HCS8830

Data-driven management of greenhouse high-wire fruiting vegetables using 3D scanning

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Original paper: Ohashi, Y., Y. Ishigami, and E. Goto. 2020. Monitoring the growth and yield of fruit vegetables in a greenhouse using a three-dimensional scanner. Sensors. 20: 5270. doi:10.3390/s20185270

Controlled environment agriculture (CEA) for food crop production allows high productivity and efficient resource use. Applications of CEA have been expanding rapidly worldwide. However, the current limitation that slows the expansion is shortage of experienced human workers. In addition, increasing costs of labor are serious problem that slows the potential growth of CEA. More automation is needed to lower the labor input in various tasks of crop management. Automated monitoring of plant growth and morphology (structure) can allow growers to make more science-based, data-driven decisions for crop management and minimize labor, energy, water, and other resource usage.

Three-dimensional scanning technologies have developed rapidly to create a digital twin (model) for various measurements and computational analyses. There are affordable technologies and various data processing software.  The authors of this paper are a team of horticultural engineers at Chiba University (Matsudo, Japan) and demonstrated usage of 3D scanning for greenhouse crop management. Their specific objective was to estimate key metrics of plant growth and productivity, such as leaf area, plant height, biomass, and fruit yield for three major fruiting crops grown in greenhouse (tomato, cucumber, and sweet (bell) pepper).

The tests were conducted in a greenhouse located in Chiba University. Different ages (sizes) of tomato, cucumber and pepper plants of selected cultivars are used for the tests. A hand-held 3D scanner (DPI-8X, Opt Technologies, Tokyo) was used to scan individual plants as well as a group (canopy) of six plants grown using a typical soilless cultivation method with a common density applied in commercial greenhouses. The scan generated a set of many data points with xyz coordinates (point cloud) which are then converted to a digital ‘surface model’ for finding leaf area and a digital ‘solid model’ for finding fruit yield. The leaf areas were then used for finding biomass (dry weight) of leaves and whole plants using predetermined correlation between these variables. However, accuracy of estimation of leaf area and plant biomass for tomato was less than that for cucumber and pepper, due to more complex leaf morphology (compound leaves with many leaflets) of tomato than the other crops.

The authors also estimated the leaf areas at different heights of canopy, demonstrating the capacity of quantifying leaf distributions inside the canopy.  This information is especially useful for finding levels of available light at various heights inside the canopy. The information of light distribution in the canopy will help growers use more data-driven crop management practices including leaf pruning, plant density management, so that light use efficiency can be maximized.

Yield prediction based on 3D scans showed reasonable correlation with measured yield (R2 > 0.7) for tomato and pepper. The RGB data obtained by the scanner was used for detecting ripe fruit, whose point cloud data were converted to sold (voxel) models. Fruit volume estimated by the solid model was then converted to fruit mass (grams) by a predetermined fruit density (ratio of mass and volume). Cucumber was not scanned for fruit analysis as fruit shapes were extracted based on colors (red or yellow) and fruit color of cucumbers is green (difficult to distinguish from leaf images).  Similarly, immature (unripe) fruits of tomato and pepper were not extracted either for the same issue of color-based recognition.  Overall, fruit yield estimate was least accurate compared with leaf area and other metrics. The inaccuracy is based on the difficulty to scan the opposite side of fruit.

Although dense canopy and overlapping leaves add difficulty in achieving high accuracy, this preliminary effort to demonstrate the potential use of a 3D scanner for crop management was successful. Coefficient of determination (R2) was almost always high (>0.8) except LAI for tomato.  The tools (hardware and software) that they used for scanning and data conversions were commercially available. Although more work will need to be done to improve the accuracy and perhaps to streamline the logistics of measurement and data processing, this is a valuable demonstration of what we can do with a simple handheld 3D scanner or similar tool in future greenhouse crop production.

Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production

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Citation

Hemming, S., de Zwart, F., Elings, A., Righini, I., & Petropoulou, A. (2019). Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production. Sensors, 19(8), 1807. doi.org/10.3390/s19081807 

Background

As the global population keeps growing, so does the demand for healthy, fresh, and accessible food. Greenhouse environments contribute an important function in providing fresh food at a high production rate while maximizing resource efficiency. The greenhouse industry has stumbled over the obstacle of finding enough skilled staff to manage these crop production systems. Modern high-tech greenhouses are equipped with process computers, and, to add more automated control, greenhouse climate and crop models have been developed. The use of Artificial Intelligence (AI) has reached breakthroughs in many areas and has not been used yet to control climate and irrigation and make crop management decisions for growing a greenhouse crop autonomously. An international challenge was conducted on autonomous greenhouses in 2018 at the high-tech research greenhouses of Wageningen University and Research in cooperation with five multi-disciplinary international teams to combine the use of modern AI algorithms and the greenhouse crop production of cucumbers cultivar “Hi-Power”. This article aims to describe the results of the teams in terms of optimizing crop yields and net profit using state-of-the-art AI algorithms for cucumber production.

