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Functional films have been used in greenhouses to improve the light environment for plant growth. Among them, a spectrum conversion film converting the green light of incident sunlight into red light has been reported to increase the crop productivity. However, the results are not always consistent, and the reasons for the improvement are not fully understood. The objectives of this study were to reveal the cumulative effects of a green-to-red spectrum conversion film (SCF) on the electron transport and photosynthetic performance of Fragaria × ananassa Duch. The photosynthetic efficiency, leaf optical properties, chlorophyll content, chlorophyll fluorescence, growth, and fruit qualities when the plant was grown under a transparent polyethylene film (PE) and SCF were evaluated. The sunlight modified by SCF did not change the leaf optical properties and chlorophyll content but significantly increased the chlorophyll fluorescence parameters related to reduction end electron acceptors at PSI acceptor side and the efficiency of electron transport. Without an increase in nonphotochemical quenching, the effective quantum yields of PSII and PSI of leaves grown under SCF were significantly higher than those parameters when grown under PE. Forty eight days after transplanting, the photosynthetic efficiency and photosynthetic rates of leaves and whole plants increased significantly under SCF compared to PE. The vegetative growth was not affected by SCF, but the fruit weight, sweetness, acidity, and firmness under SCF were significantly improved. These results indicated that sunlight modified by SCF stimulates electron flow and improves photosynthetic capacity and fruit quality of Fragaria × ananassa Duch.

Visible traits can be criteria for selecting a suitable crop. Three-dimensional (3D)-scanned plant models can be used to extract visible traits; however, collecting scanned data and physically manipulating point-cloud structures of the scanned models are difficult. Recently, deep generative models have shown high performance in learning and creating target data. Deep generative models can improve the versatility of scanned models. The objectives of this study were to generate sweet pepper (Capsicum annuum) leaf models and to extract their traits by using deep generative models. The leaves were scanned, preprocessed and used to train the deep generative models. The variational autoencoder, generative adversarial network (GAN) and latent space GAN were used to generate the desired leaves. The optimal number of latent variables in the model was selected via the Jensen–Shannon divergence (JSD). The generated leaves were evaluated by using the JSD, coverage and minimum matching distance to determine the best model for leaf generation. Among the deep generative models, a modified GAN showed the highest performance. Sweet pepper leaves with various shapes were generated from eight latent variables following a normal distribution, and the morphological traits of the leaves were controlled through linear interpolation and simple arithmetic operations in latent space. Simple arithmetic operations and gradual changes in the latent space modified the leaf traits. Deep generative models can parameterize and generate morphological traits in digitized 3D plant models and add realism and diversity to plant phenotyping studies.

Ultraviolet-B (UV-B, 280–315 nm) radiation has been known as an elicitor to enhance bioactive compound contents in plants. However, unpredictable yield is an obstacle to the application of UV-B radiation to controlled environments such as plant factories. A typical three-dimensional (3D) plant structure causes uneven UV-B exposure with leaf position and age-dependent sensitivity to UV-B radiation. The purpose of this study was to develop a model for predicting phenolic accumulation in kale (Brassica oleracea L. var. acephala) according to UV-B radiation interception and growth stage. The plants grown under a plant factory module were exposed to UV-B radiation from UV-B light-emitting diodes with a peak at 310 nm for 6 or 12 h at 23, 30, and 38 days after transplanting. The spatial distribution of UV-B radiation interception in the plants was quantified using ray-tracing simulation with a 3D-scanned plant model. Total phenolic content (TPC), total flavonoid content (TFC), total anthocyanin content (TAC), UV-B absorbing pigment content (UAPC), and the antioxidant capacity were significantly higher in UV-B-exposed leaves. Daily UV-B energy absorbed by leaves and developmental age was used to develop stepwise multiple linear regression models for the TPC, TFC, TAC, and UAPC at each growth stage. The newly developed models accurately predicted the TPC, TFC, TAC, and UAPC in individual leaves with R2 > 0.78 and normalized root mean squared errors of approximately 30% in test data, across the three growth stages. The UV-B energy yields for TPC, TFC, and TAC were the highest in the intermediate leaves, while those for UAPC were the highest in young leaves at the last stage. To the best of our knowledge, this study proposed the first statistical models for estimating UV-B-induced phenolic contents in plant structure. These results provided the fundamental data and models required for the optimization process. This approach can save the experimental time and cost required to optimize the control of UV-B radiation.

Supplemental interlighting is commonly used in modern greenhouses to improve light deficiency, but the light spectrum affects fruit quality and color change. This study aimed to analyze the effect of interlighting with red, blue, and additional far-red light on the fruit qualities and carotenoid contents of red and yellow sweet peppers (Capsicum annuum L.). Three light treatments were applied: natural light (NL), NL with red + blue LED interlighting (71 μmol m–2 s–1) (RB), and RB with far-red light (55 μmol m–2 s–1) (RBFR). Ascorbic acid, free sugars, and individual carotenoid content were quantified with HPLC analysis. Fruits were sampled on 2020.11.14 (Group 1) and 2021.01.03 (Group 2) from the plants grown under average light intensities of 335.9 and 105.6 μmol m–2 s–1, respectively. In the overall period, total yields in RB and RBFR were 22 and 33% higher than those in NL in red fruits and 2 and 21% higher in yellow fruits, respectively. In both colored fruits, ascorbic acid, total soluble sugar, and carotenoid content were higher in RB and RBFR than NL. In Group 1, ascorbic acid and total soluble sugar were significantly different between RB and RBFR only in red fruits. In Group 2, ascorbic acids in red and yellow fruits were 9 and 3% higher in RBFR than RB but total soluble sugars were 4 and 2% lower, respectively. Carotenoid contents in red and yellow fruits were 3.0- and 2.1-fold higher in RB and 2.0- and 1.4-fold higher in RBFR than those in NL, respectively. In this study, interlighting had a significant impact on fruit quality in Group 2, mainly due to the increase in the ratio of interlighting to total light by seasonal changes. In particular, red and yellow fruit yields were 9% and 19% higher in RBFR than RB, but carotenoid contents were 26 to 9% lower, respectively. This result exhibited that additional far-red lighting has a trade-off relationship between fruit yield and carotenoid content. Thus, it is necessary to provide an adequate light spectrum according to a specific cultivation purpose, such as improving yield or accumulating plastids in fruits.

