrangements of plants had little effect on the simulation effect of the same kind of software, but the simulation effect of the three kinds of software under the same planting arrangement was significantly different, and the difference between the simulated value and the measured value of TracePro was the least. Further, TracePro was used to trace and simulate the leaf surface light conditions of spinach under a rectangular planting arrangement at different lighting distances, and the simulation results showed that there was no significant difference between the software simulation value and the measured value, and the simulation accuracy was the highest when the distance from the light source was 30 cm. Therefore, TracePro software can accurately simulate the light intensity of spinach leaves during the growth process and is most suitable for monitoring the change of light environment of spinach growth in plant factories.

An accurate gas utilization model is essential for precisely detecting plant photosynthetic capacity. Existing equipment for measuring the plant photosynthetic rate typically considers the key parameters of mesophyll cell conductance and a photosynthetic model based on the carbon reaction process under direct light conditions. However, the light environment signals received by the plant canopy not only vary significantly in incidence angles, but the effective light intensity also differs greatly from the measured values under vertical incidence conditions. To reduce the deviation between existing photosynthetic models and the actual photosynthetic efficiency of leaves, this study employs the gas diffusion method from engineering, using the finite element approach. Based on elastic mechanics and seepage mechanics, the internal stress field control equation of tomato leaves and the two-phase flow equation under a CO₂ porous medium were derived. A mathematical model of porous gas–liquid two-phase fluid-solid coupling was established, solved, and analyzed. Preliminary verification was conducted through tests. The results show that in the initial stage of CO₂ entering the leaf, the gas flow velocity is higher because of the larger pressure gradient between the pore and the leaf. In this stage, the gas diffusion rate is higher. As the intake time increases, the pressure gradient gradually decreases, and the inlet velocity slows down. Consequently, the diffusion rate gradually reduces. Because of the coupling of light quantity and light direction, the gas diffusion rate significantly increases compared with the uncoupled model. Additionally, a diffusion model that does not consider fluid–solid coupling will overestimate the gas flow rate as the depth of gas entry increases. Therefore, the internal gas diffusion model must account for the effect of coupling on the diffusion rate.