ASHS 2025 Conference

Basil (Ocimum basilicum L. Genovese) is a highly valued and economically important herb with high culinary and medicinal qualities. Light intensity and photoperiod are the most influential environmental parameters affecting its growth, morphology, and biomass production under controlled environments. This study aims to evaluate the impact of gradually increasing light intensity and photoperiod on the growth and yield of basil while the total daily integral was the same at the end of cultivation. Four different treatments were used: (T1) constant light intensity (300 µmol m⁻² s⁻¹) and constant 16 h photoperiod (Control), (T2) constant light intensity with an increasing photoperiod (14 -16 -18 h), (T3) constant photoperiod (16 h) with an increasing light intensity (200 - 300 - 400 µmol m⁻² s⁻¹), and (T4) both dynamic light intensity and photoperiod increasing over time. The treatments were applied for 24 days in a growth chamber equipped with a drip hydroponic system, and the treatment dynamic changes were implemented every 8 days. Plants grown under increasing photoperiod and light intensity (T4) exhibited better morphological characteristics, more significant biomass accumulation (fresh and dry weight), and light use efficiency, measured as the proportion of light absorbed by PS II used in biochemistry than the other treatments. The results emphasize the relevance of adaptive lighting to optimize basil growth in indoor farming. Dynamic optimization of lighting can increase the utilization efficiency of light with positive implications for vertical farming and hydroponics cultivation. Future studies should explore the nutritional and olfactory profile to refine adaptive lighting approaches for vertical farming and hydroponic systems. Keywords: Basil, dynamic lighting, photoperiod, indoor farming, biomass accumulation, hydroponics.

Basil (Ocimum basilicum) is a widely cultivated culinary and medicinal herb valued for its aroma, flavor, and nutraceutical properties. During hydroponic greenhouse production, precise regulation of air and nutrient solution temperatures plays a crucial role in enhancing yield and nutritional quality. Basil's inherent sensitivity to temperature makes it crucial to optimize these factors, as they have a significant impact on its bioactive metabolite profile. This study aimed to determine the impact of air and nutrient solution temperature on bioactive metabolites in hydroponically grown sweet basil to maximize accumulation. In a greenhouse sweet basil ‘Nufar’ were propagated in ebb-and-flow hydroponic systems for two weeks then transplanted into deep-water culture hydroponic systems and grown for three weeks. Air temperatures ranged from 20 to 30°C with a 5°C difference in day and night temperature and deep-water culture nutrient solution temperatures ranged from 15 to 35°C. At harvest, total phenolics (TPs), total flavonoids (TFs), and antioxidant activity including ABTS (2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)), FRAP (ferric reducing antioxidant power), and DPPH (2,2-Diphenyl-1-picrylhydrazyl) were measured. Air and nutrient solution temperature interacted to influence all parameters measured, with nutrient solution temperature exerting a greater influence on metabolite accumulation and antioxidant activity than air temperature. As air temperature increased from 20.3 to 28.5°C, TPs and TFs decreased by 40% and 58%, respectively, while ABTS, DPPH, and FRAP antioxidant activity decreased by 18%, 67%, and 53%, respectively. Similarly, increasing the nutrient solution temperature from 14.6 to 24.9°C resulted in a 76%, 87%, and 84% decline in TPs, TFs, and FRAP antioxidant activity, respectively. A greater increase in nutrient solution temperature from 14.6 to 30.0 and 32.3°C led to a 95% reduction in DPPH and 57% reduction in ABTS antioxidant activity, respectively. Thus, if enhancing phenolics, flavonoids, and antioxidant activity of sweet basil ‘Nufar’ is a primary production goal, maintaining an air temperature of ~23°C and a low nutrient solution temperature of ~14°C is an effective strategy.

