Light is a critical factor regulating plant development and productivity under controlled environment conditions. However, the light conditions are often kept static throughout the cultivation period, potentially overlooking plants' dynamic responses to changing environmental stimuli over time. This study proposes a stage-specific optimization strategy to maximize lettuce growth, based primarily on shoot fresh weight by adjusting red:green:blue (R:G:B) light ratio at different growth stages. After transplanting 2-week-old seedlings, their growth period was divided into an early stage (ES, the first 2 weeks) and a late stage (LS, after 2 weeks). To account for potential carry-over effects, the ES optimization was designed to evaluate how early-stage light conditions influence final growth performance. Response surface methodology was then employed to identify the optimal spectral combinations for each stage. The optimal R:G:B light ratios were determined to be 44.2:55.8:0 for ES and 25.2:57.8:16.9 for LS. These results suggest that excluding B light during ES promotes morphological traits favorable for light interception, presumably at the expense of immediate photosynthetic efficiency, and ultimately supporting enhanced biomass accumulation during LS. A sequential-optimized lighting strategy combining these two stage-specific light ratios was then evaluated against other lighting strategies, including a static-optimized, a reference, two white LED treatments with different color temperatures of 2700 and 5000 K. While the static-optimized treatment with an R:G:B ratio of 77:23:0 produced the highest shoot fresh weight during ES, the sequential-optimized ultimately delivered the greatest biomass by the end of the growth stage. These findings highlight the importance of stage-specific light requirements and demonstrate that dynamic light management aligned with developmental physiology can significantly enhance crop productivity. This study provides a practical framework for implementing adaptive light strategies in controlled environment systems.

Crop growth information is collected through destructive investigation, which inevitably causes discontinuity of the target. Real-time monitoring and estimation of the same target crops can lead to dynamic feedback control, considering immediate crop growth. Images are high-dimensional data containing crop growth and developmental stages and image collection is non-destructive. We propose a non-destructive growth prediction method that uses low-cost RGB images and computer vision. In this study, two methodologies were selected and verified: an image-to-growth model with crop images and a growth simulation model with estimated crop growth. The best models for each case were the vision transformer (ViT) and one-dimensional convolutional neural network (1D ConvNet). For shoot fresh weight, shoot dry weight, and leaf area of lettuce, ViT showed R2 values of 0.89, 0.93, and 0.78, respectively, whereas 1D ConvNet showed 0.96, 0.94, and 0.95, respectively. These accuracies indicated that RGB images and deep neural networks can non-destructively interpret the interaction between crops and the environment. Ultimately, growers can enhance resource use efficiency by adapting real-time monitoring and prediction to feedback environmental controls to yield high-quality crops.

This study was conducted to develop a nutrient solution for coastal glehnia, evaluate the performance of the newly developed nutrient solution, and determine an adequate electrical conductivity (EC) level for growth and bioactive compounds production in controlled environment agriculture (CEA). Coastal glehnia plants cultivated in Hoagland nutrient solution with EC 1, 2, 3, 4, and 5 dS·m−1 for 20 weeks had the same ratio of cations and anions in terms of macro essential elements. Based on the ratio, a new nutrient solution for coastal glehnia was developed. Subsequently, seedlings with two main leaves were grown in Hoagland nutrient solution (H1 and H2; EC 1 and 2 dS·m−1) or a newly developed nutrient solution (N1–5; EC 1–5 dS·m−1) for 23 weeks (about 6 months), and the leaves were harvested every 5 weeks. The N1 treatment resulted in significantly higher accumulated and average shoot fresh and dry weights than in the H1 and H2 treatments. In addition, the total phenolic content and antioxidant capacity per shoot were the highest under the N1 treatment. Individual bioactive compounds, such as xanthotoxin, bergapten, and imperatorin, levels per shoot with the N1 treatment were significantly higher than those with the H1 and H2 treatments. These results demonstrate that the newly developed nutrient solution of EC 1 dS·m−1 increases the biomass and bioactive compound levels of coastal glehnia and is suitable for cultivating coastal glehnia in CEA, such as vertical farms and greenhouses.

