A previously developed algorithm controls on/off decisions for greenhouse supplemental light fixtures and retractable shade curtains to achieve a target daily light integral (DLI). The algorithm, termed LASSI (Light and Shade System Implementation) originally used high pressure sodium (HPS) lights with a 1-hour time step to avoid the warm-up time and reduced lifespan of HPS bulbs when they are frequently turned on/off. We have updated the algorithm accounting for dimmability of light emitting diodes (LEDs) for which light intensity can be adjusted in near real-time (RT LASSI). The objective of this study was to compare performance of lettuce ‘Rex’ and Rouxai’ and tomato ‘Sweetelle’ in response to the LASSI algorithm with HPS fixtures vs. RT LASSI with dimmable white LEDs. Experiments were conducted in adjacent greenhouses and DLI setpoints were 17 mol·m-2·d-1 for lettuce and 25 mol·m-2·d-1 for tomatoes. RT LASSI greenhouses had white LEDs (TSR Grow TG-600 HVR) and LASSI greenhouses had HPS fixtures (PL Light 1000 W). For treatments with RT LASSI, when supplemental lighting was called for, LED treatments were adjusted to complement sunlight to achieve a target instantaneous light intensity of 300 and 400 µmol·m-2·d-1 for lettuce and tomatoes, respective, averaged over a 10 minute interval. For tomatoes a minimum 4-hour dark period was imposed while for lettuce, supplemental lighting could occur anytime within the 24-hr period. For lettuce there were three replicate, 35 d crop cycles and for tomatoes plants received 15 weeks of treatment after reaching the fruiting stage with no replication. Both algorithms controlled DLI close to target. For lettuce, LASSI with HPS led to larger plant height and volume and increased fresh weight (but not dry weight) vs. RT LASSI with LED. For tomatoes, RT-LASSI with LED led to about a 30% greater tomato yield vs. LASSI with HPS. Increased yield was associated with increased fruit size but not increased fruit or truss number. Brix of HPS grown fruit was higher than LED fruit. While air temperature was very similar between both treatments, HPS fixtures may have increased plant temperature of LED. More research is needed to determine if plant impacts were due to type of lighting fixture and associated plant temperature and light spectrum or to the control algorithm itself (spreading supplemental lighting across greater hours per day).
The surging demand for sustainable agriculture has accelerated the adoption of indoor vertical farming as a pragmatic solution. Lettuce, a cornerstone crop in this context, assumes significant importance. Accurate forecasting of lettuce yield is indispensable for optimizing resource allocation and ensuring a steady supply. Most existing models used either environmental data or images to predict yield predictions, which could be erroneous for complex systems. This study aims to improve the accuracy of yield prediction in indoor farming settings with a hybrid model. First, we applied the feedforward neural network and random forest models for yield prediction, leveraging data from environmental sensors, cultivation practices, and historical yield records. Then, a convolutional neural network model is tailored to forecast yield using image data captured by RGBD cameras. Based on our results, we found reasonable accuracy in terms of RMSE and MAE, which range between 10-25 gm and 28-49 g, respectively. By amalgamating these diverse models, we aim to elevate yield prediction accuracy. It’s hypothesized that the proposed hybrid model would outperform individual approaches, offering invaluable insights for indoor vertical farming operations decision-making.
Sweetpotato (Ipomoea batatas) production relies on clonal propagation of either sprouted storage roots grown in plant beds or from cuttings from greenhouse grown plants. In the USA, the Sweetpotato National Clean Plant Network Centers (SP-NCPN) provide virus-free planting stock of important cultivars for sweetpotato growers. However, most of the virus-indexed materials can be rapidly re-infected by one or more viruses within one growing season via insect vectors. Production of enough virus-indexed propagules is a major challenge and annually shortages exist. We have developed a controlled environment agriculture (CEA) technology that provides rapid propagation of virus-indexed propagules from SP-NCPN foundation stocks within 6 – 12 months. From a single virus-indexed in vitro plantlet, 500 rooted plants were obtained in 3 months compared to 3 plants via tissue culture micropropagation. Within 3 months the 500 rooted plants generated about 250,000 plants. Field performance (establishment and yield) of slips and rooted transplants from the CEA technology were compared with traditional plant bed derived slips. No significant differences were detected for establishment and canner and cull yield, whereas differences were found for total, marketable, jumbo, and US no. 1 yield. Our results offer a potential solution for providing growers a readily available source of virus-indexed propagule source that are comparable to field grown slips.