Basil (Ocimum spp.) is a widely cultivated aromatic herb known for its culinary, medicinal, and industrial applications. The composition of volatile organic compounds (VOCs) that give basil their characteristic aroma and flavor is greatly influenced by environmental conditions, particularly temperature. Hydroponic cultivation in greenhouses allows precise control over air and nutrient solution temperatures, providing an optimized system for studying the effects of temperature on VOC profiles. This study investigated the effect of air and nutrient solution temperature on sweet basil volatilomes. An untargeted volatilomics approach was used to characterize key volatile compounds in sweet basil influenced by the temperature during hydroponic production. Sweet basil (Ocimum basilicum ‘Nufar’) were propagated in ebb-and-flow hydroponic systems for two weeks then transplanted into deep-water culture hydroponic systems and grown for three weeks. Air temperatures ranged from 20 to 30°C with a 5°C difference in day and night temperature, and deep-water culture nutrient solution temperatures ranged from 15 to 35°C. At harvest, VOCs from leaf extracts were analyzed using gas chromatography–mass spectrometry (GC-MS). A total of 86 volatile compounds were identified across all treatments, encompassing aliphatic hydrocarbons, aliphatic alcohols, aliphatic acids, aromatic acids, aliphatic ketones, aromatic ketones, aliphatic aldehydes, aliphatic amines, esters, volatile phenylpropanoids, acid anhydrides, silicones, and furans. Among these, aliphatic hydrocarbons were the most abundant (48%), followed by aliphatic alcohols (22%). Multivariate statistical analyses, including principal component analysis (PCA), partial least squares–discriminant analysis (PLS-DA), and Pearson correlation-based heatmaps, were used to determine the key VOCs influenced by air and nutrient solution temperature interactions. PLS-DA analysis determined 18 candidate volatile metabolites with variable important projection (VIP) scores higher than > 1.5 as the significant discriminant for air and nutrient solution treatments. These findings will contribute to optimizing hydroponic production strategies for enhancing basil’s aromatic profile in controlled environment production.

Current practices aim to produce quality containerized culinary herbs at the end of greenhouse production, but the effects of fertilization choices during production on the post-production performance of these crops in the retail and consumer environment are unclear. This study aimed to quantify the effects of fertilizer type, source, and concentration applied during the greenhouse production phase on the post-harvest performance of containerized culinary herbs during the retail and consumer phases. Seedlings of sweet basil (Ocimum basilicum ‘Nufar’) were transplanted into 11.4 cm-diameter containers filled with certified organic soilless substrate compromised of peat moss and coarse perlite and irrigated with solutions containing 100, 200, or 300 mg∙L –1 N from a conventional or organic water-soluble fertilizer (WSF) starting at transplant and throughout the end of the greenhouse phase, seedlings were; or were transplanted into the same organic substrate with amended with 0.25, 0.5 or 0.75 kg N∙m-3 from conventional controlled-release (CRF) or organic slow-release fertilizer (SRF) and irrigated with clear tap water Plants were grown in three different phases: 1) in a greenhouse for 21 d with 22°/18° day/night air temperatures and 12 mol∙m–2∙d –1 daily light integral (DLI) to simulate the greenhouse production phase; in a growth chamber for 7 d at 20° constantly with a DLI of 1 mol∙m–2∙d –1 to stimulate the retail phase; and, after harvesting shoots above the second node, an additional 21 d in a growth chamber with the same conditions to simulate the consumer phase. One-third of the plants were harvested at the end of each phase and data was collected. During production, conventional WSF produced plants 1.3-5.7 cm taller than all other treatments, but by the consumer phase there were no differences across all fertilizer treatments. The optimum fertilizer type and concentration for basil varied between conventional and organic sources. Fresh mass of basil was greatest for plants receiving conventional WSF, which were 4-9.5 g greater than plants which received conventional CRF. However, plants receiving organic SRF had a fresh mass which was 2.1-3.9 g greater than plants receiving organic WSF treatments. Fertilizer treatments did not affect the rate of biomass accumulation, but the phase did. The relative growth rate was lowest in the consumer phase compared to the greenhouse production and retail phases. The results of this study indicate fertilizer type, source, and concentration do not impact containerized basil growth and development in the post-harvest consumer environment.