Anions are molecules that have gained one or more extra electrons, and oxygen anions are the anions most commonly present in the atmosphere. Several studies have reported an improvement in plant respiration and growth through the application of air anions in several plants. In this study, the effect of air anions on the growth of lettuce was explored, and further, the economic feasibility of this technique was analyzed in a plant factory. Two cultivars of lettuce were cultivated for 4 weeks with the application of negatively ionized air in a commercial plant factory. The exposure to air anions improved the growth of the lettuce plants in the plant factory. A profitability analysis of applying air anions revealed that the annual net profit per 1500 m2 cultivation area was about USD 60,000 and USD 70,000 for red leaf lettuce and Lollo bionda lettuce, respectively. Therefore, the application of air anions to lettuce in plant factories or greenhouses could increase crop production and has high economic feasibility.

Light is a critical factor regulating plant development and productivity under controlled environment conditions. However, the light conditions are often kept static throughout the cultivation period, potentially overlooking plants' dynamic responses to changing environmental stimuli over time. This study proposes a stage-specific optimization strategy to maximize lettuce growth, based primarily on shoot fresh weight by adjusting red:green:blue (R:G:B) light ratio at different growth stages. After transplanting 2-week-old seedlings, their growth period was divided into an early stage (ES, the first 2 weeks) and a late stage (LS, after 2 weeks). To account for potential carry-over effects, the ES optimization was designed to evaluate how early-stage light conditions influence final growth performance. Response surface methodology was then employed to identify the optimal spectral combinations for each stage. The optimal R:G:B light ratios were determined to be 44.2:55.8:0 for ES and 25.2:57.8:16.9 for LS. These results suggest that excluding B light during ES promotes morphological traits favorable for light interception, presumably at the expense of immediate photosynthetic efficiency, and ultimately supporting enhanced biomass accumulation during LS. A sequential-optimized lighting strategy combining these two stage-specific light ratios was then evaluated against other lighting strategies, including a static-optimized, a reference, two white LED treatments with different color temperatures of 2700 and 5000 K. While the static-optimized treatment with an R:G:B ratio of 77:23:0 produced the highest shoot fresh weight during ES, the sequential-optimized ultimately delivered the greatest biomass by the end of the growth stage. These findings highlight the importance of stage-specific light requirements and demonstrate that dynamic light management aligned with developmental physiology can significantly enhance crop productivity. This study provides a practical framework for implementing adaptive light strategies in controlled environment systems.

Crop growth information is collected through destructive investigation, which inevitably causes discontinuity of the target. Real-time monitoring and estimation of the same target crops can lead to dynamic feedback control, considering immediate crop growth. Images are high-dimensional data containing crop growth and developmental stages and image collection is non-destructive. We propose a non-destructive growth prediction method that uses low-cost RGB images and computer vision. In this study, two methodologies were selected and verified: an image-to-growth model with crop images and a growth simulation model with estimated crop growth. The best models for each case were the vision transformer (ViT) and one-dimensional convolutional neural network (1D ConvNet). For shoot fresh weight, shoot dry weight, and leaf area of lettuce, ViT showed R2 values of 0.89, 0.93, and 0.78, respectively, whereas 1D ConvNet showed 0.96, 0.94, and 0.95, respectively. These accuracies indicated that RGB images and deep neural networks can non-destructively interpret the interaction between crops and the environment. Ultimately, growers can enhance resource use efficiency by adapting real-time monitoring and prediction to feedback environmental controls to yield high-quality crops.