Methodology

The experiments were conducted in six identical greenhouse compartments equipped with standard motor movers. Cucumber seedlings were grown in rockwool substrate cubes and placed on slabs on hanging gutters. The five participating teams were: Sonoma, iGrow, deep_greens, The Croperators, and AiCu. These teams were able to remotely control the motors’ activity in their greenhouse compartment by using their own original AI algorithm, varying in design and techniques. As part of the competition, a sixth team led by Dutch growers controlled a greenhouse compartment and served as a reference. The competing teams used their original AI algorithms to regulate the climate and irrigation setpoints through a central computer and operated in the greenhouse compartments accordingly. Standard sensors set up in the greenhouse compartments continuously measured data and calculations were made based on these and digitally sent back to the teams. Teams were also allowed to install additional sensors in their compartments at the start of the experiment. Three harvest quality parameters were established: A: no defects (>375g), B: defects in shape, color, or others (300-374g), and C: less than 300g per fruit. Harvest data was measured manually by greenhouse staff and digitally sent to the teams.

The teams were judged based on three criteria: Sustainability, based on resource use efficiency (20%), Net profit, based on the number of fruits harvested per price of the fruit and category (50%), and AI algorithm, based on originality and efficacy of the algorithm (30%). Two models were used to analyze and compare the different AI algorithm approaches since the operation resulted in differences in cropping, climate, irrigation strategies, harvest yields, and resource use efficiencies. However, the combined model did not represent the presence and effects of pests and disease. The combined model was carried out to compare the calculated output as the predicted fresh cucumber yield per greenhouse compartment versus the realized cucumber yield in the same greenhouse compartment to verify the models. Additionally, model calculations were carried out applying the cropping strategy, lighting strategy, and climate strategy of other teams for each greenhouse compartment to predict the changes in yield and compare these.

Figure 2 – Scheme of data exchange (Hemming et al., 2019)

Results and Discussion

Out of all the teams, including the Dutch growers, the Sonoma team resulted in the highest production of cucumbers and consistently did this throughout the experiment. They predicted that by having a high daily light integral, they would be able to achieve a greater harvest, as they did, focusing their algorithms on this. Other teams decided to increase daily light integral and maintain a low carbon dioxide concentration (The Croperators) or have lower daily light integrals in the beginning to minimize fruit abortion (Dutch growers) which resulted in lower yields but led to a greater understanding of important production factors. All teams began the experiment with a relatively low amount of carbon dioxide, where most teams increased their concentration throughout the experiment and later diminished their dosage towards the end, the Sonoma team decided to increase their carbon dioxide concentration continuously throughout the total cropping period.

The goal of maximizing crop management is finding the optimum combination of the climate, lighting, and cropping strategies. This can be achieved by attaining the greatest number of fruits per area without hindering photosynthesis. Light, for example, is the foundation for healthy growth. For this particular experiment, having higher light integrals resulted in higher yields for the teams. As predicted with the combined models, the teams iGrow, AiCu, and growers could have possibly achieved a higher yield if they would have used the lighting strategies of either team Sonoma or The Croperators. Overall, most of the teams were able to obtain a decent production using a low amount of resources while reaching a net profit close to or better than manual commercial growers.

The goal of this experiment was to see how AI algorithms would become better at making crop management decisions and their effects on the greenhouse climate and production. At the end of the cucumber crop’s growth period, all the algorithms developed for this experiment resulted in establishing a successful greenhouse environment. As this was the first successful experiment on remotely controlling a greenhouse for cucumber production using artificial intelligence algorithms, the authors demonstrated that AI algorithms can compete, and even outperform, experienced manual growers. Artificially assisted or managed greenhouse systems could be a useful tool to grow crops where there is limited knowledge. In the future, more developments and studies in this area will be needed to make AI an alternative for trained and skilled greenhouse workers and growers, which to this day, greenhouses cannot function without.

Blue Radiation Interacts with Green Radiation to Influence Growth and Predominantly Controls Quality Attributes of Lettuce

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Citation

Meng, Q., J. Boldt, and E.S. Runkle. 2020. Blue radiation interacts with green radiation to influence growth and predominately controls quality attributes of lettuce. Journal of the American Society for Horticultural Sciences 145(2):75-87. https://doi.org/10.21273/JASHS04759-19

With recent shifts in modern agriculture to more urban environments, indoor farming has become increasingly popular. Such an environment allows growers to control everything from air flow to water. Arguably the most important aspect that can be controlled is lighting, specifically with the use of light-emitting diodes (LEDs) that allow for customizable wavelengths for varying stages in a plant’s life cycle. Research has shown that different wavelengths can produce different results. For instance, exposing certain varieties of lettuce to blue radiation (400-500 nm) has been tied to a significant reduction in biomass weight, as well as an increase in the production of secondary metabolites. Growers will often combine blue with red and far-red radiation (600-800 nm) to achieve desired results. Green radiation (500-600 nm), however, does not have much of a history of being used by growers, despite its ability to penetrate deep into the leaves. This has huge implications on its ability to drive photosynthesis and has recently been studied as a substitute for blue radiation. Previous research on this subject has shown an increase in biomass of several lettuce varieties, but the authors believe this could have been attributed to the shifting levels of blue radiation. To combat this, they designed a new experiment to keep levels of blue radiation constant and substitute red radiation for green.