Basil (Ocimum basilicum) and sage (Salvia officinalis) are some of the most popular fresh cut culinary herbs, but little information is available on how to cost-effectively maximize their growth and development in controlled environments. Given that cut herbs are sold by fresh mass, the goal is to maximize harvestable fresh mass, while not increasing production time, space, or energy inputs. Therefore, our objective was to determine the most effective root-zone temperature (RZT) in combination with carbon dioxide (CO2) concentration and reduced air temperature (AT) to maximize culinary herb yield. Seeds of basil ‘Genovese’ and sage were sown into 200-cell (2.5 cm × 2.5 cm) rockwool plugs and germinated for two and four weeks, respectively. Twelve seedlings of each species were transplanted into each of six 250 L, 0.9-m-wide by 1.8-m-long deep-flow hydroponic tanks among three walk-in growth chambers. Plants were grown under a total photon flux density of 260 µmol∙m–2∙s–1 for 16 h. The nutrient solution within the tanks was heated to 24, 28, or 32 °C. Additionally, AT and CO2 concentration setpoints of 20 and 23 °C and of 450 and 900 μmol∙mol‒1, respectively, were maintained for a total of 12 treatments. Basil and sage were harvested three and four weeks after transplant, respectively. Of AT, RZT, and CO2, AT was the largest contributing factor to shoot fresh mass (SFM) accumulation for both species. Increasing the air temperature from 20 to 23 °C resulted in a SFM increase of 100 and 180% in sage and basil, respectively. SFM of sage was not influenced by increasing CO2 from 450 to 900 μmol∙mol‒1 and resulted in a 12% decrease in basil SFM. However, at the high CO2 concentration, specific leaf area was 4 and 12% lower for sage and basil, respectively, resulting in greater biomass accumulation per cm2 of leaf area. RZT had no effect on basil SFM, but SFM of sage was greatest when the nutrient solution was heated to 24 and 28 °C. By maintaining an AT of 23 °C, RZT of 28 °C, and CO2 concentration of 450 μmol∙mol‒1, the SFM of both basil and sage can be maximized without further increasing RZT or CO2 concentration.

The hydroponic industry is valued at close to 1 billion dollars in North America and is expected to grow over the next 5 years. Hydroponic crop production in controlled environments has the advantage of year-round production opportunities and has been well-established for some vegetable crops, such as cucumber (Cucumis sativus) and tomato (Solanumlycopersicum). One area for growth includes edible flowers which have potentially increased use in the medical field for human health benefits and culinary arts as ingredients and garnishes. Considering the limited information about edible flower hydroponic production, we initiated research to evaluate two popular hydroponic production methods for three different edible flower species; dahlia (Dahlia xhybrida ‘Figaro Red Shade’), zinnia (Zinnia elegans ‘Zesty Scarlet’), and dianthus (Dianthus chinensis ‘Venti Parfait’). These species were grown in three treatments: two hydroponic systems, deep water culture (DWC) and nutrient film technique (NFT), and a traditional peat-based substrate. Plants were fertilized with General Hydroponics FloraSeries using the medium feed nutrient schedule. Data collected included plant biomass, flower biomass, and antioxidant and polyphenol concentrations. After 14 weeks, dahlia and zinnia grown in the DWC system produced significantly more plant biomass, flower numbers, and flower biomass compared to the NFT and substrate treatments. Dahlia plants in DWC also flowered ~ 10 days earlier than the other treatments. No significant differences were observed with dianthus plants between the treatments, except for lower flower numbers and flower fresh weight for NFT compared to the DWC and substrate treatments. Phytochemical analysis for antioxidant composition using 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays and polyphenolic content through Folin-Ciocalteu assays will be conducted. The results of our initial study suggest that growing dahlia and zinnia on DWC hydroponic systems in our applied conditions has potential as an edible flower production system. However, dianthus may not be suitable for hydroponic system production, or additional modifications to hydroponic systems need to be evaluated to determine feasibility.

