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Rosmarinic acid (RA) is a phenolic antioxidant naturally occurring in the plants of the Lamiaceae family, including basil (Ocimum basilicum L.). Existing analytical methods for determining the RA content in leaves are time-consuming and destructive, posing limitations on quality assessment and control during cultivation. In this study, we aimed to develop non-destructive prediction models for the RA content in basil plants using a portable hyperspectral imaging (HSI) system and machine learning algorithms. The basil plants were grown in a vertical farm module with controlled environments, and the HSI of the whole plant was captured using a portable HSI camera in the range of 400–850 nm. The average spectra were extracted from the segmented regions of the plants. We employed several spectral data pre-processing methods and ensemble learning algorithms, such as Random Forest, AdaBoost, XGBoost, and LightGBM, to develop the RA prediction model and feature selection based on feature importance. The best RA prediction model was the LightGBM model with feature selection by the AdaBoost algorithm and spectral pre-processing through logarithmic transformation and second derivative. This model performed satisfactorily for practical screening with R2P = 0.81 and RMSEP = 3.92. From in-field HSI data, the developed model successfully estimated and visualized the RA distribution in basil plants growing in the greenhouse. Our findings demonstrate the potential use of a portable HSI system for monitoring and controlling pharmaceutical quality in medicinal plants during cultivation. This non-destructive and rapid method can provide a valuable tool for assessing the quality of RA in basil plants, thereby enhancing the efficiency and accuracy of quality control during the cultivation stage.

The growing interest in healthy diets has driven the demand for food ingredients with enhanced health benefits. In this study, we aimed to explore a method to enhance the bioactivity of kale using a home vertical farming appliance. Specifically, we investigated the effects of treating kale with a green tea water extract (GTE; 0.1–0.5 g/L in nutrient solution) for two weeks before harvest during five weeks of kale cultivation. GTE treatment did not negatively affect the key quality attributes, such as yield, semblance, or sensory properties. However, it led to the accumulation of bioactive compounds, epicatechin (EC) and epigallocatechin gallate (EGCG), which are typically absent in kale. In the control group, no catechins were detected, whereas in the GTE-treated group, the concentration of EC and EGCG were as high as 252.11 and 173.26 μg/g, respectively. These findings indicate the successful incorporation of catechins, known for their unique health-promoting properties, into kale. Additionally, GTE treatment enhanced the biosynthesis of glucosinolates, which are key secondary metabolites of kale. The total glucosinolate content increased from 9.56 μmol/g in the control group to 16.81 μmol/g in the GTE-treated group (treated with 0.5 g/L GTE). These findings showed that GTE treatment not only enriched kale with catechins, the primary bioactive compounds in green tea but also increased the levels of glucosinolates. This study, conducted using a home vertical farming appliance, suggests that bioactivity-enhanced kale can be grown domestically, providing consumers with a nutrient-fortified food source.

Artemisia annua L. is a well-known therapeutic herb that is widely used in folk medicine in Asian and African countries. A. annua can alleviate fever, wounds, and inflammation and is also popular as an anti-malarial agent. Cu and Zn are essential nutrients for human wellness and are vital to plants; they sometimes act as elicitors and induce stress mechanisms in plants to stimulate the production of secondary metabolites, which have bioactivities. Therefore, we added Cu or Zn to a nutrient solution and cultivated A. annua to enhance the Cu or Zn content. The Cu or Zn treatment during A. annua cultivation elevated their accumulation, and Zn showed a dramatic accumulation level in harvests. The aerial part of Zn16X contained 35 times higher Zn content than that of the control. Although the Cu or Zn contents were elevated, the plant height and yield were not affected, indicating the absence of toxic effects. The Cu or Zn treatment decreased the artemisinin content; however, these treatments increased the amounts of phenolic acids and flavonoids in A. annua. In particular, Zn4X showed a notable increase in the phenolic acids and flavonoids amounts. Moreover, the contents of certain types of caffeoylquinic acids were also highly elevated in Zn4X. Overall, Cu or Zn treatment in A. annua increased Cu or Zn accumulation and stimulated phenolic acid and flavonoid synthesis, which may have enhanced the therapeutic efficacy of A. annua.