Although spectrum conversion films are used to improve the photosynthetic efficiency and, ultimately, crop growth, the effects of the modified spectrum on photosynthetic traits in plants have not yet been sufficiently reported. The objective of this study was to investigate the changes in photosynthetic performance and chlorophyll fluorescence of sweet pepper (Capsicum annuum L.) under a green-to-red conversion (GtR) film. The GtR-modified spectrum increased the dry mass and decreased the petiole length. The photosynthetic light-response curves were significantly improved, and the gap of the maximum photosynthetic rates increased over time after covering. The GtR-modified spectrum significantly increased chlorophyll fluorescence parameters in the JIP-test, such as parameters related to the reduction of end electron acceptors on the PSI acceptor side, the efficiency for electron transport in PSII, and the performance indexes. Our data indicated that the GtR-modified spectrum promotes electron transfer around PSI, improving photosynthetic performance and growth.

The photosynthetic rates of leaves depend on the vertical position and cultivation conditions. However, few models have been proposed to express photosynthesis according to leaf position, and there was a lack of quantitative analysis between physiological indicators and model parameters. The objectives of this study were to analyze the leaf photosynthetic characteristics of paprika plants according to leaf vertical position using photosynthesis models, and to analyze the relationship between the total nitrogen content and the photosynthetic model parameters. Leaf photosynthetic rates at different vertical positions were measured under varying light intensities and CO2 concentrations in triplicate. Rectangular hyperbola and FvCB (Farquhar, von Caemmerer, and Berry) models were selected, calibrated, and validated as multivariable photosynthesis models. Total nitrogen contents and SPAD values were measured at each leaf position and the coefficients of the photosynthetic rate models were compared. The R2 values for the rectangular hyperbola and FvCB models were 0.86 and 0.91, and the RMSE values were 4.651 and 2.104, respectively. Total nitrogen content linearly increased with increasing vertical leaf position and it was linearly related to the maximum carboxylation capacity and maximum electron transport rate, estimated in the FvCB model. In this study, the FvCB model was considered more suitable for expressing the relationship between total nitrogen contents and plant’s physiological responses according to the vertical position of leaves. The vertical leaf photosynthetic rate models established in this study will contribute to determining optimal environmental conditions for maximizing crop photosynthesis and establish the criteria for precise CO2 enrichment in greenhouses.

Spectrum conversion film (SCF) is a covering material that modifies incident solar spectrum to more-active wavelengths in photosynthesis. Due to its fluorescence property, the performance of SCF cannot be accurately evaluated in a conventional way for agricultural films. We proposed quantitative methods to evaluate the conversion performance of SCF and tested the plant responses. The performance of GR films [green light (500–600 nm) to red light (600–700 nm)] containing different concentrations of dye (40–3000 ppm) was tested in comparison to a transparent film (control). A method for estimating light emission was developed using a series of equations and values measured under artificial lighting. The spectral properties and photosynthetic rates of sweet pepper (Capsicum annuum L.) leaves covered with the GR film were measured using a solar simulator. The emission of GR film was detected using a spectroradiometer connected to an integrating sphere instead of using a spectrophotometer used in the conventional method. The transmittance of GR film and its change rate of transmitted photon flux densities at emission wavelength differed depending on the light source. The change rate calculated through the equations was constant within the target spectrum and could be used as a basis for conversion performance. The solar spectrum modified by the GR film caused to increase the red light reaching the leaves by 10.47% and thereby increased the photosynthetic rates by 15.41% compared to the control. The photosynthetic efficiency based on incident and absorbed photons under the GR film increased by 22.14% and 21.87%, respectively. These methods were proposed for quantifying spectral properties of SCF under the solar spectrum, confirming the application of solar simulator for photosynthetic evaluation. When used as a standard light source for SCF, a solar simulator provides indoor test conditions with a spectrum similar to solar radiation without long-term cultivation or a large-scale film test.

The nutrient replenishment method primarily impacts the nutrient variations in a closed-loop soilless culture system. However, there is still a lack of systematic approaches for the effective way of nutrient replenishment. Our previous study theoretically derived and experimentally validated an alternative nutrient replenishment method expecting synchronized total fertilizer supply to total nutrient absorption by crops and lower concentration fluctuations than conventional methods. However, no individual nutrient management has been performed. The objective of this study was to apply individual nutrient management to the alternative nutrient replenishment technique under experimental- and commercial-scale electrical conductivity (EC)-based closed-loop soilless cultures. Automated nutrient solution mixing modules and sweet peppers grown on rockwool slabs were used. Nutrient concentrations and crop productivity were compared between the closed-loop system using the alternative nutrient replenishment and the conventional open-loop systems. During early treatment, rapid decreases in K+ and H2PO4− were observed in the closed-loop system. However, after the stock solution nutrient adjustment, the decreasing trend was stabilized and returned close to initial concentrations. No significant differences in sugar content, incidence of blossom-end rot, and productivity of sweet peppers were observed between the closed- and open-loop soilless cultures. We confirmed that the nutrient variation stabilizing effect of the alternative nutrient replenishment method was valid under nutrient adjustment conditions and had comparable nutrient management performance with the open-loop system.

The leaf area index (LAI), which represents crop growth characteristics, is used to calculate canopy photosynthetic rates, set irrigation standards, and predict crop growth. The LAI can be non-destructively and continuously estimated using the light-intensity ratio of the upper and lower crop canopy, but it is affected by solar altitude and external weather conditions. The objective of this study was to develop a method to estimate the LAI of bell peppers (Capsicum annuum L.) using the light-intensity ratio of the upper and lower crop canopy via solar altitude and weather conditions. Growth stages and weather conditions with solar altitude were set using 3D-scanned plant models and ray-tracing simulation, respectively. The light intensities at each location of the canopy for given conditions were calculated using ray-tracing simulation. The relationship between the light-intensity ratio and the LAI was analyzed using a long short-term memory (LSTM) algorithm, which is a type of artificial neural network. According to our results, the ratio varied depending on solar altitude and external weather conditions and exponentially decreased with increasing LAI. This LSTM algorithmic approach was able to quantitatively analyze this complex relationship; compared with a greenhouse experiment for validation, the algorithm was highly accurate (R2 = 0.808). Accuracy further increased when solar altitude and weather conditions were added to the model. Therefore, we conclude that, using this method, the LAI can be accurately measured in a non-destructive and continuous manner.