Domestic production of ginger is increasing, as it is used in a variety of culinary and medicinal applications due to its unique flavor and potential health benefits. However, some growing parameters, such as growing media and fertigation levels, have not yet been optimized for containerized production. Therefore, the objective of this study was to evaluate the growth and rhizome yield of ginger (Zingiber officinale) using different soilless substrates and nutrient levels under greenhouse conditions. Two separate experiments were conducted, each lasting six months. In Experiment 1, six substrates were evaluated: 100% coir (control), 100% peat, peat-bark mixtures at 75%-25%, 50%-50%, and 25%-75%, and 100% bark. In this setup, 1-2 sprouted ginger rhizomes were transplanted into each 12 L nursery container and harvested after 3 and 6 months of transplanting. In Experiment 2, five nitrogen-based nutrient levels (50, 100, 200, 300, and 500 ppm N) were evaluated. In this setup, 1-2 sprouted ginger rhizomes were transplanted into grow bags filled with coconut coir pith and husk chips. In both experiments, treatments were arranged as completely randomized design with six replicates. Physical growth parameters, such as the number of stems, relative chlorophyll content, number of roots, unemerged buds, and fresh and dry weight of stems, roots, and new rhizomes, were measured. According to the data from Experiment 1, no significant differences were observed among the substrates, except for the fresh and dry weight of stems and the dry weight of roots at mid-harvest in the peat-bark 25%-75% combination. In contrast, nutrient level significantly influenced all ginger growth parameters except chlorophyll content. Ginger grew well under low nutrient levels (50 to 100 ppm N). The overall growth differences between 50 and 500 ppm N ranged from 6% to 68%. For example, the fresh and dry weight of new rhizomes were 65.7% and 49.1% greater at the 50 ppm N nutrient level, respectively. The results demonstrated that ginger plants prefer well-draining substrates with low nutrient levels under controlled-environment production.

Hydroponic production systems with recirculating nutrient solutions are routinely monitored and adjusted to maintain a target pH value. Supra-optimal or sub-optimal pH values can lead to nutrient deficiencies or toxicities, respectively, reducing crop quality and yields. The objective of our research was to determine appropriate nutrient solution pH ranges for herbs grown in recirculating nutrient solutions. Two week old seedlings of basil (Ocimum basilicum ‘Nufar’), and three week old seedlings of dill (Anethum graveolens ‘Hera’), parsley (Petroselinum crispum ‘Giant of Italy’), and sage (Salvia officinalis), grown in phenolic foam cubes were transplanted into one of six deep-flow technique (DFT) systems in a greenhouse with different pH treatments. Treatments consisted of pH setpoints of 4.5, 5.0, 5.5, 6.0, 6.5, and 7.0. DFT systems contained nutrient solutions made with tempered municipal water supplemented with a complete water-soluble fertilizer (16N-2.2P-14.3K) to maintain a target electrical conductivity of 2.0 dS·m–1. The nutrient solution pH was maintained through a dosing system using 2% sulfuric acid and 2% potassium hydroxide as the acid and alkali, respectively. One-third (by vol.) of the nutrient solution was renewed with freshly mixed 16N-2.2P-14.3K fertilizer each week of production to ensure adequate nutrients in the nutrient solution. Greenhouse target environmental conditions consisted of day and night temperatures of 22 °C and 18 °C respectively, and a daily light integral of 12 mol∙m–2∙d–1. The optimal pH for culinary herb growth varied by species. Basil fresh mass was optimized at pH of 5.5 and decreased by 41.7% when grown at pH 7.0 compared to basil grown at pH of 5.0. Basil grown at pH of 7.0 was 3.38 cm shorter than plants grown at pH of 6.0. Similarly, dill and parsley had the greatest fresh mass when grown at pH of 5.0 and 5.5, respectively, and fresh mass was reduced by 40% and 33 %, respectively, when grown at pH of 7.0 compared to their optimum pH. In contrast, sage growth increased with pH, with a 17% increase in fresh mass between pH of 4.5 and 7.0. The results of this study indicate herbs may be able to grow throughout a broader range of pH values than originally thought, if nutrients do not become limited. Furthermore, when possible, hydroponic culinary herb producers can group species with similar pH requirements to maximize yields.

Regulating the microclimate to achieve the desired crop quality and yield demands substantial resource consumption, making it essential to optimize resource use. AI models can be used to forecast future plant development based on microclimate conditions, allowing controllers to preemptively adjust climate settings to optimize growth and resource consumption. However, the current paradigm of microclimate controller lacks AI-assisted feedback to predict how crops respond to dynamic climate conditions (crop × environment interactions). Thus, there is an urgent need to develop an AI-assisted predictive analytics system that can support decision-making processes. This study presents a multimodal deep learning approach for forecasting lettuce growth in CEA using both microclimate (aerial and rootzone) and early-stage plant image data. We employed Long Short-Term Memory (LSTM) networks to model the temporal dependencies of microclimate variables such as temperature, humidity, and light intensity. Further, we integrated image and microclimate data into the multimodal growth predictor to enhance T-days ahead prediction accuracy by capturing visual and temporal cues of plant growth and development. The model effectively predicted the lettuce growth trend using multimodal data, achieving high accuracy in its forecasts for the next few days. The combined use of LSTM and image data provides an efficient framework for forecasting lettuce growth, offering valuable insights for optimizing resource use in CEA.