Many studies have examined how various cultivation methods and elicitors elevate target crop values. Post-harvest treatments, such as the drying method, may alter the final product’s characteristics. We investigated the effect of nutrient solution concentration during the hydroponic cultivation and drying method (cold air drying and freeze drying) after harvesting Agastache rugosa Kuntze, an herb used for folk medicine and as a deodorant, spice, and tea in East Asia. To explore the properties of A. rugosa, we determined its growth parameters, secondary metabolites, and photoprotective effects. We observed the highest growth and yields in A. rugosa grown under 4.0 dS·m−1 electrical conductivity (EC) conditions. However, the EC 2.0 group showed higher antioxidative properties than those grown in EC 1.5 and 4.0 conditions. Drying conditions varied the content and ratios of major active compounds (rosmarinic acid, tilianin, and acacetin) in A. rugosa. Cold air drying caused rosmarinic acid transmutation to tilianin and acacetin, and tilianin showed a stronger positive correlation with antioxidative and photoprotective activities than rosmarinic acid. The increased tilianin content in cold-air-dried A. rugosa accompanied the improved photoprotective effects in dermal cell lines. This study reveals the effects of cultivation and drying methods in A. rugosa production, and proposes alternate industrial production techniques.

The integration of hyperspectral imaging with machine learning algorithms has presented a promising strategy for the non-invasive and rapid detection of plant metabolites. For this study, we developed prediction models using partial least squares regression (PLSR) and boosting algo-rithms (such as AdaBoost, XGBoost, and LightGBM) for five metabolites in Brassica juncea leaves: total chlorophyll, phenolics, flavonoids, glucosinolates, and anthocyanins. To enhance the model performance, we employed several spectral data preprocessing methods and feature-selection al-gorithms. Our results showed that the boosting algorithms generally outperformed the PLSR models in terms of prediction accuracy. In particular, the LightGBM model for chlorophyll and the AdaBoost model for flavonoids improved the prediction performance, with R2p = 0.71–0.74, com-pared to the PLSR models (R2p = 0.53–0.58). The final models for the glucosinolates and anthocya-nins performed sufficiently for practical uses such as screening, with R2p = 0.82–0.85 and RPD = 2.4–2.6. Our findings indicate that the application of a single preprocessing method is more effective than utilizing multiple techniques. Additionally, the boosting algorithms with feature selection ex-hibited superior performance compared to the PLSR models in the majority of cases. These results highlight the potential of hyperspectral imaging and machine learning algorithms for the non-destructive and rapid detection of plant metabolites, which could have significant implications for the field of smart agriculture.

The urban hydroponic production system is accelerating industrialization in step with the potentials for reducing environmental impact. In contrast, establishing sustainable fertilizer dosing techniques still lags behind the pace of expansion of the system. The reproducibility of root-zone nutrient dynamics in the system is poorly understood, and managing nutrients has so far primarily relied on periodic discharge or dumping of highly concentrated nutrient solutions. Here, we assayed root-zone nutrient concentration changes using three possible nutrient dosing types. Three Brassica species were hydroponically cultivated in a controlled environment to apply the nutrient absorption and transpiration parameters to the simulation analysis. We found that nutrient dosing based on total ion concentration could provide more reproducible root-zone nutrient dynamics. Our findings highlight the nutrient absorption parameter domain in management practice. This simplifies conventional nutrient management into an optimization problem. Collectively, our framework can be extended to fertilizer-emission-free urban hydroponic production.

Kale is a prominent leafy vegetable because of its high content of bioactive compounds and various health benefits. Microalgae have been suggested as a biostimulator that can replace chemical fertilizers by enhancing crop yield and supporting soil carbon sequestration. In this study, the effect of Chlorella vulgaris as a plant biostimulant on the growth and secondary metabolite contents of “Red Russian” kale (Brassica napus var. Pabularia) with green leaves and purple veins has been demonstrated. Three Chlorella treatments were used: CS, C. vulgaris suspension; CB, C. vulgaris biomass; and CFS, filtered C. vulgaris-free supernatant. The plant growth rates, phytochemical contents, and individual glucosinolate and anthocyanin contents were determined. There was no significant difference under the CS and CB treatments, while CFS negatively influenced on kale growth with 37% reduction of dried weight. In contrast, metabolite production differed according to Chlorella treatments. Total contents of chlorophyll and carotenoid were increased by 1.57 and 1.41 folds by CS treatment, whereas total contents of phenol and flavonoids were enhanced by 1.30 and 1.22 folds by CFS treatment. Totally, seven glucosinolates and four anthocyanins were characterized and quantified individually. Notably, CFS treatment increased gluconasturtiin and all anthocyanins the most, 10.28-fold and 5.90-fold, respectively.

