A primer on data-driven growing and technologies for optimal crop production in the greenhouse
Kenneth Tran ++
Foreword. This book is designed to provide the technical training and decision support tools to greenhouse growers. The book is written for growers ... by a collective group of plant scientists, physicists, technologists, and growers.
WIP: the book is a work-in-progress and still in a very-early stage. If you would like us to prioritize the writing of certain topics, don't hesitate to send us a request to firstname.lastname@example.org
- 1. Introduction
- 2. Crop growth and response to environmental factors
- 2.1. Crop Growth Overview
- 2.2. Carbohydrate distribution and plant growth
- 2.3. Crop response to environmental factors
- Carbon dioxide (CO⁍)
- Water (Irrigation)
- Transpiration Modeling
- 3. Climate dynamics inside the Greenhouse
- 3.1. The Principles of Energy Balance
- 3.2. The Dynamics of Humidity
- 4. Crop-specific Physiology
- 4.1. Lettuce and Leafy Greens
- Optimal Environment for Lettuce Production
- Most Common Problems in Lettuce Production
- 4.2. Tomato
- 4.3. Other Vine Crops
- Differences between Cucumber and Tomato
- 5. IPM
- 6. Hardware tech to improve crop production
- 6.1. Movable Screens
- 6.2. Supplemental Lighting
- 6.3. Vertical Fans
- 6.4. Sensors
- 7. Software tech and data-driven growing
- 7.1. Data Collection and Visualization
- Manually registered data
- 7.2. Advanced Decision Support System
- 7.2.1. Digital Decisions
- 7.x. Be aware of the high-interest technical debt
- Data Silos
- Extra Materials
- 7. References
- 8. About Authors
- 9. FAQ
This neobook is designed to provide the technical training and tools to greenhouse
growers. The book is written for growers ... by a collective group of plant scientists, physicists, technologists, and growers.
Why do we call it a neobook?
This book provides an integrated approach to crop growth and development and the technical aspects of greenhouse cultivation and climate management. It combines an analysis of the relationship between crop production and ambient climate with an explanation of the processes that determine the climate in a protected environment.
What is #datadriven growing?
Growing plants can be a complex task often described as a work of art. What we mean by this, is that we all know plants require certain things to grow and thrive, such as light, water, and nutrients, but anyone that grows a plant knows it is not as simple as 1,2,3. This is because the factors plants need to thrive interact with and influence each other. For example, light also has a heating element that affects the temperature, water can affect the temperature by changing the humidity, and so on. What complicated things even more, is that plant growth interacts with the climate; transpiration increases heat and humidity, photosynthesis can affect CO2, and so on. In order for growers to be successful, they must know the art of how to balance many different factors to keep their plants happy. Up until now, this art form of growing has relied on years of experience to develop the knowledge-base and intuition required for optimal production.
Data-driven growing is a different form of growing, where growers read the data about their climate factors and plant growth factors, and can tell how these measurements will influence their yield. In #datadriven growing, growers not only react to data points, but are able to predict downstream effects on their crop early on to take action before any negative effects unfold. For example, growers may see the absolute humidity of their greenhouse is high and notice the photosynthetic rate of their plants in one area has decreased, indicating these plants are under some sort of stress, such as a fungal pathogen. These growers could take action to quarantine these plants to prevent fungal pathogens from spreading throughout the greenhouse, all before the physical plant symptoms are visible to the naked eye. Recent technology advancements such as artificial intelligence (AI), internet of things (IoT), and intelligent automation (IA) have begun to revolutionize the field of agriculture, ushering in a new digital age. Many different types of data can be collected within indoor farming facilities to produce a digital image of the climate and current crop conditions using IoT sensors. Growers can now access these data points from anywhere with wifi, even on their phones. Technologies like Koidra’s Krop Manager platform utilize these data points along with AI and the mathematics defining nature to explain how different climate factors influence each other and plant growth. For the first time ever, growers can harness the power of big data in the palm of their hands to monitor and analyze how their current climate status is affecting their crops, and what actions they can take to improve yield and resource use efficiency. No advanced mathematics degree required 😉.