Since kale (Brassica oleracea L. var. acephala) is one of the healthiest vegetables, its cultivation is increasing for either fresh consumption or as a source for functional foods and nutraceuticals. Plant factories are able to control the environment and trigger the accumulation of bioactive compounds with a stable supply by systematic cultivation methods. The objectives of this study were to evaluate the changes in the total phenolic compounds (TPCs), total flavonoid compounds (TFCs), glucosinolates (GLSs), and antioxidant capacity of kale in a plant factory and to determine an optimal harvest time for the maximum annual production. Two cultivars, namely ‘Manchoo collard’ and ‘Jangsoo collard’, were cultivated in a plant factory and thinned to avoid mutual shading. Both cultivars were harvested every week from 14 to 49 days after transplanting (DAT). The fresh weight (FW), dry weight (DW), projected leaf area (PLA), TPCs, TFCs, GLSs, and antioxidant capacity of both plants were measured every week. The annual production was calculated as follows: DW × the concentration × planting density × cultivation cycles per year. The optimal harvest time was determined based on the continuous phase of the production by modeling. The FW and DW of both cultivars exponentially increased, but the PLA hardly increased at 35 DAT. The TPCs, TFCs, and antioxidant capacity fluctuated or slightly changed, but the amount of substance per plant gradually increased. Their annual production increased with increasing harvest time, and only the production of TPCs in ‘Manchoo collard’ showed a local maximum when harvested at 35–42 DAT. Glucoiberin, sinigrin, and glucobrassicin were the major components of GLSs in both cultivars, and their contents fluctuated. The concentration of total GLSs was the highest at 42 DAT. Additionally, the annual production of the total and major GLSs showed the same results as the TPCs, TFCs, and antioxidant capacity. From the results, the optimum harvest time for production was determined to be 42 DAT.

Environmental and growth factors are important variables that affect the transpiration rate of crops, but due to their complex nature, it is difficult to systematically use all these factors to estimate transpiration rates. Application of artificial neural networks (ANNs) can be an efficient way of deriving meaningful results from complex nonlinear data. The objectives of this study were to estimate transpiration rates using an ANN, to compare these estimations with the Penman–Monteith (P–M) equation, and to analyze the estimation accuracy according to cultivation period. Paprika (Capsicum annuum L. cv. Scirocco) was cultivated for two cropping periods in a year. Environmental factors were collected every minute and leaf area index (LAI) as a growth factor was measured every 2 weeks. An ANN consisting of an input layer using eight environmental and growth factors, five hidden layers, and an output layer for transpiration rate was constructed. The estimation accuracy in the ANN was higher than the P–M when using aerial environmental factors, but it was further increased by adding root-zone factors. Using daily average data, ANN accuracy was higher for longer cultivation periods and accompanying data. R2 values were 0.88 and 0.73 in the ANN and P–M for one year, whereas they were 0.84–0.93 and 0.79–0.83 for the individual seasons, respectively. The accuracy of the ANN tended to increase when the time step (data-averaging time unit) decreased to 10 min and there was no significant difference over 10 min. Using 10-min average data, the ANN showed high accuracies with R2 = 0.95–0.96 and root mean square error = 0.07–0.10 g m−2 min−1, regardless of cultivation period and season. Therefore, it was confirmed that the ANN could accurately estimate transpiration rates at specific times using the data collected from the entire cultivation period. This approach may be useful for developing irrigation strategies by estimating the transpiration rates of crops grown in soilless cultures.

As smart farms are applied to agricultural fields, the use of big data is becoming important. In order to efficiently manage smart farms, relationships between crop growth and environmental conditions are required to be analyzed. From this perspective, various artificial intelligence algorithms can be used as useful tools to quantify this relationship. The objective of this study was to develop and validate an algorithm that can interpret the crop growth rate response to environmental factors based on a recurrent neural network (RNN), and to evaluate the algorithm accuracy compared to the process-based model (PBM). The algorithms were trained with data from three growth periods. The developed methods were used to measure the crop growth rate. The algorithm consisted of eight environmental variables days after transplanting and two crop growth characteristics as input variables producing weekly crop growth rates as output. The RNN-based crop growth rate estimation algorithm was validated using data collected from a commercial greenhouse. The CropGro-bell pepper model was applied to compare and evaluate the accuracy of the developed algorithm. The training accuracies varied from 0.75 to 0.81 in all growth periods. From the validation result, it was confirmed that the accuracy was reliable in the commercial greenhouse. The accuracy of the developed algorithm was higher than that of the PBM. The developed algorithm can contribute to crop growth estimation with a limited number of data.

An electrical conductivity (EC)-based closed-loop soilless culture system is practical for in-field deployment. Literature on the closed-loop soilless culture nutrient management premise the limitations in managing recycled nutrients under dynamic changes in individual nutrient uptake concentrations. However, recent systems analysis studies predicting solutions for nutrient fluctuation stabilization in EC-based closed-loop soilless culture systems suggest that the system may have a deterministic side in nutrient variation. This study aims to derive a nutrient control principle in an EC-based nutrient recycling soilless culture system by theoretical and experimental analyses. An integrated model of solutes such as K+, Ca2+, and Mg2+ and water transport in growing media, automated nutrient solution preparation, and nutrient uptake was designed. In the simulation, the intrinsic characteristics of nutrient changes among open-, semi- closed-, and closed-loop soilless cultures were compared, and stochastic simulations for nutrient control were performed in the closed-loop system. Four automated irrigation modules for comparing nutrient changes among the soilless culture systems were constructed in the greenhouse. Sweet pepper plants were used in the experiment. In the experimental analysis, nutrient concentration conversion to the proportion between nutrients revealed distinctive trends of nutrient changes according to the treatment level of drainage recycling. Theoretical and experimental analyses exhibited that nutrient variations in open-, semi- closed-, and closed-loop soilless culture systems can be integrated as a function of nutrient supply to the system’s boundary areas. Furthermore, stochastic simulation analysis indicated that the nutrient ratio in the soilless culture system reveals the nutrient uptake parameter-based deterministic patterns. Thus, the nutrient ratio in the closed-loop soilless culture could be controlled by the long-term feedback of this ratio. We expect that these findings provide theoretical frameworks for systemizing nutrient management techniques in EC-based closed-loop soilless culture systems.