 

In this experiment, researchers focused on ‘Rouxai’ red leaf lettuce and tested the effects of varying wavelengths using LED lighting. During the light quality treatment phase of the experiment, each treatment had a 20 hour photoperiod and a total photosynthetic photon flux density (PPFD) of 180 μmol m-2 s-1 . They exposed the lettuce to nine different treatments of lighting, including combinations of blue and red radiation, as well as introducing green radiation at a photon flux density (PFD) of 60 μmol m-2 s-1 in place of a reduction in red radiation. Researchers measured biomass accumulation including fresh and dry mass, different morphological features such as plant diameter and leaf number, and coloration of the foliage. They found that at 20 μmol m-2 s-1 of blue radiation, the presence of 60 μmol m-2 s-1 green radiation increased the fresh mass of lettuce, but had negative effects on the weight at any higher levels of blue radiation. Additionally, an increase in blue radiation with 60 μmol m-2 s-1 of green radiation decreased leaf diameter, width, and length. With an increase in blue radiation, lettuce is known to have an increase in red color of the foliage. Without green radiation, the foliage became saturated at 20 μmol m-2 s-1 of blue radiation. However, with 60 μmol m-2 s-1 of green radiation, the saturation point increased to 60 μmol m-2 s-1 of blue radiation. This has interesting implications for growers wishing to increase the coloration of their foliage.

 

This study provides valuable information regarding the role green radiation may have in a plant’s life cycle. With an increase in fresh mass at low levels of blue radiation, incorporating green radiation at the right stages could potentially increase yield for growers. Additionally, the presence of green light has been shown to vary depending on the species and age of the crop, which implies further research is needed on the subject. Having this research and knowledge that green radiation influences photosynthesis and other varying characteristics of lettuce growth is critical for growers looking to optimize their lighting in controlled environments.

Welcome

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Welcome to 2021 HCS 8830, ‘Current research topics on controlled environment plant physiology and technology’!

Effects of continuous or end-of-day far-red light on tomato plant growth, morphology, light absorption, and fruit production

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Citation

Kalaitzoglou, P., W. van Ieperen, J. Harbinson, M. van der Meer, S. Martinakos, K. Weerheim, C.C.S. Nicole, and L.F.M. Marcelis. 2019. Effects of continuous or end-of-day far-red light on tomato plant growth, morphology, light absorption, and fruit production. Frontiers in Plant Science 10: 322 https://doi.org/10.3389/fpls.2019.00322

Background

LEDs are becoming increasingly common in modern controlled environment horticultural systems. The potential energy-savings and flexibility of LEDs makes them attractive lighting options compared to traditional high-pressure sodium or metal halide bulbs. Questions remain regarding the effects of light wavelengths emitted by LEDs on plant growth. Plants respond to differing wavelengths of light by changing physical characteristics and growth. Shading causes a low red light (R) to far-red light (FR) ratio (R:FR), resulting in low levels of the active form of a key plant photoreceptor, phytochrome. This low phytochrome stationary state (PSS) leads to a variety of shade avoidance responses that affect plant morphology and development. In contrast, LEDs used for greenhouse lighting often emit low levels or zero far-red light, causing plants to have higher PSS values than sunlight. Little is known about the effects of changing R:FR ratios on photosynthesis and plant growth in greenhouses using LEDs. Using tomato as a model crop, researchers at Wageningen University investigated how tomato morphology changes in response to higher than sunlight R:FR ratio supplied by LEDs and what effect these changes have on plant light absorption and growth (Kalaitzoglou et al., 2019). In addition, the researchers were interested if a short end-of-day FR treatment (EOD-FR) could compensate for any negative effects of growing plants without FR light during daytime.

Methods

The researchers conducted two experiments (EXP1 & EXP2). In both experiments, greenhouse chambers were divided into 15 equal compartments, each containing 20 tomato plants. Each compartment was illuminated by a combination of red (95%) and blue (5%) LEDs that supplied approximately 150 mmol
m-2 s-1 of photosynthetically active radiation (PAR) over the course of a 16 hour day. Additional FR LEDs were installed to provide five different treatments based on FR intensities. In four treatments, both FR LEDs and red/blue LEDs were on at the same time during the day, resulting in plant PSS values of 0.70, 0.73, 0.80, 0.88. The fifth treatment was a 15 minute end of day FR cycle (EOD-FR) following the end of the photoperiod. To investigate the interaction between the effects of FR and solar radiation on plant morphology the first experiment (EXP1) used a blackout screen to block incoming sunlight, while the second experiment (EXP2) exposed the plants to broadband solar radiation from morning to afternoon. In addition, EXP1 lasted only four weeks after transplanting while EXP2 was extended to 16 weeks to allow for tomato fruit development.