Imagine predicting precisely when and how much water your crops will need in order to maximize growth, minimize water use, and increase profits. Or, being able to predict that turning the temperature down 5 more degrees now instead of 2 degrees 8 hours later will save more energy and keep your plants happier? These are the types of predictions made possible with data-driven growing, based on the data collected from sensors and the math behind the nature of growing plants. This book will cover the topics of plant growth fundamentals and how growers can use new technologies available to improve their controlled environment agriculture production with data-driven growing.
2. Crop growth and response to environmental factors
Most textbooks start with Physics and climate modeling as the foundation. In this book, we start with the plants first, explaining what the plants need to thrive and how they respond to different climate conditions.
2.1. Crop Growth Overview
Physically speaking, plant production takes place on a farm or in a greenhouse. From a theoretical point of view, plants are produced in bio-economic systems, i.e. systems that are determined by economic, biological and technical drivers. To understand the behavior of these systems, we will first look at the major relationships (Figure 1).
The commercial production of plants is an economic activity. Profit is the defining trait of all companies and what drives farmers. Without profit no operation is able to stand.
Cost vs Revenue
Profit [Wikipedia] is the difference between revenue and costs. For the company, especially the internal costs matter, but on a higher level, e.g. the society or on the level of an ecosystem, costs related to the use of natural resources like water or emissions of greenhouse gases are equally important. Expenditures for installations like the greenhouse are fixed costs, whereas expenditures for fertilizers, pesticides or energy are variable costs. The latter are important for day-to-day control decisions, whereas fixed costs are more strategically important.
With the future of renewable energies more and more relying on solar and wind power and energy storage solutions still being built these prices are on a trajectory to vary wildly depending on the daily conditions. While at the current time energy prices are often cheaper at “off peak hours” [Wikipedia] this could change in a future more dependent on renewable energy. Here the necessity of energy storage will be the limiting factor, perhaps resulting in cheaper energy during the day and windy hours. With higher prices for energy and as such transport, local markets might grow considerably, changing the fruit demands of the future.
The revenue is the product of the (marketable) yield and the price per unit. In vegetables, the latter depends mostly on quality and season.
Biologically, marketable yield is the product of the total plant mass produced and the fraction of plant mass which constitutes the product. This fraction is called harvest index. During production, plants may not produce as much as they could under the given physical conditions, probably because insects feed on their fruits or fungi reduce their photosynthetic potential. The proportion of yield lost that way determines the total plant mass together with the potential plant mass. Since light is the energy source of plant growth and usually the main constraint to plant growth, Therefore, potential plant mass can be described as the product of light intercepted by the plant and light use efficiency.
Box: Product quality, season and price,
Depending on which market we want to produce for the quality of the food is of large importance and we could theoretically break it down even further into “External quality” and “Internal quality”, which are further subdivided into, for example, “Look” “Smell” “Taste” and “Nutrients”.
- A large factory producing apple juice does not care for the look and smell of the product, only its taste because that is all that matters to them. The most beautiful apple in the world will just be treated like all the others. As such producing for beauty is wasting money here.
- A pharmaceutical corporation would mostly be interested in the nutrients, the local farmers market prides itself on these as well as a sign of “bio” and “eco”, yet a supermarket will only put the most beautiful products on its shelves.
- Cucumbers will grow as long as they are attached to the plant and drain nutrients from the later developing ones, so one has to pick between few larger and many smaller cucumbers when harvesting. Which is “better” ?
This question can not be answered here for its a question of shipping and sale, which is much better and deeper explained here [Wikipedia]. Still, we can separate these four conditions into two larger subsections: External and Internal quality.