A non-destructive analytical method to measure plant internal electrical conductivity (ECp) was developed to monitor plant responses to changes in environmental conditions. However, the relationship between ECp and plant physiological responses has not yet been established. The objective of the study was to evaluate the relationships among ECp, photosynthetic responses, and sap flow in paprika to monitor ECp in relation to changes in environmental conditions. High ECp levels were related to high photosynthetic rate, stomatal conductance, and transpiration rate. Sap flow in the plant was also associated with ECp with a correlation coefficient of 0.606. However, the sap flow reflected only water flux, while ECp was determined by both water and ion content in stems of paprika. The ECp was predicted using environmental factors including temperature, irradiation, and relative humidity. A comparison of measured and predicted ECps could be used to detect unusual cultivation conditions for paprika such as drought and high temperature. Plant responses to water shortage were reflected by lower ECp compared to the predicted value.

UV-B (280–315 nm) radiation has been used as an effective tool to improve bioactive compound contents in controlled environments, such as plant factories. However, plant structure changes with growth progress induce different positional distributions of UV-B radiation interception, which cause difficulty in accurately evaluating the effects of UV-B on biosynthesis of bioactive compounds. The objective of this study was to quantitatively analyze the positional distributions of UV-B radiation interception and bioactive compound contents of kales (Brassica oleracea L. var. acephala) with growth progress and their relationships. Short-term moderate UV-B levels did not affect the plant growth and photosynthetic parameters. Spatial UV-B radiation interception was analyzed quantitatively by using 3D-scanned plant models and ray-tracing simulations. As growth progressed, the differences in absorbed UV-B energy between leaf positions were more pronounced. The concentrations of total phenolic compound (TPC) and total flavonoid compound (TFC) were higher with more cumulative absorbed UV-B energy. The cumulative UV energy yields for TFC were highest for the upper leaves of the older plants, while those for TPC were highest in the middle leaves of the younger plants. Despite the same UV-B levels, the UV-B radiation interception and UV-B susceptibility in the plants varied with leaf position and growth stage, which induced the different biosynthesis of TFC and TPC. This attempt to quantify the relationship between UV-B radiation interception and bioactive compound contents will contribute to the estimation and production of bioactive compounds in plant factories.

Although plant responses to artificial lighting spectra often produce abnormal morphogenesis and reduced productivity, no quantification method to determine how plants perceive and respond to light has been available. Our objective in this study was to test whether a plant&rsquo, s spectral perception can be quantified using the light absorption of its major photoreceptors, phytochrome, cryptochrome, and phototropin. We developed an artificial solar lamp and three different light sources, based on a high-pressure sodium lamp, a fluorescent lamp, and red and blue light-emitting diodes, whose absorption by photoreceptors was equal to that of the standard solar spectrum. Cucumber plants grown under the artificial solar and developed light sources showed normal photomorphogenesis and were indistinguishable from each other. Plants grown under unmodified commercial light sources had abnormal photomorphogenesis that made them short and small. The photosynthetic rate was higher under the unmodified light sources, however, dry masses were highest under the artificial solar and modified light sources, indicating that the cucumber plants are optimized to the solar spectrum. Our results clearly demonstrate that the spectral perceptions of plants can be quantified using the light absorption of their photoreceptors, not visual color or spectra. We expect that our findings will contribute to a better understanding of plant perceptions of and responses to light quality, and improve the productivity of plants cultivated under artificial light.

Among abiotic stresses, both drought and UV-B radiation effectively trigger the accumulation of secondary metabolites, and can be widely applied in plant factories. The objectives of this study were to investigate antioxidant accumulation under drought stress alone, or in combination with UV-B radiation near harvest, and to determine an optimal treatment time for maximum antioxidant production. Kale (Brassica oleracea L. var. acephala) plants were grown in a plant factory and harvested at 42 days after transplanting. The single and combination treatments lasted for 7 to 1 days and 4 to 2 days before harvest, respectively. The results of both Fv/Fm (maximal photochemical efficiency in photosystem II) and leaf water potential could ensure the function of photosynthesis and maintain normal leaf moisture in single drought treatments of less than 4 days. The total phenolic and flavonoid contents and antioxidant activities were significantly increased in both single and combination treatments for 3 to 4 days, compared to other treatments. The supplementary UV-B treatments showed no extra formation of antioxidants compared to the single drought treatments. As a result, drought for 3 days before harvest could achieve the highest potential value of kale as a source of natural antioxidants.

In order to improve photosynthesis efficiency and crop growth, it is important to predict CO2 concentration as well as CO2 consumption in greenhouses. The objective of this study was to predict greenhouse CO2 concentration via an artificial neural network (ANN) that incorporated environmental factors. Temperature, relative humidity, atmospheric pressure, solar radiation, and CO2 concentration were measured every 10 min over a 6-month period in a greenhouse located in Boryeong, Korea. Measured environmental data were used to train the ANN. Among the 14,866 data points used in the experiment, 10,000 and 4866 data points were used for training and testing, respectively. An ANN with an input layer with input neurons, two hidden layers with 32–2048 neurons, and an output later with one neuron was selected. A rectified linear unit was used as the activation function in each node of the ANN. An ANN structure that included 256 neurons in the hidden layers showed the highest test accuracy (R2 = 0.97) was selected from all the structures, while multivariate linear regression showed lower test accuracy than the ANN (R2 = 0.78). The ANN accurately estimated CO2 concentration in the greenhouse using big data for changing patterns of the inside environmental factors without vent position data. Furthermore, it is possible to estimate crop CO2 consumption in greenhouses with this ANN using the change in greenhouse CO2 concentration.