Partial least squares regression (PLSR) prediction models were developed using hyperspectral imaging for noninvasive detection of the five most representative functional components in Brassica juncea leaves: chlorophyll, carotenoid, phenolic, glucosinolate, and anthocyanin contents. The region of interest for functional component analysis was chosen by polygon selection and the extracted average spectra were used for model development. For pre-processing, 10 combinations of Savitzky–Golay filter (S. G. filter), standard normal variate (SNV), multiplicative scatter correction (MSC), 1st-order derivative (1st-Der), 2nd-order derivative (2nd-Der), and normalization were applied. Root mean square errors of calibration (RMSEP) was used to assess the performance accuracy of the constructed prediction models. The prediction model for total anthocyanins exhibited the highest prediction level (RV2 = 0.8273; RMSEP = 2.4277). Pre-processing combination of SNV and 1st-Der with spectral data resulted in high-performance prediction models for total chlorophyll, carotenoid, and glucosinolate contents. Pre-processing combination of S. G. filter and SNV gave the highest prediction rate for total phenolics. SNV inclusion in the pre-processing conditions was essential for developing high-performance accurate prediction models for functional components. By enabling visualization of the distribution of functional components on the hyperspectral images, PLSR prediction models will prove valuable in determining the harvest time.

Red beet (Beta vulgaris L.) is a root vegetable consumed and cultivated all around the world. It contains plenty of sugars, inorganic ions and a variety of secondary metabolites known to improve human health. The aim of this work was to investigate the effect of light spectra on red beets and their components in a vertical farm (VF) compared to open field (OF). RED (red:blue-white = 4:1)-treated shoots elevated total phenolic contents (TPC) among lights. Sugar content in VF red beets was 4.2 times higher than beets from OF. Betalains in VF red beets were 2.4–2.8 times higher than OF ones, and RED-treated roots had significantly higher betalain levels compared to CON (red:blue-white = 2:1)-treated ones. VF red beets contained a higher level of inorganic nitrates and lower chloride compared to OF beets. In conclusion, the light spectrum alters the concentration of beet components to be higher than that of OF red beets, and RED light elevated TPC, sugars and betalains.

The effect of salt treatment on Brassica carinata (BC) microgreens grown under different light wavelengths on glucosinolates (GLs) and phenolic compounds were evaluated. Quantifiable GLs were identified using ultra-high performance-quadrupole time of flight mass spectrometry. Extracts’ ability to activate antioxidant enzymes (superoxide dismutase (SOD) and catalase (CAT)) was evaluated on human colorectal carcinoma cells (HCT116). Furthermore, BC compounds’ ability to activate expression of nuclear transcription factor-erythroid 2 related factor (Nrf2) and heme-oxygenase-1 (HO-1) proteins was examined using specific antibodies on HCT116 cells. Sinigrin (SIN) was the abundant GLs of the six compounds identified and its content together with total aliphatic GLs increased in saline conditions. Fluorescent (FL) and blue plus red (B1R1) lights were identified as stable cultivation conditions for microgreens, promoting biomass and glucobrassicin contents, whereas other identified individual and total indole GLs behaved differently in saline and non-saline environments. Blue light-emitting diodes and FL light in saline treatments mostly enhanced SIN, phenolics and antioxidant activities. The increased SOD and CAT activities render the BC microgreens suitable for lowering oxidative stress. Additionally, activation of Nrf2, and HO-1 protein expression by the GLs rich extracts, demonstrate their potential to treat and prevent oxidative stress and inflammatory disorders. Therefore, effective salt treatments and light exposure to BC microgreens present an opportunity for targeted regulation of growth and accumulation of bioactive metabolites.