In both experiments, researchers measured plant growth and morphology traits such as plant height, petiole angle, and leaf area. In EXP2, fruit traits including total fruit weight (g/plant), number of fruits, and number of open flowers at 4 weeks were recorded. Additional measured traits included leaf ligh absorbance and chlorophyll and carotenoid content. To simulate light absorption for each treatment, researchers constructed a 3D plant model using GroIMP software (Hemmerling et al., 2008). Researchers used the model to estimate the effects of changes in plant morphology in response to FR light treatments on plant light absorption.

Supplemental Figure S5. (Kalaitzoglou et al., 2019)

Results

Increasing R:FR ratio to levels above sunlight had a negative impact on tomato plant growth. In both experiments, morphological parameters such as plant height and leaf area decreased as PSS values became higher (Supplemental Figure S5). The researchers concluded that lower levels of plant light absorption in high PSS treatments were primarily caused by the decrease in leaf area, ultimately reducing plant growth. Similar results were observed for fruit characteristics in which fruit size and fruit number were greater in treatments with increasing FR compared to the 0.88 PSS treatment (no FR) and EOD-FR treatment. FR treatments also stimulated early flower and fruit maturity. Interestingly, while leaf PAR absorbance and chlorophyll content were lower in low PSS treatments, net photosynthesis was higher. Researchers attributed this result to the Emerson effect in which a higher rate of photosynthesis occurs when plants are exposed to a simultaneous mixture of red and far-red light. In both studies, EOD-FR treatments were not enough to offset the negative effects of growing plants with low levels or zero FR light throughout the day. In conclusion, the results of the study indicated that the presence of FR light increased fruit yield and accelerated flowering and FR LEDs could be a beneficial addition to greenhouses to improve tomato fruit production.

Energy, water and nutrient impacts of California-grown vegetables compared to controlled environmental agriculture systems in Atlanta, GA

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Energy, water and nutrient impacts of California-grown vegetables compared to controlled environmental agriculture systems in Atlanta, GA

Steven W. Van Ginkel, Thomas Igou, Yongsheng Chen*School of Civil and Environmental Engineering, Georgia Institute of Technology, 200 Bobby Dodd Way, Atlanta, GA 30332, United States.

Citation

Van Ginkel, S.W., T. Igou, and Y. Chen. 2017. Energy, water and nutrient impacts of California-grown vegetables compared to controlled environmental agriculture systems in Atlanta, GA. Resources, Conservation and Recycling 122:319-325. Https://doi.org/10.1016/j.resconrec.2017.03.003 (Links to an external site.)

 Background

This paper compares the efficiency of California Based traditional vegetable agriculture to hydroponics and aquaponics systems. Efficiency is defined by water usage, energy and nutrient input as it relates to crop yield. California is the leader in fruit and vegetable agriculture; therefore the rest of the United States is reliant on their system. However, California is also susceptible to severe drought, which can lead to reduce yields. Additionally, California has very large watershed, which can cause runoff of fertilizers in ponds, lakes and other bodies of water. Therefore to mitigate the environmental footprint of agriculture production, the author’s suggests that future generations focus on urban agriculture. Aquaponics is a system that allows the production of vegetables and fish, while reducing the input of fertilizers, and using waste byproducts as the source of nutrients. The authors show that this system reduces the nutrient input, water usage and is more productive that traditional based vegetable production. Therefore the purpose of this paper is to compare and contrast the productivity of each system.

 Experimental Design

California vegetable data was derived from www.casestudies.ucdavis.edu. Data was taken for several crops including tomato, spinach, strawberries, peppers, and broccoli. The data displays yield, nutrient input, energy input for each crop. The data was normalized by dividing each component by yield per acre. For hydroponics, there was one grower who grew lettuce and leafy greens in shipping containers. The data was normalized for energy (lighting and cooling) and water usages over a year divided by yield per container. For aquaponics, there were three growers, one from Hawaii and two from University of Virgin Islands and Atlanta, GA. All growers used deep-water culture and grew leafy greens. The data was normalized for energy and water utilized over a year divided by the yearly productivity. Data from all three systems was then compared using statistical analysis.

 Results

Areal Productivity

When comparing hydroponics and aquaponics there was no significant difference in the areal productivity. However, there was a significant difference between the ponic-systems and the California-based system. Ponic-systems were found to be 10 to 29 times more productive than the California-based system. In addition, areal productivity in hydroponics could be substantially improved increasing vertical production in closed environments.