Greenhouse tomato production with high-wire system and indeterminate tomato cultivars facilitates year-round production with high quality and productivity. However, maintaining optimal climate conditions in greenhouse is expensive due to high operational costs. Optimizing climate control strategies requires in-depth understanding of controlling systems, outdoor climate, and plant physiology. But skilled and experienced growers may not always be available. Artificial intelligent-driven climate control (AI) has been emerged as a potential solution. Yet, few trials have conducted, which may not be at an equivalent scale as the industry and following the industry standard. To address this gap, we compared AI and conventional climate control strategies (human decision-based; CV) for greenhouse tomato production in two identical high-tech greenhouse compartments (namely, AI and CV each with 481.7 m²) over 145 days after the final transplanting with management practices established by commercial growers. Each compartment had 420 plants of the indeterminant cultivar Maxxiany at a planting density of 3 plants m⁻². The AI algorithms were developed using datasets from commercial growers and a digital twin via physiology-informed neural network (photosynthesis and transpiration modules). Leaf pruning in AI was determined based on weekly light integral below canopy (Kim and Kubota, 2025), while CV followed conventional pruning based on harvesting trusses. To evaluate the performance of AI, parameters for crop development, yield, and fruit quality were collected in addition to environmental conditions and resource usage for lighting, cooling, heating, and fertigation. AI maintained relatively higher day and night temperature with high heating pipes temperature and keeping windows closed. AI also resulted in more leaves within canopy from fewer leaf pruning compared to CV. Those contributed to increase in cumulative irrigation volume (936 vs. 785 l m⁻² for AI and CV) and thus total fertilizer use (878 vs. 639 g m⁻²). AI used more natural gas for heating (190 vs.79 MJ m⁻²) and more electricity for supplemental lighting (91.4 vs. 80.4 kWh m⁻²). However, AI had higher cumulative yield (9.3 ± 0.3 vs. 8.5 ± 0.3 kg m⁻²) and greater PAR-based productivity (grams of fruits per PAR mol; 4.1 vs 3.6 g mol⁻¹). These findings suggest that AI increased resources use (water, fertilizer, natural gas, and electricity) but also resulted in higher yields as a trade-off. Further optimization of AI’s algorithms regarding fertigation and heating strategies may improve economic feasibility of AI application in greenhouse tomato production.

In controlled environment agriculture (CEA), accurate yield forecasting remains challenging due to reliance on environmental sensor data, which fails to capture plants’ dynamic morphological responses to growth conditions. This study bridges the gap by establishing a vision-based framework to forecast plant growth dynamics through automated phenotyping and time-series modeling. A plant phenotype monitoring framework was implemented using commercially available cameras and off-the-shelf deep learning-based models (YOLO). The robustness of the YOLO and time-series models was evaluated under various treatment conditions, including salt stress and variations in root architecture, in hydroponic greenhouse trials across two seasons. Top-view images of the plants were collected using GoPro and Raspberry Pi cameras, and different YOLOv8 instance segmentation model variants were trained on four image datasets to extraction of morphological traits such as area, major, and minor axes. Results indicated that YOLOv8 generalized well, achieving mAP50 for bounding boxes and masks in the range of 0.897 – 0.952 and 0.896 – 0.947, respectively. Plants with split root systems exhibited superior growth under the highest salt stress levels compared to single-root systems. Comparisons between physical measurements and image-derived parameters such as major and minor axes yielded high R² values of 0.85 and 0.92 for single-root systems, and 0.90 and 0.84 for split root systems. Additionally, the area parameter obtained from images showed an R² of 0.882 when compared with plant fresh weight. Area parameters were forecasted using an ARIMA model over 2-, 4-, and 8-day windows, evaluated using MAPE. The lowest MAPE values (3.99 in the fall and 1.70 in the spring) were attained by single-root plants under salt stress when projected for two days. The forecasted area values demonstrated R² values of 0.623, 0.671, and 0.75 for the 2-, 4-, and 8-day forecast windows respectively when compared with fresh weight, indicating that the area parameter is a reliable predictor of yield. These findings confirm that morphological changes capture environmental influences and can be reliably forecasted, introducing a scalable, data-driven method to predict yield in CEA while helping growers optimize resource usage and reduce productivity risks.