- External quality describes the shape, look, feel and smell of a fruit product, from simple “beauty” to special shapes and colors. Halloween pumpkins are not bought for their taste but only for their large size, orange color and thick fruit flesh to keep their shape. As such spending energy on their taste is wasted. Decorative fruits fall into the same category, but most of the time the looks of a fruit are used to advertise it on the market. Rich and strong colors which, importantly, need to be the right colors. Oranges are orange, Cauliflower is white and cucumbers a deep dark green. Switching up these colors in special breeds could potentially be the “next big hit” on the market. The best example of this perhaps are bell peppers. For a long time green bell peppers were simply unripe red ones until the “permagreen” variant was developed [CDC]
- The shape of a fruit is of large importance as well. Cucumbers for example are sold in most supermarkets around the world in large boxes, which are shipped to them from the warehouses where the cucumbers are packed. It is a necessity to fit as many fruits into a box as possible, to reduce transport costs. As such curved cucumbers are sorted out, with only the straight ones being shipped. For a deep insight into how the cucumber fruit shape is regulated on a biochemical level click here [Liu et al., 2020]
- Internal qualities are those that come to the surface during the eating or preparation of the fruits. These can range from taste and nutrients to more specialized qualities like seedless grapes.
Same with the time of sale. This could be again further divided into transport and place of sale or perhaps the storage costs, but we will keep it simple. Product is best sold at times when there is less of it on the market, since this causes prices to rise. The less strawberries there are in the winter, the higher the price. At the same time many products are only pushed to the market at a certain time because they won’t be sold around the year, see large orange pumpkins for Halloween. Also keep in mind that the knowledge of “Strawberries sell better in the winter” might cause other growers to adopt the same strategy, driving prices down again, which has to be weighted in with the higher costs to grow strawberries during the winter in a greenhouse.
In a way, this could be described as an observation of the market that in itself changes the market. Like in quantum physics we are changing the result of a reaction by observing it.
Perhaps it would have been cheaper to grow them on the southern globe and import them by plane? But what about the ecological costs of the fuel? All these are questions that an AI can answer. It must be pointed out however that a failure to rely accurate data or not checking on global market trends is not something an AI can help with. For information please see the “High interest technical debt” point.
This is a practical tip.
This is a a practical tip for the growers.
The potential yield is the calculation of the energy uptake of a plant minus the loss factor determined through upkeep and pests. This is the absolute maximum of biomass the plant is able to produce. It is determined through the combination of the light interception through leaf area size and orientation, as well as the light use efficiency (LUE). Figure 2 shows the Light use efficiency calculation for our binary tree in more detail.
The loss factory is the amount of biomass that is lost by external causes, independent from drought or light. In this category, we talk about plant pathogens and insect damage and further separate the plant protection into preventive (passive) and combative (active).
Active plant protection deals with the direct combat of pathogens, pests and unwanted growth of weeds by directly targeting them. Mechanical plucking, chemical agents and the direct handling of infected plants are necessary to protect the whole greenhouse. Based on the intensity of the detected danger this ranges from the plucking of weeds or the application of poison to the removal of entire plants. Combative measures are effective but come with a price. Due to their nature they can only be used when the threat has already shown itself, when the plants are already infected. Early detection systems like sensors in the greenhouse can help, but they cannot prevent it.
Preventive measures are used to prevent an infection in the first place, but need to be carefully considered. Like all preventive actions they cost money and time, without showing a direct positive effect. It is impossible to tell if a greenhouse was not infected by a pathogen due to the preventive measures or just by random chance. Was the application of preventive actions the cause for healthy plants or an unneeded expense? In models this is the area for risk calculations, statistical models that draw data from environmental conditions, other growers experiences and analytical data of the cultivar to determine the risk of an infection. If these risk calculations, whether done by a AI or the growers experience signal a large potential risk for the crop preventive measures are advised. For more details see this article on risk management [Wikipedia].
Plants breath CO2 through their stomata, which allow the gas exchange. Depending on the CO2 in the atmosphere, which can be controlled by either removing CO2 from the atmosphere or adding more from gas tanks. The stomatal conductance depends on the need for the plant to breath and control its water relations, since the stomata are both needed for the gas exchange and the evaporation of water to disperse heat. More information on this can be found in the water relations section. Carbon taken up through the stomata is metabolized via photosynthesis to store the intercepted light energy in the form of sugar, which is used as a fuel for the metabolism to create proteins. The rate of carbon per energy unit of light is described as the assimilation rate.