Energy Usage

Hydroponics uses 30 times more energy (lighting, cooling) than the California-based system. There was no significant difference in energy usage between aquaponics and California-based system. However there were differences in energy usage between aquaponic growers, therefore it is would be wise to compare each aquaponic grower to the California-based system in the future.

 Water Usage

California-system uses 66 and 8 times more water than hydroponics and aquaponics. There were differences in water usage between hydroponics and aquaponics, however the authors suggests that results maybe skewed due to the lack of data points.

Conclusion

Based on the authors study, it seems that ponic-systems are overall more efficient than California-based system. They believe that these systems should be integrated into urban cities. By integrating such systems, cities become less reliant on vegetable and fruit production from California. At the same time it reduces the negative environmental footprint. Nevertheless, the biggest challenge will be to address the socio-economic challenges in integrating the system into urban environments.

Chlorophyll Fluorescence Biofeedback System

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Citation

van Iersel, M. W., Weaver, G., Martin, M. T., Ferrarezi, R. S., Mattos, E., & Haidekker, M.

(2016). A Chlorophyll Fluorescence-based Biofeedback System to Control Photosynthetic Lighting in Controlled Environment Agriculture, Journal of the American Society for Horticultural Science J. Amer. Soc. Hort. Sci., 141(2), 169-176. https://doi.org/10.21273/JASHS.141.2.169

Background

Controlled environment agriculture (heavily managed, indoor production facilities that include greenhouses, growing facilities, and indoor farms) has become an increasingly important part of agriculture in the world.  But, it is expensive as electricity and resources in these systems can quickly push up produce price. It makes sense to optimize energy costs to produce the most food (edible biomass) possible. By reducing inefficiency, it might be able to optimize the system to maximize the economics of this industry.

The authors chose to examine chlorophyll fluorescence as a way to address this problem. When light energy encounters a plant, the leaves absorb the light to power the electron transport chain (ETC – a system that produces sugar, the primary food for plants). As this happens, some of the light is reflected or absorbed and does not help power the ETC. One of the byproducts is a fluorescent excitation of molecules that can be measured and accurately predict how well the plant incorporates light into the ETC versus how much light is lost due to efficiency. Research has shown that measuring this can help predict the efficiency of photosystem II, one of the integral parts of the ETC. Written as ɸPSII, this efficiency reduces over the course of a day due to some molecules being degraded and then losing functionality (D1 proteins). Another way to reduce ɸPSII is through non-photochemical quenching (NPQ). NPQ results from too much heat in the leaf that cannot be dissipated. So, the plant makes molecules (called Xanthophylls) to deal with the excess heat and help dissipate it. The authors think that they should measure and target a certain level of ETC efficiency with a certain level of light (photosynthetic photon flux density, or PPFD in µmol m-2 s-1) and attain a specific electron transport rate (ETR) by using a feedback system that automatically adjusts light. If the model works, they believe it can be scaled up to be used in a larger setting and reduce cost.

Materials and Methods

The study revolves around the setup and use of a biofeedback control system that measures ɸPSII and calculates ETR using a chlorophyll fluorometer called a MINI-PAM. This device is clamped onto one portion of the leaf, and the established plants are tested in a growth chamber. The biofeedback system takes fluorescence measurements, calculates ETR, compares it to the target ETR (ETRT), and adjusts the LED duty cycle (on/off rate) to increase or decrease the light available to the plant. This is all done automatically based on short term averages. The MINI-PAM applies a saturating pulse of light and then measures the reflected light to estimate various metrics including fluorescence, ETR, NPQ, and ɸPSII.

The authors selected three plants with varying levels of light requirements: Pothos (low light), Lettuce (intermediate light), and Sweetpotato (high light).

They used two different methods to adjust ETR: 1) Maintain constant ETR for 16 h and 2) Increase ETR from 0 to maximum ETR in 7 steps (for 1 h each), and then back down to 0. The ETRT for each plant species was selected based on preliminary data not shown here.

Results

 

Maintaining a Stable ETR:

 

Lettuce was the only plant examined for stable ETR. It was tested at ETRT of 70 and 100 µmol m-2 s-1 (unit is related to amount of light applied to the plant), with 70 being the maximum ETRT determined for the crop. It was found that ɸPSII was initially low (around 0.5, while healthy leaves should be close to 0.7 or 0.8), but that was the case due to the need for reaction centers to open and produce ATP and NADP. These molecules assist portions of the ETC and are produced by the Calvin Cycle, which begins running off of byproducts of photosynthesis. So, as these molecules were replenished by the Calvin Cycle, ɸPSII quickly increased to ~0.7 and ~0.6 for ETRT of 70 and 100 µmol m-2 s-1 respectively. The ETR at 70 µmol m-2 s-1 was much more tightly controlled than at 100 µmol m-2 s-1. This is partially due to the biofeedback control system only needing to adjust the lower ETRT slightly, as the higher ETRT was more unstable. The increased variation in the higher ETRT was a result of increased NPQ that reduced ɸPSII and caused a PPFD increase over time (20 µmol m-2 s-1). So, the higher ETRT caused instability over the course of the day that increased NPQ, reduced ɸPSII, and caused a need for increased PPFD that increased cost.