The spectrum of light affects both the electrical energy consumption by plants and photosynthetic efficiency. In a plant factory, where light-emitting diodes (LEDs) serve as an alternative to solar light, the optimal spectrum of light should be carefully chosen to maximize the rate of photosynthesis and the electrical energy efficiency of the crop. The objectives of this study were to investigate the photosynthetic rate of different colored lettuces (reddish and green leaves), to quantify the spectral dependence of photosynthetic efficiency, and to optimize the LED spectrum for maximum canopy photosynthesis and electrical energy consumption in lettuce grown in a plant factory. Two lettuce cultivars (Lactuca sativa L.), ‘JeokChukMyeon’ and ‘CheongChukMyeon’, were assessed for light absorption and photosynthetic efficiency at the single leaf and canopy levels, and the relative consumption of electrical energy from the LED lights was measured at 18 narrow wavelength bands of 10 nm from 400 to 700 nm. Anthocyanin and chlorophyll content (SPAD value) were measured and correlated with leaf color. Light interception by the canopy was estimated with light transmittance models. The light absorption was similar among the green and reddish lettuce cultivars at most wavelengths, but slightly higher in the reddish leaves around 550 nm (green region). In the reddish leaves, photosynthetic rates per incident photon of a single leaf had two peaks at 650-660 and 400-410 nm, while the photosynthetic rate per absorbed photon had three peaks at 650-660, 400-410, and 540-560 nm. In the green region of the light spectrum, both photosynthetic rates per incident photon and those per absorbed photon were lower in the reddish cultivars than in the green cultivars. The spectral dependence of light absorption at the canopy level was much weaker than that at the single leaf level. The quantum yield and absorption of green light at the canopy level were nearly same as those of blue and red lights, indicating that the photosynthetic efficiency of green light at the canopy level was higher than that at the single leaf level. The relative electrical energy consumption was lower in the green region than in the red and blue regions. Therefore, the photosynthetic efficiency based on electrical energy consumption at the canopy level was much lower with green LEDs than with blue or red LEDs. These results describe the plant response to the light spectrum at the canopy level and can be useful for optimizing artificial lighting sources for maximum plant productivity and energy-savings in a plant factory.

Current LED-based artificial lights for crop cultivation consist of red and blue lights because these spectra effectively promote leaf photosynthesis. However, the absence of green light could be disadvantageous for crop production, as green light plays an important role in plant development. The objective of this study was to investigate whether adding green light to different proportions of red and blue light would affect the leaf photosynthetic rate, growth, and morphology of lettuce plants. Plants were transplanted and grown hydroponically for 25 days under different combinations of red, blue (0, 10, 20, and 30%), and green (0 and 10%) light at 150 ± 15 μmol•m-2•s-1 of photosynthetic photon flux density (PPFD). The leaf photosynthetic rate was highest under 80% red and 20% blue light and decreased significantly with the addition of green light and the absence of blue light. As the fraction of blue light increased, leaf size and plant growth decreased significantly. However, while the addition of green light considerably reduced the leaf photosynthetic rate, it did not reduce plant growth. In the absence of blue light, the plants showed symptoms of the shade avoidance response, which possibly enhanced their growth by improving their light interception. Therefore, the addition of 10% (15 μmol•m-2•s-1) green light did not have a positive effect on the growth of lettuce. Further study using higher intensities of green light is required to investigate the effects of green light on plant growth.

Background Root-zone environment is considered difficult to analyze, particularly in interpreting interactions between environment and plant. Closed-loop soilless cultures have been introduced to prevent environmental pollution, but difficulties in managing nutrients can cause nutrient imbalances with an adverse effect on crop growth. Recently, deep learning has been used to draw meaningful results from nonlinear data and long short-term memory (LSTM) is showing state-of-the-art results in analyzing time-series data. Therefore the macronutrient ion concentrations affected by accumulated environment conditions can be analyzed using LSTM. Results The trained LSTM can estimate macronutrient ion concentrations in closed-loop soilless cultures using environmental and growth data. The average training accuracy of six macronutrients was R2 = 0.84 and the test accuracy was R2 = 0.67 with RMSE = 1.48 meq L−1. The used values of input interval and time step were 1 h and 168 (1 week), respectively. The accuracy was improved when the input interval became shorter, but not improved when the LSTM consisted of a multilayer structure. Regarding training methods, the LSTM improved the accuracy better than the non-LSTM. The trained LSTM showed relatively adequate accuracies and the interpolated ion concentrations showed variations similar to those seen during traditional cultivation. Conclusions We could analyze the nutrient balance in the closed-loop soilless culture, the model showed potential in estimating the macronutrient ion concentrations using environmental and growth factors measured in greenhouses. Since the LSTM is a powerful and flexible tool used to interpret accumulative changes, it is easily applicable to various plant and cultivation conditions. In the future, this approach can be used to analyze interactions between plant physiology and root-zone environment.

When designing a plant production system, it is crucial to perform advanced estimation of growth and productivity in relation to cultivation factors. In this study, we developed Planting-density Growth Harvest (PGH) charts to facilitate the estimation of crop growth and harvest factors such as growth rate, relative growth rate, shoot fresh weight, harvesting time, marketable rate, and marketable yield for quinoa (Chenopodium quinoa Willd.) and sowthistle (Ixeris dentata Nakai). The plants were grown in a nutrient film technique (NFT) system in a closed-type plant factory under fluorescent lamps with three-band radiation under a light intensity of 140 μmol·m-2 ·s-1, with a 12-h/12-h (day/night) photoperiod. We analyzed the growth and yield of quinoa and sowthistle grown in nutrient solution at EC 2.0 dS·m-1 under four planting densities: 15 cm between rows with a within-row distance of 15 × 10 cm (67 plants/m 2), 15 × 15 cm (44 plants/m2), 15 × 20 cm (33 plants/m2), and 15 × 25 cm (27 plants/m2). Crop growth rate, relative growth rate, and lost time were closely correlated with planting density. We constructed PGH charts based on the growth data and existing models. Using these charts, growth factors could easily be determined, including growth rate, relative growth rate, and lost time, as well as harvest factors such as shoot fresh weight, marketable yield per area, and harvesting time, based on at least two parameters, for instance, planting density and shoot fresh weight.