This study was conducted to develop a nutrient solution for coastal glehnia, evaluate the performance of the newly developed nutrient solution, and determine an adequate electrical conductivity (EC) level for growth and bioactive compounds production in controlled environment agriculture (CEA). Coastal glehnia plants cultivated in Hoagland nutrient solution with EC 1, 2, 3, 4, and 5 dS·m−1 for 20 weeks had the same ratio of cations and anions in terms of macro essential elements. Based on the ratio, a new nutrient solution for coastal glehnia was developed. Subsequently, seedlings with two main leaves were grown in Hoagland nutrient solution (H1 and H2; EC 1 and 2 dS·m−1) or a newly developed nutrient solution (N1–5; EC 1–5 dS·m−1) for 23 weeks (about 6 months), and the leaves were harvested every 5 weeks. The N1 treatment resulted in significantly higher accumulated and average shoot fresh and dry weights than in the H1 and H2 treatments. In addition, the total phenolic content and antioxidant capacity per shoot were the highest under the N1 treatment. Individual bioactive compounds, such as xanthotoxin, bergapten, and imperatorin, levels per shoot with the N1 treatment were significantly higher than those with the H1 and H2 treatments. These results demonstrate that the newly developed nutrient solution of EC 1 dS·m−1 increases the biomass and bioactive compound levels of coastal glehnia and is suitable for cultivating coastal glehnia in CEA, such as vertical farms and greenhouses.

Anions are molecules that have gained one or more extra electrons, and oxygen anions are the anions most commonly present in the atmosphere. Several studies have reported an improvement in plant respiration and growth through the application of air anions in several plants. In this study, the effect of air anions on the growth of lettuce was explored, and further, the economic feasibility of this technique was analyzed in a plant factory. Two cultivars of lettuce were cultivated for 4 weeks with the application of negatively ionized air in a commercial plant factory. The exposure to air anions improved the growth of the lettuce plants in the plant factory. A profitability analysis of applying air anions revealed that the annual net profit per 1500 m2 cultivation area was about USD 60,000 and USD 70,000 for red leaf lettuce and Lollo bionda lettuce, respectively. Therefore, the application of air anions to lettuce in plant factories or greenhouses could increase crop production and has high economic feasibility.

This study investigated the effects of UV-A and UV-A-closed visible light (deep-blue [DB]) on the growth and bioactive compound accumulation of green and red perilla. Four-week-old seedlings were cultivated in an environment control room under visible light with red, blue and white LEDs for 4 weeks and then were continuously grown under supplemental UV-A (365 nm and 385 nm) and DB (415 nm and 430 nm) lights for 7 days. UV-A and DB treatments did not enhance the growth characteristics of green perilla compared with the control; while these treatments enhanced the growth parameters of red perilla, and the values were highest in DB 415 nm. The photosynthesis rate of both cultivars showed similar trends as the growth results of each cultivar. The electron transport rate and maximum quantum yield of both cultivars were reduced under UV-A 365 nm, while these values were maintained in DB treatments. In both cultivars, total phenolic, antioxidant capacity, rosmarinic and caffeic acids and perillaldehyde levels were enhanced in DB treatments, whereas UV-A 365 nm and DB 415 nm increased the total anthocyanin content. Overall, supplemental DB 415 nm and 430 nm was suitable for improving the growth and biochemical accumulation of both perilla cultivars.

Selecting the optimal light in Controlled Environment Agriculture (CEA) is crucial for crop yield and quality. However, the optimal light conditions can only be identified through extensive testing. This study proposes a methodology for selecting the optimizing light conditions and applies it to a medicinal herb Glehnia littoralis. Using a mixture design and response surface methodology with red (R), green (G), and blue (B) based LEDs, the optimal light spectrum was identified as R:B = 7:5 based on growth parameters (shoot fresh weight, number of leaves, leaf area, and shoot area). Verification experiments showed that R:B = 7:5 enhanced growth through R and B light synergy, while the high proportion of blue light stimulated secondary metabolism, leading to increased biomass and accumulation of medicinally valuable compounds-imperatorin, bergamottin, and coumarin. These findings highlight the potential of using tailored artificial light spectra in CEA to overcome cultivation challenges and optimize yield and quality, contributing to the development of a stable and sustainable scalable production system for high value medicinal plants.