Stepwise ETR

This stepwise experiment was conducted using all three plants (lettuce, pothos, and sweetpotato). The maximum ETR was achieved by seven equal increases in ETRT from 0, and seven equal decreases in ETRT back to 0. Each step lasted 1 h. During the initial steps up, the ETR was more tightly controlled by the PPFD. Little variation occurred when compared to the decreasing steps (which also required a higher PPFD, due to the decrease in ɸPSII that occurs over the course of the day). There was a decrease in ɸPSII as ETRT­ and PPFD increased. In all three species, this decrease was associated with an increase in NPQ, which is typical. In lettuce and pothos, NPQ increased throughout the trial, but also decreased as ETRT decreased. In sweetpotato, it increased differently, meaning that the xanthophyll production is variable between species. Due to the differing relationship between NPQ and ɸPSII, it indicated that the period of decreasing ETRT was caused by photoinhibition and not NPQ, or else several measures of chlorophyll fluorescence would have responded differently. This was confirmed as ɸPSII was restored after a period of darkness that allowed the D1 protein to be re-synthesized.

Conclusion

Chlorophyll fluorescence is a tool that can be used to assess the efficiency of lighting and plant environmental conditions in order to optimize a system. Based on the physiological metrics of the plant, the lighting conditions can be controlled in greenhouses, vertical farms, and other production systems in order to increase efficiency and limit wasted energy and cost. More biofeedback systems can be designed an implemented in order to create an ideal growing condition.

Physiological and Morphological Changes Over the Past 50 Years in Yield Components in Tomato

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Physiological and Morphological Changes Over the Past 50 Years in Yield Components in Tomato

Tadahisa Higashide and Ep Heuvelink

Horticultural Supply Chains Group, Wageningen University, Marijkeweg 22, 6709 PG Wageningen, The Netherlands

Citation

Higashide, T., & Heuvelink, E. (2009).Physiological and Morphological Changes Over the Past 50 Years in Yield Components in Tomato . Journal of the American Society for Horticultural Science, 134(4), 460-465.

Background

Greenhouse tomato yield in The Netherlands has more than doubled since the 1980s. This increase is caused by environmental effects such as greenhouse and controlled environment production practices and improved cultivation techniques. In addition to production environment improvements there are genetic effects that positively influence performance that are attributable to breeding efforts.

The aim of this research was to investigate whether  tomato cultivars that were released between 1950 and 2000 show an increasing trend in the trait “yield” which is an aggregate of many traits that are influenced by plant morphology and plant physiology.

Experimental Design

Eight Dutch tomato cultivars [Moneymaker (release in 1950), Premier (1960), Extase (1960), Sonatine (1975), Calypso (1982), Liberto (1988), Gourmet (1991), and Encore (2002)] and one Japanese cultivar [Momotaro Fight (2001)] were tested in a randomized complete block design with each cultivar (genetic treatment) occuring in each block randomly. Two blocks were tested under the same environmental conditions to account for spatial variation. All cultivars were indeterminate type and had medium–large round fruit. Plants were measured destructively and non-destructively for various morphological and physiological traits that are considered to be components of yield.

Results

An increase in tomato yield because of breeding efforts was not caused by an improvement in resource partitioning to the fruit but by an improvement in resource partitioning to vegetative characteristics that resulted in higher dry matter. This increase in vegetative dry matter production was caused by higher light use efficiency and is influenced by tomato morphology and architecture. This result is consistent with previous studies in maize (Hay, 1995).

The leaf photosynthetic rate of the modern cultivars increased proportionally to light use efficiency indicating that there is a positive relationship between light use efficiency and leaf photosynthetic rate. Light use efficiency and leaf photosynthetic physiological traits were indirectly selected for over the course of 50 years as a product of selecting and releasing cultivars that had the highest yield. Yield an aggregate trait of many physiological and morphological characteristics. A more detailed study of leaf photosynthetic rate would need to be done to clarify the cause of its increase over the 50 years of variety release.

Yield of the Japanese cultivar was significantly lower than the other Dutch cultivars. This is likely because breeding objectives of Japanese cultivars are geared more toward quality instead of yield. Soluble solids in the Japanese cultivar was significantly higher than the dutch cultivars.