Maintaining a target pH range is important for root zone management and overall plant growth and quality. Commercial soilless substrates often contain liming amendments to increase initial substrate pH to between 5.5 and 6.2. Hydroponic nutrient solutions are less-well buffered than soilless substrates and can experience pH drift in the absence of frequent monitoring and adjustment. Hydroponic deep water culture (DWC) was explored for strawberry (Fragaria × ananassa) research studies, to more-easily collect root growth parameters and root samples for elemental analysis, compared to soilless substrate culture. However, an effective strategy for pH management needed to be developed. The objective of this study was to develop a protocol for growing strawberry mother plants hydroponically. First, three hydroponic systems (drip-irrigated coarse perlite, drip-irrigated sand, and DWC) were compared to a peat-based soilless substrate control. Plants received a modified strawberry nutrient solution (Yamazaki) at a nitrogen (N) concentration of 100 mg·L-1. Plants grew similarly across the four growing systems. Deep water culture provided the easiest access to clean roots; however, root zone pH decreased
Dissolved oxygen (DO) level in hydroponic solution is an important factor affecting plant root development and water and nutrient uptake. However, precisely controlling the DO level in hydroponics has always been difficult due to the direct linkage of solution temperature and oxygen concentrations, especially under different aeration methods. Besides potentially controlling solution temperature, using liquid oxygen fertilization such as hydrogen peroxide (H2O2) has been shown to burst increase DO concentration in the solution, and ozonation, which is a sanitization treatment, has the potential to adjust DO level by supplying oxygen in nutrient solution. Our objective was to evaluate the effects of different DO levels and oxygenation strategies in a hydroponic system for the optimal growth of kale (Brassica oleracea) and arugula (Eruca vesicaria). In this study, we used ozone generators and hydrogen peroxide (H2O2) as a DO enrichment method in addition to the air pump-based aeration system to test the effects of different DO levels – low, medium, high as 6, 9, 12 mg/L, respectively – on kale ‘KX-1’ and ‘Red Russian’, and arugula ‘Astro’ and ‘Esmee’ grown in a deep water culture system. Treatment without using ozone generators or H2O2 was assigned as control. The study was arranged as a completely randomized design with three replications. DO and temperature probes were connected to a datalogger to trigger ozone generators and H2O2 injection using a relay once the DO levels were below the set thresholds. Weekly measurements were taken for plant height, leaf and anthocyanin chlorophyll content. The final harvest additionally measured leaf area, shoot and root biomass the leaf soluble solids content, titratable acidity, and leaf nutrient concentration. Plants grown under a high DO level had a higher root-to-shoot ratio, but the overall higher plant yield was achieved under the medium DO level. This system demonstrated that precise DO level control could be achieved using a sensor-based system.
In the evolving landscape of controlled environment agriculture (CEA), precise temperature management remains a pivotal factor in enhancing the growth, development, productivity, and quality of high-value leafy greens. Our research identifies the cardinal temperatures — base (Tb), optimum (Topt), and maximum (Tmax) — for red-leaf and green butterhead lettuce (Lactuca sativa), arugula (Eruca sativa), and kale (Brassica oleracea), comparing how both constant mean daily temperature (MDT) within a greenhouse and positive day-night temperature differences (DIF) in a growth chamber influence plant growth and development. In the greenhouse, we had a constant MDT of 8, 13, 18, 23, 28, and 33 °C under a photosynthetic photon flux density (PPFD) of 220 µmol∙m‒2∙s‒1 for 12 h∙d–1, while in the growth chamber we targeted the same MDTs with air day/night (12 h/12 h) set points of 11/5 °C, 16/10 °C, 21/15 °C, 26/20 °C, 31/25 °C, or 36/30 °C under a PPFD of 300 µmol∙m‒2∙s‒1. Both arugula and kale had greater biomass accumulation at lower Tb and Topt compared to lettuce, suggesting a propensity for growth under a cooler MDT. Specifically, the Topt for fresh mass accumulation was found to be at 24.7 °C for arugula, 22.9 °C for kale, and higher for lettuce cultivars 'Rex' and 'Rouxaï RZ' at 24.7 and 26.2 °C, respectively. We found that DIF exerted minimal influence on these crops, emphasizing the critical role of MDT in influencing their developmental outcomes. Additionally, our research provides insight into the impact of temperature on various physiological and morphological parameters, such as leaf unfolding rate, biomass accumulation, and susceptibility to physiological disorders such as bolting or tipburn. This study underscores the importance of precise temperature management in CEA, offering guidance for producers seeking to optimize energy use while maximizing crop yield and quality.