Efficient supplemental lighting control is crucial for optimizing crop productivity and energy use in controlled environment agriculture (CEA). While environmental factors such as temperature and carbon dioxide (CO2) concentration significantly influence photosynthesis, current lighting control strategies rely solely on ambient sunlight levels. To address this limitation, a chlorophyll fluorescence (CF)-based biofeedback system has been proposed to dynamically adjust LED light intensities based on real-time photosynthetic responses. However, frequent CF measurements using pulse-amplitude modulated (PAM) fluorometers can induce severe photoinhibition due to repetitive saturating light pulses, limiting long-term application. This study explores an alternative approach by developing a machine learning model to estimate the quantum yield of photosystem II (ΦPSII) from environmental parameters, eliminating the need for the fluorometer and continuous physical measurements. Four-week-old green and red lettuce (Lactuca sativa) cultivars (‘Casey’ and ‘Cherokee’) were grown in a greenhouse for a month, where ΦPSII was measured every 15 minutes using a fluorometer (Monitoring-PAM; Heinz Walz, Effeltrich, Germany) alongside environmental data, including extended photosynthetically active radiation, temperature, CO₂ concentration, and vapor pressure deficit. A linear regression model was developed to estimate ΦPSII, generating cultivar-specific equations that were integrated into the biofeedback system for LED control. The estimated ΦPSII values exhibited a strong correlation with the measured data, allowing the biofeedback system to optimize lighting without the risk of photoinhibition associated with frequent PAM fluorometer measurements. This approach enabled dynamic light adjustment based on environmental conditions and lettuce cultivar, with the regulated light levels closely aligning with direct measurements. These findings highlight the potential of integrating predictive models into the biofeedback-controlled lighting systems, offering a cost-effective and non-invasive alternative to direct CF measurements for precision lighting management in CEA.

Using luminescent quantum dot (QD) films as greenhouse coverings offers a novel approach to enhancing plant growth by modifying the light spectrum. This study evaluates the effects of novel QD glass on the growth, morphology, and yield of butterhead lettuce (Lactuca sativa cv. butterhead) in a greenhouse setting. Two identical greenhouses were employed: one fitted with a QD film and the other with conventional glass, serving as a control. Lettuce seedlings were cultivated in a deep-water culture hydroponic system, with continuous monitoring of key environmental parameters—including temperature, relative humidity, CO₂ concentration, and light spectrum. After four weeks of growth, various morphological traits were assessed, such as canopy diameter, leaf count, total leaf area, and fresh and dry biomass. Results indicated that lettuce grown under the QD glass displayed enhanced leaf development and significantly higher biomass accumulation, with a 37% increase in fresh weight and a 27% rise in dry weight compared to the control. The spectral modifications induced by the QD film, especially the conversion of blue photons to red wavelengths, likely contributed to these improvements in plant morphology and productivity. These findings highlight the potential of QD glass to boost greenhouse lettuce production by increasing radiation capture and biomass accumulation.

Incorporation of quantum dots within greenhouse films has the potential to enhance local food production with a reduced carbon footprint, without compromising yield or quality. Silicon quantum dots in particular hold advantages over other photoluminescent nanoparticles in that they have low toxicity and are highly tunable. The down-shifting of photons observed under silicon quantum dot films can enhance vegetative productivity of plant commodities, but due to a relatively low photon emission efficiency of the films, the transmitted light to crop canopies below is reduced. A growth model has been used to predict the performance of lettuce grown under a silicon quantum dot spectrum, but no studies have been conducted to validate these predictions. Our study aimed to evaluate the yield and physiological performance of Lactuca sativa cv. ‘Rex’ grown in controlled environment growth chambers fit with tunable 11-channel LEDs which were used to match the color fraction of a solar spectrum transmitted through glass greenhouse glazing or a solar spectrum transmitted through a silicon quantum dot film. Light intensity levels of 500 and 350 µmol m−2 s−1 were also tested to simulate the expected 33% loss of light transmission through the silicon quantum dot film at a density of 5 wt%. The spectrum and light intensity treatments were tested in a factorial design for a total of four treatments, with each treatment replicated five times. Fresh biomass results from the growth chambers showed that growth model predictions underestimate the performance of ‘Rex’ under the mock silicon quantum dot spectrum. The elimination of UV-A photons and enrichment of red and far red photons in the mock silicon quantum dot treatment increased leaf area and growth at their respective light intensities compared to the mock solar spectrum; however, the yield of the 350 µmol m−2 s−1 mock silicon quantum dot spectrum did not surpass that of the 500 µmol m−2 s−1 mock solar spectrum. This research highlights the importance of coupling solar cells with silicon quantum dot films to increase their economic feasibility and further illuminates the effects of down-shifted spectra on lettuce physiology.