We can use this knowledge to inform future breeding efforts

Yield as we measure it is an aggregate of many components (Figure 2). From the results of this study we know that all components of yield do not contribute to yield improvement equally. We can optimize future breeding efforts to improve morphological and physiological characteristics that directly improve performance of a variety measured as yield. Focusing on yield components that are directly proportional to variety performance measured as yield could  improve our ability to map regions of the genome that are associated with specific morphological and physiological traits to determine the genetic basis of yield. To better implement morphological and physiological traits resulting in measurable yield increase into a breeding program seed companies would need to develop  high throughput phenotyping techniques as many of these measurements are not cost effective in larger population sizes.

Literature cited

Hay, R.K.M. 1995. Harvest index: A review of its use in plant breeding and crop physiology. Ann. Appl. Biol. 126:197–216.

LEDs for photons, physiology and food

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Original paper: P. M. Pattison, J. Y. Tsao , G. C. Brainard, and B. Bugbee 2018. LEDs for photons, physiology and food. Nature. 563:493-500. https://doi.org/10.1038/s41586-018-0706-x

Compared to traditional lighting, LED lighting offers greater light control, improved performance, and decreased energy consumption. Due to these facts, LED lighting is beginning to be used for an array of new applications to improve human health and localize food production in controlled environments. For the first time in history, the use of LED lighting enables humans to engineer lighting of environments to elicit specific responses.

Four main features separate LED lighting from traditional lighting – light spectral control, light intensity control, control of light distribution in space, and ready integration with other technologies. LEDs for photons, physiology, and food outlines some of the applications and research avenues that LED lighting will enable in both humans and plants.

Lighting impacts both humans and plants greatly. In humans, light affects daily rhythms of sleep and wakefulness, body temperature, alertness, psychomotor performance, neurocognitive responses, and the secretion of hormones. Among the open questions posed regarding lighting for human health and productivity are the nature of the detailed pathways within the melanopsin-based photoreceptor system, interactions between the retinohypothalamic and primary optical tracts, the relationship between the dose of light and physiological regulation in everyday environments, and how to frame our understanding of the positive and negative effects of light. Light-emitting diodes will enable more precise and effective lighting research to be conducted relating to the aforementioned questions, which will enable LED lighting to be increasingly tailored to enhance human health and productivity.

Plants not only require light as fuel for photosynthesis but also use light as a signal to direct plant morphology and metabolite profile. Light sources and color filters have long been used to investigate plant responses to light. However, prior to LED lighting, many of these studies have been limited, mainly because they were conducted at low light levels on single leaves. LED lighting now enables research to be conducted at higher light intensities at the plant canopy level. Additionally, LED lighting allows light intensity, spectrum, and timing of light application to be precisely controlled, taking plant-light response research to new levels.

LED lighting has not only enhanced our understanding of plant-light responses but has also made it cost-effective to grow certain plants indoors for food. To demonstrate the efficacy of indoor agriculture, the authors calculate the grams of dry mass produced per mole of photons for various crops. In doing so, the authors conclude that the photon cost (% of dry market price) is 1% for microgreens, 5% for lettuce, 18% for tomatoes, 103% for general vegetables (i.e. broccoli), and 10,000% for staple crops (i.e. rice).

The main parameters driving the increased photon cost for the above mention crops are:

  1. Fraction of photons absorbed by the plant: Microgreens can be grown at a very high density, thus the fraction of photons absorbed by the plant is very high. However, as plant size increases, plant spacing must also increase. Increased spacing between plants leads to reduced radiation captured and thus reduces the fraction of photons absorbed by plants, as some of these photons will inevitability be lost in space between plants.
  1. Quantum yield (moles of carbon fixed per mole of photons absorbed): The more a particular crop benefits from increased light levels will dictate its quantum yield. Lettuce benefits from higher light levels than microgreens and tomato benefits from higher light than lettuce, thus the quantum efficiency of lettuce is lower than that of microgreens and the that of tomato is lower than lettuce.
  1. Harvest index (moles of carbon in edible product per mole of carbon in plant biomass): Microgreens and lettuce have a very high harvest index as the entire aboveground portion of the plants are edible. Alternatively, tomato stems and leaves are not edible, reducing harvest index. Other general vegetables and staple crops harvest index is further reduced, as these crops generally posses even less edible plant biomass. Thus, for crops with low harvest index, photons are being captured by non-edible plant biomass, leading to increased photon cost per dry mass.

Based on these parameters the authors concluded that “electric light input is a small cost for microgreens, a high cost for general vegetables, and an unacceptable cost for staple crops”. Currently, most indoor farms are focused on growing leafy greens. However, as LED lighting efficiency and technology continues to increase, more general vegetables will be attempted to be grown indoors. Nevertheless, according to this report, even if LEDs were 100% efficient, growing staple crops indoors would not be cost-effective.