The ice plant (Mesembryanthemum crystallinum L.), a medicinal plant with well-known effects on retarding diabetes mellitus, is increasingly being produced in plant factories in Asia. The ice plant is a Crassulacean Acid Metabolism (CAM) plant, but performs C3 photosynthesis during the juvenile period. The objective of this study was to develop a photosynthetic model of ice plants growing under plant factory conditions. C3 photosynthesis was observed in juvenile plants in plant factory growth conditions and a conversion from C3 to CAM photosynthesis was observed under salt-stressed condition at an electrical conductivity (EC) of 6.0 dS·m-1. The light saturation and compensation points, determined by a regression analysis of C3 light curves for the ice plant leaves, were 609.4 and 53.2 μmol·m-2·s-1, respectively. The accuracy of the light response was compared between negative exponential and non-rectangular hyperbolic function models. The non-rectangular hyperbola was more accurate with complicated parameters while the negative exponential function was more practical with simple parameters in the light response curves. Measurement of net CO2 assimilation rate (A) and intercellular CO2 concentration (C i ) allowed construction of the A-Ci curve and regression analysis of this curve revealed the CO2 saturation and compensation points as 632.9 and 117.2 μmol·mol-1, respectively. A coupled photosynthetic model was developed for the simultaneous prediction of photosynthesis, stomatal conductance, transpiration, and temperature of the ice plant leaves. Sharkey’s regression method was used to determine the photosynthetic parameters of maximum carboxylation rate, the potential rate of electron transport, and the rate of triose phosphate utilization, which were 222.3, 234.9, and 13.0 μmol·m-2·s-1, respectively. The parameters of minimum stomatal conductance of water vapor at the light compensation point (b) and the empirical coefficient (m) for the sensitivity of stomatal conductance and relative humidity in the Ball, Woodrow and Berry model could be solved as b = 0.0487 and m = 0.0012 by linear regression analysis using the measured A-Ci values. Although the A-Ci curve of the negative exponential function had higher accuracy than the biochemical model, the coupled biochemical model could physiologically explain the photosynthesis of the ice plant leaves under plant factory conditions.

For the production of plants in controlled environments such as greenhouses and plant factories, crop modeling and simulations are effective tools for configuring the optimal growth environment. The objective of this study was to develop a coupled photosynthetic model of sweet basil (Ocimum basilicum L.) reflecting plant factory conditions. Light response curves were generated using photosynthetic models such as negative exponential, rectangular hyperbola, and non-rectangular hyperbola functions. The light saturation and compensation points determined by regression analysis of light curves using modified non-rectangular hyperbola function in sweet basil leaves were 545.3 and 26.5 µmol·m-2·s-1, respectively. The non-rectangular hyperbola was the most accurate with complicated parameters, whereas the negative exponential was more accurate than the rectangular hyperbola and could more easily acquire the parameters of the light response curves of sweet basil compared to the non-rectangular hyperbola. The CO2 saturation and compensation points determined by regression analysis of the A-Ci curve were 728.8 and 85.1 µmol·mol-1, respectively. A coupled biochemical model of photosynthesis was adopted to simultaneously predict the photosynthesis, stomatal conductance, transpiration, and temperature of sweet basil leaves. The photosynthetic parameters, maximum carboxylation rate, potential rate of electron transport, and rate of triose phosphate utilization determined by Sharkey’s regression method were 102.6, 117.7, and 7.4 µmol·m-2·s-1, respectively. Although the A-Ci regression curve of the negative exponential had higher accuracy than the biochemical model, the coupled biochemical model enable to physiologically explain the photosynthesis of sweet basil leaves.

In existing closed-loop soilless cultures, nutrient solutions are controlled by the electrical conductivity (EC) of the solution. However, the EC of nutrient solutions is affected by both growth environments and crop growth, so it is hard to predict the EC of nutrient solution. The objective of this study was to predict the EC of root-zone nutrient solutions in closed-loop soilless cultures using recurrent neural network (RNN). In a test greenhouse with sweet peppers (Capsicum annuum L.), data were measured every 10 s from October 15 to December 31, 2014. Mean values for every hour were analyzed. Validation accuracy (R2) of a single-layer long short-term memory (LSTM) was 0.92 and root-mean-square error (RMSE) was 0.07, which were the best results among the different RNNs. The trained LSTM predicted the substrate EC accurately at all ranges. Test accuracy (R2) was 0.72 and RMSE was 0.08, which were lower than values for the validation. Deep learning algorithms were more accurate when more data were added for training. The addition of other environmental factors or plant growth data would improve model robustness. A trained LSTM can control the nutrient solutions in closed-loop soilless cultures based on predicted future EC. Therefore, the algorithm can make a planned management of nutrient solutions possible, reducing resource waste.