Energy costs are typically the second largest operational cost for greenhouses behind labor and these costs are increasing over time. Energy use varies greatly between operations based on their geographic location, type of technology, months of operation, and type of crops grown. Energy benchmarking is a process used for many commercial buildings whereby energy performance of facilities are quantified. The information can be used by operations to better understand their energy use relative to their peers and can help identify opportunities for energy efficiency improvements and cost savings. The Greenhouse Lighting and Systems Engineering (GLASE) consortium leads a project with a goal of benchmarking energy use in 40 greenhouse operations in New York State. The process began with implementing a database tool with EnSave’s FEAT (farm energy audit tool) specific to greenhouse operations. The tool allows energy efficiency contractors to enter information from farm site visits on: building dimensions and properties, equipment usage (including HVAC and lighting), past utility bills, crops grown and months of the year they are grown. The database tool outputs a benchmarking report to give operations a clear understanding of energy use (total energy, energy use intensity and on a per square foot production space and per crop unit basis). Through New York State Energy Research and Development Authority (NYSERDA), funding was made available for up to 80 greenhouses in New York State to participate. More than 40 operations have now enrolled in the project. Findings will be presented on the initial results. Challenges in reporting include the diversity of types of operations (with different types of products produced) and in many diversified farms there are not specific energy meters relative to greenhouses vs. other diversified farm activities. Nevertheless the results provide a baseline of energy use intensity in New York State greenhouses.

While Controlled Environment Agriculture (CEA) continues to expand rapidly across North America, the U.S. lacks a unified national organization to represent, support, and connect greenhouse growers. In contrast to Canada and the Netherlands, which benefit from strong national-level grower associations, American growers remain fragmented across states, commodity groups, or scale-specific networks. The existing groups tend to be state-specific, crop-specific, or focused on suppliers and hobbyists—leaving a major gap for commercial growers who need actionable support and a unified voice. This fragmentation further limits access to shared knowledge, economic leverage, and consistent representation in research and policy. To address this, we propose the creation of the United Greenhouse Growers Association (UGGA), a national, grower-led association designed to support collaboration, knowledge-sharing, and improved market efficiency. Initial development will begin in Kentucky, where the University of Kentucky has already mapped CEA activity across the state, providing a strong foundation for data-driven outreach, pilot engagement, and program testing. What distinguishes this initiative is its emphasis on practicality, inclusion, and tangible value. Rather than serving as a passive affiliation, the UGGA will offer direct support through group purchasing programs, collective marketing strategies, access to shared services, and the translation of academic research into usable tools. There will be the opportunity for the UGGA to set training and certification standards for professional growers which will give guidance to trade schools and colleges. Membership will be kept affordable and low-barrier, intentionally structured to welcome small and mid-sized growers alongside larger operations. Most critically, the organization will be led by growers themselves—not just vendors or researchers—ensuring the priorities reflect real operational challenges and opportunities. The society will address national gaps that existing groups often overlook: the need for peer-to-peer knowledge on transitions from soil to substrate, crop management under protected/controlled environments, strategies for reducing the isolation of growers in low-density CEA states, and creating a network that supports national-scale coordination without losing local relevance. The UGGA structure would also allow for cross-state collaboration and integration with USDA priorities around regional supply chain resilience and U.S. producer support. This abstract proposes launching an organizing committee to begin outreach, host stakeholder roundtables in Kentucky and beyond, define founding principles, and formalize UGGA’s nonprofit framework in preparation for national rollout.