It is clear that LED lighting will continually replace traditional lighting and become the standard light source for humans and plants. By 2035, it is estimated that 86% of electrical lighting installs in the U.S. will be LED, which will save roughly US$52 billion per year in direct energy costs. Research into physiological responses to light will allow lighting systems to be optimized and the full potential of LED lighting to be reached, which include improving human health and productivity, increasing the feasibility of local food production in controlled environments, and decreased energy consumption.

Hydroponics vs. Soil Cultivation: Functional and Taste Compound Comparison

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Original Paper
Tamura Y, Mori T, Nakabayashi R, Kobayashi M, Saito K, Okazaki S, Wang N and Kusano M (2018) Metabolomic Evaluation of the Quality of Leaf Lettuce Grown in Practical Plant Factory to Capture Metabolite Signature. Front. Plant Sci.9:665.
doi: 10.3389/fpls.2018.00665

Context

Cultivation of certain crops is moving out of the field. Indoor production has taken the form of greenhouses, tunnels, and plant factories. These growing methods been collectively deemed controlled environment agriculture (CEA). The attraction is in the name – control. Moving crops out of the field helps remove risk of unpredictable weather and can allow for optimized conditions for crop production. It even enables year-round growing that provides a steady source of fresh food to the public and income to the growers instead of the seasonal flux of traditional agriculture.

With food moving indoors under controlled conditions, crops are receiving different types of input in terms of nutrients, lighting, day/night cycling, temperatures, disease and pest stresses, and other variables. Some crops are growing differently and looking different as well. It all depends on the control conditions.

As plant growth and development changes due to these controlled environments, the metabolic processes dictating that growth and development are probably varying as well. As a result, there may be changes in the plant’s profile of chemical compounds, or metabolites, which take part in and are produced by plant metabolism. These compounds are integral to the structure and general function of the plant as well as its defense against pests and disease. Again, with cultivation conditions changing, the metabolite compositions of the plants are likely changing simultaneously.

The Experiment

If we want to know if or how CEA is changing the metabolite profiles of our food compared to field cultivation, we need to isolate each element of the “control” to determine what changes are being caused by which conditions. To this end, a group from RIKEN in Japan that studies metabolite profiles (an analytical chemistry practice called metabolomics) chose to compare compounds of lettuce grown in a hydroponic system (plant roots growing directly into water with an added nutrient solution) within a Keystone Technology Inc. (Japan) plant factory to lettuce grown in a similarly-controlled growth chamber except planted traditionally in soil (Table 1).

Table 1. Plant factory conditions for hydroponic cultivation treatment compared to growth chamber conditions for the soil treatment.

This group chose two lettuce cultivars, ‘Black Rose’ and ‘Red Fire’, with one head of each cultivar grown per treatment – hydroponics and soil – for a total of 4 heads of lettuce in the experiment. Tamura et al. observed smaller and more pigmented leaves from the soil-grown lettuce compared to the hydroponic production. To detect the metabolites present in the lettuce they used precise instruments (gas and liquid chromatography mass spectrometry). They included samples from leaves on the outside of the head and the middle to account for variation in metabolite production in different parts of the plant.

Findings

Analysis resulted in 133 identified compounds and 185 unidentified. Based on the relative abundances of all 318 metabolites, they were able to clearly separate samples of hydroponically grown lettuce from those grown in soil.

Upon further study, they determined that hydroponic lettuce had higher amounts of amino acids (protein building blocks) than the soil-cultivated lettuce. On the other hand, lettuce grown in soil contained more sugars and compounds that contribute to taste and possible health-benefits, such as sesquiterpenes and organic acids. Particularly, glutamate, a metabolite contributing to the umami (or savory) taste profile of a food, was significantly higher in ‘Red Fire’ lettuce grown hydroponically. However, a sugar, sucrose, and a compound associated with bitterness, lactucopicrin-15-oxalate, were both significantly lower in the hydroponic lettuce.

Conclusions and Considerations

This study is valuable due to it being the first of its kind—applying metabolomics to understand how our crops are changing in CEA systems. These results need to be validated by another experiment in which the conditions other than soil/hydroponics are identical. Previous work by Li and Kubota (2009) demonstrated that differences in light intensity and quality can affect metabolite production in a CEA setting.

Additionally, different fertilization regimes largely influence the amount of nitrogen plants can access to produce amino acids. With the hydroponic lettuce receiving almost 3x the fertilizer compared to the lettuce in soil, a higher amino acid content in hydroponic lettuce cannot be completely attributed to hydroponic production itself. Therefore, the differences in control conditions presented in Table 1 above are confounded with the soil/hydroponic treatments, making interpretation of results complicated. This also points to the importance of collaboration across scientific disciplines to ensure the most effective and efficient experiments are conducted.

Citations

Li, Q., and Kubota, C. (2009). Effects of supplemental light quality on growth and phytochemicals of baby leaf lettuce. Environ. Exp. Bot. 67, 59–64. doi: 10.1016/j.envexpbot.2009.06.011