The moisture content (MC) and electrical conductivity (EC) in substrates are major root-zone environmental factors that affect the transpiration rate and subsequent plant growth in soilless culture. For maintaining optimum root-zone environments, efficient real-time irrigation control is required based on the substrate EC, substrate MC, and transpiration. The objectives of this study were to clarify the relationship between substrate MC and EC and analyze the changes in substrate EC, plant growth, and water use efficiency under different moisture control regimes. Irrigation systems maintaining three regimes of substrate MC (70–85, 60–85, and 50–85%) were set as treatments, and a conventional irrigation using accumulated radiation served as a control. Subsequent changes in the substrate EC and transpiration rate were continuously measured at different substrate MCs, and the relationships between these variables were derived. The transpiration rate was most sensitive to substrate EC at general cultivation conditions of a substrate EC of 2.5 to 4.5 dS·m−1 and a substrate MC of 60 to 85%. The transpiration rate tended to decrease with increasing substrate EC and decreasing substrate MC. More water was consumed in a higher substrate MC, which was controlled within a narrow range of MC. However, substrate EC was well-controlled below 4.5 dS·m−1 in a substrate MC of 70 to 85%. The relationship between the range of substrate MC and the increase in substrate EC was obtained using equations. Although more water was supplied for the control with a substrate MC of 70 to 85%, fruit productivity tended to increase compared to the other substrate MC treatments (60–85 and 50–85%). From the results, it is hypothesized that precise control of root-zone environments can be used to increase fruit productivity and water use efficiency and to minimize plant water stress as well.

This study was conducted to determine the effects of UV-A and UV-B wavelengths on the growth and content of antioxidant phenolic compounds in sowthistle (Ixeris dentata Nakai), a medicinal plant, grown in a closed plant-production system. In study I, sowthistle plants were continuously exposed to UV-A light. In study II, two UV treatments [repeated UV-B (R): 4 h·d−1 for 6 days, gradual UV-B (G): from 1 to 7 h·d−1 for 6 days] were applied to the sowthistle plants. As a result, contents of total phenolics and antioxidants in UV-A-treated plants were significantly (50 and 30%, respectively) higher than those in the control plants after 3 d of UV treatment without growth inhibition. Moreover, plants continuously exposed to UV-A for 5 d had 50% higher total flavonoid content than the control. The phenylalanine ammonia-lyase (PAL) activity supported the accumulation of phytochemicals in plants exposed to UV-A. The UV-B (R) treatment led to a more rapid decrease in the chlorophyll fluorescence ratio than UV-B (G) treatment. The UV-B (R) or UV-B (G) treatment produced more total phenolics, flavonoids, and antioxidants, although both UV-B treatments significantly inhibited plant growth measured at 2 days after treatment. The UV irradiation also enhanced PAL activity at 2 and 3 days after treatment, suggesting biosynthetic activation of secondary metabolites. Therefore, application of UV-A or UV-B light can be used as a strategy to improve antioxidant phenolic compounds of sowthistle plants grown in closed plant production systems.

The objective of this study was to determine the effect of physicochemical properties of inorganic supporting materials on the growth of potato plantlets in a nutrient-circulated photoautotrophic micropropagation (NCM) system. Nine mixtures consisting of combinations of perlite and/or vermiculite with three particle sizes (PL, PM, and PS = perlite large, medium, and small; VL, VM, and VS = vermiculite large, medium, and small, respectively) were used to cultivate potato plantlets in the NCM system. The ratios of vermiculite to perlite in the nine mixtures were determined by cation exchange capacity (CEC) and three distinct levels of water content. Among the nine mixtures with different values of porosity, bulk density, pH, and EC, the mixtures with high ratios of PS or VS had higher levels of water content, total porosity, and water-filled porosity than those with high ratios of PL or VL. Water holding capacity increased in mixtures with high levels of vermiculite. The EC and pH ranges of all mixtures were 1.2 to 2.5 mS·cm−1 and 6.3 to 7.2, respectively. Two mixtures containing 40 and 50% VS had exceptionally the highest EC and pH values. The optimum perlite and vermiculite content for potato plantlet growth was found in two treatments (PL:VL = 30:70 and PM:PS:VL:VM = 20:10:40:0) with a water content of 37% to 47% and CEC of approximately 17 cmol·kg−1. Regardless of the mixtures of supporting materials, conventional polycarbonate culture boxes retarded the growth of potato plantlet as compared with the NCM system. In conclusion, this study showed that the combinations of vermiculite and perlite with different particle sizes made different physical and chemical properties, and some of these mixtures improved growth of potato plantlets in the NCM system.

The objective of this study was to investigate the growth characteristics of sowthistle (Ixeris dentata Nakai) under different levels of light intensity (photosynthetic photon flux, PPF), electrical conductivity (EC) of nutrient solution, and planting density for efficient production in a closed-type plant factory system. Growth and yield of the plants were analyzed at EC 1.5 and 2.0 dS·m−1 with PPF 100 and 200 μmol·m−2·s−1. Further, growth and yield were measured under four different planting densities: a 15-cm between-row distance with within-row distances of 10, 15, 20, and 25 cm. Shoot dry weight and leaf photosynthetic rate all increased with increasing EC and light intensity. Shoot fresh and dry weights, chlorophyll content, and leaf photosynthetic rate were maximal at EC 2.0 dS·m−1 with PPF 200 μmol·m−2·s−1. For all planting densities, number of leaves and leaf width were not significantly different. Shoot fresh and dry weights per plant were not significantly different, however, shoot fresh and dry weights per area decreased with increasing plating densities. A linear relationship was observed between the number of leaves and days after transplantation. Based on the results, we suggest a nutrient solution of EC 2.0 dS·m−1, PPF 200 μmol·m−2 · s−1, and planting density of 15×10 cm for maximal growth and yield of sowthistle in a closed-type plant factory.

Canopy photosynthesis has typically been estimated using mathematical models that have the following assumptions: the light interception inside the canopy exponentially declines with the canopy depth, and the photosynthetic capacity is affected by light interception as a result of acclimation. However, in actual situations, light interception in the canopy is quite heterogenous depending on environmental factors such as the location, microclimate, leaf area index, and canopy architecture. It is important to apply these factors in an analysis. The objective of the current study is to estimate the canopy photosynthesis of paprika (Capsicum annuum L.) with an analysis of by simulating the intercepted irradiation of the canopy using a 3D ray-tracing and photosynthetic capacity in each layer. By inputting the structural data of an actual plant, the 3D architecture of paprika was reconstructed using graphic software (Houdini FX, FX, Canada). The light curves and A/C i curve of each layer were measured to parameterize the Farquhar, von Caemmerer, and Berry (FvCB) model. The difference in photosynthetic capacity within the canopy was observed. With the intercepted irradiation data and photosynthetic parameters of each layer, the values of an entire plant's photosynthesis rate were estimated by integrating the calculated photosynthesis rate at each layer. The estimated photosynthesis rate of an entire plant showed good agreement with the measured plant using a closed chamber for validation. From the results, this method was considered as a reliable tool to predict canopy photosynthesis using light interception, and can be extended to analyze the canopy photosynthesis in actual greenhouse conditions.