Environmental control in crop production typically relies on real–time sensor data to monitor environmental conditions. However, the data does not reflect the immediate physiological status of crops, limiting the precision of crop-based environmental management. To enhance crop productivity, quality, and resource use efficiency, it is necessary to collect direct information about crop health status and reflect this in crop–specific environmental control. Plant electrical signals, which reflect physiological responses to external environment, can be used as biosensors for crop health. Here, electrical signals from lettuce seedlings were measured under different temperature and lighting conditions. Machine learning techniques were applied to both raw and extracted feature signals to classify the electrical responses to each environmental parameter. Lettuce seedlings were exposed to different air temperature conditions, with and without light. The electrical signals were detected through electrode needles inserted into the stems of the seedlings under each condition. Gaussian smoothing, moving average, and peak–to–peak methods were used to remove noise from raw electrical signals and extract the features, which were then used in a machine learning model to classify the temperature and light environments. The results showed that decision tree model achieved high F1 scores of 0.9778 for temperature and 0.9316 for lighting conditions. Additionally, evaluating the generalisation performance of the model by adding another lettuce variety resulted in F1 scores confirmed the robustness of model. These findings suggest that plant electrical signals can be used to monitor crop health in complex environments, offering the potential for more precise environmental management in crop production.

본 연구는 수직농장에서 상업적 생산에 적합한 왜성 토마토를 대상으로 음이온을 처리하고, 생육단계별(영양생장, 생식생장) 생육 특성과 기체교환 활성 반응을 분석하여, 과채류에대한 음이온의 적용 가능성을 평가하고자 수행되었다. 실험을 위해 왜성 토마토 묘를 온도 24도, 습도60%, 이산화탄소농도 600μmol·mol-1, 광도 320μmol·m-2·s-1 PPFD, white LED, 광주기16h의 수직농장 모듈에서 5주간 재배하였다. 음이온은 광주기 시간동안 12 × 105ions∙cm-3의 음이온을 처리하였다. 정식 후 5주 동안 매주 생육조사를 진행하였고, 정식후 4주 동안 매주 광합성율과 증산율을 측정하였다. 정식 1주차 광합성율은 음이온 처리구에서 유의적으로 높게 나타났으나, 이후에는 광합성율과 증산율 모두 처리구간 유의 차이는확인되지 않았으나, 전반적으로 음이온 처리구가 대조구보다높은 수치를 나타내는 경향을 보였다. 영양생장 지표들은 처리간 차이가 크지 않았으나, 생식 기관의 발달(화방수, 꽃수,과실수)은 대조구에 비해 유의적으로 높은 값을 나타냈다. 이러한 결과는 음이온 처리가 과채류인 왜성 토마토의 생식생장을 촉진하고 과실 생산량을 증가시켰음을 확인하여, 과채류에 음이온의 적용 가능성을 확인하였다. 향후에는 음이온 처리 농도의 최적화 및 수확한 과실의 품질 비교를 포함한 후속연구가 필요하다.

This study investigated the effects of supplemental far-red (FR) lighting on photomorphogenic responses and productivity in Crepidiastrum denticulatum, a native Korean medicinal plant, grown in vertical farms using artificial lighting. Photomorphogenesis refers to the growth and developmental changes in plants in response to light signals. The background light sources were white (W) light-emitting diodes (LEDs) and red, green, and blue (RGB) mixed LEDs. Supplemental FR lighting was added to both background light sources at a red to far-red (R/FR) ratio of 1.2, referred to as W + FR and RGB + FR treatments. Fluorescent light (FL) served as a control. Plants were cultivated under five light treatments for six weeks in a controlled environment with an air temperature 20°C, relative humidity 60%, photosynthetic photon flux density 200 µmol·m–2·s–1, photoperiod 16 h·d–1, and CO2 concentration 1000 µmol·mol–1. The addition of supplemental FR lighting significantly influenced photomorphogenic response, enhancing plant growth by increasing leaf area and biomass by up to 2.7 times compared to the control. Canopy net photosynthetic rates were significantly increased by supplemental FR lighting, especially under the W + FR treatment. While total phenolic content and antioxidant capacity per unit dry weight were highest under the RGB treatment, the W + FR treatment exhibited the highest accumulation of these metabolites per shoot due to increased biomass. These findings suggested that supplemental FR light effectively modulates photomorphogenic responses, enhancing growth and bioactive compound accumulation in C. denticulatum. The results indicate the potential of manipulating light quality, particularly by adding FR light, to optimize vertical farming practices for medicinal plants.