Excessive accumulation of nitrates in vegetables is a common issue that poses a potential threat to human health. The absorption, translocation, and assimilation of nitrates in vegetables are tightly regulated by the interaction of internal cues (expression of related genes and enzyme activities) and external environmental factors. In addition to global food security, food nutritional quality is recognized as being of strategic importance by most governments and other agencies. Therefore, the identification and development of sustainable, innovative, and inexpensive approaches for increasing vegetable production and concomitantly reducing nitrate concentration are extremely important. Under controlled environmental conditions, optimal fertilizer/nutrient element management and environmental regulation play vital roles in producing vegetables with low nitrate content. In this review, we present some of the recent findings concerning the effects of environmental factors (e.g., light, temperature, and CO2) and fertilizer/nutrient solution management strategies on nitrate reduction in vegetables grown under controlled environments and discuss the possible molecular mechanisms. We also highlight several perspectives for future research to optimize the yield and nutrition quality of leafy vegetables grown in controlled environments.

Light supplementation can increase crop yield in greenhouses by promoting photosynthesis and plant growth. However, the high energy costs associated with light supplementation are a predominant factor that limits development and profit improvement of controlled environment agriculture. Light-emitting diodes (LEDs) are a promising technology that has tremendous potential to improve irradiance efficiency and to replace traditionally used horticultural lighting. Compared with traditional light sources (e.g., high-pressure sodium lamps and metal halide lamps) used in crop production, LEDs have distinct advantages, such as their small size, long lifetime and high photoelectric conversion efficiency. Most importantly, as a monochromatic light source, the spectrum of LEDs can be adjusted based on plant growth requirements. This project aimed to investigate energy-use efficiency, vegetable nutrition and photosynthesis improvement of light supplementation in a protected horticulture system. In the initial phase, the effects of LED light on plant growth and light-use efficiency for pak choi and photosynthetic performance were investigated. The results showed that the highest fresh and dry weight and leaf area were observed under red and blue LED light, with the blue light percentage at 23%. Compared with fluorescent lamps (FL) with photosynthetic photon flux density (PPFD) at 220 μmol m -2 s -1 , the light-use efficiency increased by 55, 114 and 115% for mixed red and blue LEDs with PPFD at 100, 150 and 220 μmol m -2 s -1 , respectively. Monochromatic red-and blue-light LEDs resulted in significant decreases in P n of tomato plants, but the stomatal conductance (G s ) for monochromatic blue LEDs was higher than that for FL. The effect of light spectrum composition on lettuce nutrition quality was also studied. Continuous light with combined red, green and blue LEDs exhibited a remarkable decrease in nitrate. Moreover, continuous LED light for 24 h significantly increased phenolic compound content and free-radical scavenging capacity in lettuce leaf.

Light plays a pivotal role in plant growth, development, and stress responses. Green light has been reported to enhance plant drought tolerance via stomatal regulation. However, the mechanisms of green light-induced drought tolerance in plants remain elusive. To uncover those mechanisms, we investigated the molecular responses of tomato plants under monochromatic red, blue, and green light spectrum with drought and well-water conditions using a comparative transcriptomic approach. The results showed that compared with monochromatic red and blue light treated plants, green light alleviated the drought-induced inhibition of plant growth and photosynthetic capacity, and induced lower stomatal aperture and higher ABA accumulation in tomato leaves after 9 days of drought stress. A total of 3,850 differentially expressed genes (DEGs) was identified in tomato leaves through pairwise comparisons. Functional annotations revealed that those DEGs responses to green light under drought stress were enriched in plant hormone signal transduction, phototransduction, and calcium signaling pathway. The DEGs involved in ABA synthesis and ABA signal transduction both participated in the green light-induced drought tolerance of tomato plants. Compared with ABA signal transduction, more DEGs related to ABA synthesis were detected under different light spectral treatments. The bZIP transcription factor- HY5 was found to play a vital role in green light-induced drought responses. Furthermore, other transcription factors, including WRKY46 and WRKY81 might participate in the regulation of stomatal aperture and ABA accumulation under green light. Taken together, the results of this study might expand our understanding of green light-modulated tomato drought tolerance via regulating ABA accumulation and stomatal aperture.

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Understanding how plants respond to environmental conditions such as temperature, CO2, humidity, and light radiation is essential for plant growth. This paper proposes an Artificial Neural Network (ANN) model to predict plant response to environmental conditions to enhance crop production systems that improve plant performance and resource use efficiency (e.g. light, fertiliser and water) in a Chinese Solar Greenhouse. Comprehensive data collection has been conducted in a greenhouse environment to validate the proposed prediction model. Specifically, the data has been collected from the CSG in warm and cold weather. This paper confirms that CSG’s passive insulation and heating system was effective in providing adequate protection during the winter. In particular, the CSG average indoor temperature was 18 $$^{\circ }$$ ∘ C higher than the outdoor temperature. The difference in environmental conditions led to a yield of 320.8g per head in the winter after 60 growing days compared to 258.9g in the spring experiment after just 35 days. Three different architectures of Bayesian Neural Networks (BNN) models have been evaluated to predict plant response to environmental conditions. The results show that the BNN network is accurate in modelling and predicting crop performance.