Appropriate temperature control of seeds leads to uniform germination and efficient management of the production of seedling grafts, which are required for successful cultural practices. In this study, the base, optimum, and maximum temperatures of four hot pepper cultivars were used as scions as well as four hot pepper cultivars used as rootstocks were estimated using a quadratic model. Seeds of the cultivars were germinated in growth chambers at constant temperatures of 20°C, 25°C, 30°C, and 35°C. Cumulative germination was described using a logistic function. The base, optimum, and maximum temperatures were estimated by regressing the inverse of the time to 50% germination (1/GR50) against temperature. Although germination rates varied according to cultivar and temperature, the highest germination rates were observed at temperatures of 25°C and 30°C. Wongang 1 was the most tolerant at low temperature, whereas Koregon PR-380 and Wongang 1 were the most tolerant at high temperature. Further, we suggest appropriate combinations of scion and rootstock cultivars based on our cardinal temperature results for the eight hot pepper cultivars.

Irrigation frequency is one of the major factors required for adequate irrigation control in soilless culture. In order to investigate the effect of irrigation frequency on transpiration, growth, fruit yield, and water use efficiency, the paprika plants (Capsicum annuum L.) were compared under different irrigation frequencies based on solar radiation in soilless culture systems. The plants were grown on rockwool slabs following the vertical trellis “V” technique. Irrigation started whenever cumulative solar radiation reached the set value. Two set values of 120 J·cm−2 (high irrigation frequency, HIF) and 160 J·cm−2 (low irrigation frequency, LIF) were applied from 25 days after transplanting. Irrigation amount was controlled to keep a drain ratio at 20–30% of the total supply in order to avoid the salt accumulation in the root medium. Total water amount supplied to the plants in LIF was 94% of that in HIF. Transpiration in LIF or HIF was similar to that of estimated transpiration by Penman-Monteith equation, but slightly lower or higher value was observed, respectively. Leaf area and marketable fruit yield were not affected by the irrigation treatment. The both ratios of total transpiration to marketable fruit yield and total irrigation to marketable fruit yield were a little higher in LIF than in HIF. We concluded that water use efficiency in HIF was considered to be similar to or a little higher than that in LIF, but irrigation frequency did not affect the growth and production of paprika plants in open-loop system.

Modeling of crop transpiration is important to manage the irrigation strategy in soilless culture. In this study, the transpiration of paprika plants (Capsicum annuum L.) grown in rockwool was analyzed considering the relationship between incident radiation (RAD) and leaf area index (LAI). Coefficients of the simplified Penman-Monteith formula were calibrated in order to calculate the transpiration rate of the crop (Tr). Transpiration rate per floor area was measured by weighing plants with load cells. The following model was developed: Tr = a [1 − exp(−k × LAI)] × RAD /λ + b for estimating transpiration of paprika. Determination coefficient for the linear regression between estimations and measurements of daily transpiration was 0.80 with a slope of 0.93. In validation, the model showed high agreement between estimated and measured values of daily transpiration. Radiation showed a great effect on transpiration of paprika plants. The results indicated the simplified Penman- Monteith formula could be used to predict water requirements and improve irrigation control in soilless culture. However the model coefficients require parameter adjustments for specific climate and crop conditions.

Temperature is a principal environmental factor that directly affects the growth and timing of appearance for crop leaves. To estimate the leaf number and leaf area of ‘Seoul’ pak-choi plants (Brassica campestns ssp.chinensis), we applied the concept of growing degree-days GDD=(Tavg-Tbase) × days, where Tavg, Tbase and days were the daily average air temperature, base temperature, and days after transplanting, respectively. Leaves that were beginning to unfold with a leaf area ≥1 cm2 were counted every 2 to 3 d. Linear relationships were found between leaf number and days after transplanting as well as between leaf number and GDD. The rate of appearance and the number of leaves per GDD were 0.542 leaves d-1 and 0.051 leaves oC-1 d-1, respectively. In contrast, the relationship was non-linear between leaf number and leaf area, with the latter being calculated as [(128.9+11.6×GDD-0.03×GDD2)/1+(0.051×GDD+3.5) /13.7)-3.9] (cm2oC1 d-1). Using model validation, we found that the estimated leaf number and leaf area showed strong agreement with measured values. our results demonstrate the usefulness of modeling to estimate total leaf area and growth from hydroponically grown pak-choi plants.

Photosynthetic models of crops are essential for predicting the optimum CO2 concentrations that should be maintained for crop productivity in closed systems throughout the growth period. The objective of this study was to develop a canopy photosynthetic model of romaine lettuce (Lactuca sativa L., cv. Asia Heuk romaine) incorporating CO2 concentration and plant growth stage. The canopy photosynthetic rates of the plants were measured 4, 7, 14, 21, and 28 days after transplanting using closed acrylic chambers, in which the temperature was maintained at 24°C and a 200 µmol · m -2 · s-1 light intensity was provided by an 8:1:1 ratio of RBW light-emitting diodes. The canopy photosynthetic rate of the lettuce was calculated by measuring the reduction in CO2 within the chamber over time, from an initial concentration of 2,000 µmol · mol -1. The canopy photosynthetic rate became saturated as the CO2 concentration increased, while it exponentially decreased with the plant growth stage. Among the previously published models available, the Thornley model was suitable for the expression of the canopy photosynthetic rate; however, it had to be adapted to take into account growth stage, resulting in an R2 of 0.985. The canopy photosynthetic rates estimated by the models showed good agreement with those actually measured (R2 = 0.939). Based on these results, the established model may be helpful in determining the optimum level of CO2 required for crop production and in calculating the decreasing CO2 requirements throughout the cultivation period.