The assimilation rate describes the rate at which carbon from the air is taken up into the plant and used in the metabolism. Photosynthesis is responsible for this, breaking up CO2 to access the carbon inside, metabolizing it into sugar. Oxygen is a byproduct of this reaction, which incidentally allowed the development of higher life on earth. Looking back into history we can roughly pinpoint the time at which photosynthesis developed in cyanobacteria, which came along with a mass extinction of all species that were unable to adapt to this sudden rise in oxygen in the atmosphere. For more information please look at the “great oxidation event” [Wikipedia].
The biochemical reactions that allow photosynthesis to happen in detail, its dependency on nutrient supply, Vmax (the maximum rate of carboxylation) and temperature are too complex to break down here in detail. In short, the photosynthesis apparatus works better at higher temperature, but like all enzymes and proteins it has a maximum temperature after which it starts to break down, or denature. For the photosynthesis enzymes the “optimal” temperature lays around 20°C, with higher temperatures slowly decreasing its effects. Above 40°C the enzymes start to break down quickly. For more information on the effects of temperature on crop photosynthesis please read this article by Moore et al (2021) “The effect of increasing temperature on crop photosynthesis: from enzymes to ecosystems.
In our growth models and harvest predictions we are often separating the yield into two parts, on one side the pure biochemical machinery that, roughly speaking, turns light energy into biomass in “sources”, and the plant development where this biomass is send into various “sinks”. Over the course of the plant development the focus of the “source/sink” ratio is changing. One of the best examples for this are the so-called “early” and “late” variety of various vegetables like cabbage.
Early varieties shift from leaf and root development to fruit development early, resulting in a quick harvest of small yet already developed fruits. Late varieties go the other way, focusing on large leaf and root development first before they begin to produce very large fruits. Depending on what the market demands the choice of the appropriate variety if important. Local markets prefer early fruits of manageable size while larger commercial buyers prefer large fruits for their higher yield.
The canopy density, meaning how tightly the plants are placed together, also plays into this calculation. Early plants are grown closer to another since they will not expand in size as much as late plants. These need to be separated either during the growth or from the beginning so that enlarging leaves and fruits don’t compete for space and light. Of course a higher canopy density increases the yield per area, but only as long as the negative effects of competition can be compensated. Additional lighting or intercanopy lighting are common ways to increase the supply of light in densely packed plants.
Plants regulate their leaf positions and leaf angles [Wikipedia] depending on the time of day (Circadian cycle/nastic movement), the light intensity (sun avoidance), the position of the sun (heliotropism), water relations in the soil and plant (drought response), impact and touch, heat/cold and the shading by other plants, structures or even itself (shade avoidance).
The three main driving patterns of this leaf movement are the Circadian cycle, the Heliotropism mechanisms and the shade avoidance:
- The circadian cycle is the internal clock of the plant, a massive interconnected biochemical network of feedback loops that pick up light and darkness signals, enabling the plant to measure the length of the day and night cycle and adapting to it. In most plants, this leads to leaves taking horizontal positions during the end of the night for maximized sun interception during the day and lowering/raising the leaves at the end of the day. While the mechanical basics [Ueda and Nakamura, 2007] are quite well understood by now, them being a chemically controlled shifting of water pressure in the pulvinus cells [Wikipedia] the evolutionary benefits are still under research. Experiments have shown that even under artificial patterns like six hours of light and darkness, the leaf movement adapted, with many lowering and raising reactions of the leaves happening before the lights turned on and off.
- Heliotropism is the ability of plants to follow the path of the sun or a suitable artificial light source. Sunflowers are the most common plant showing a very defined reaction, with their large flowers following the suns path throughout the day. Just like with the circadian cycle a variety of possible reasons for this phenomenon have been proposed [Wikipedia]
- Shade avoidance is the ability of plants to detect shade and shading, activating the leaf and stem elongation mechanisms to escape this shadow, reaching the light again. Regulating this response are the phytochrome complexes in the plant, photoreceptors that switch between and active and inactive form based on the ratio of red to far red light they detect [Wikipedia]
When sunlight is intercepted by a leaf not all wavelengths are absorbed by the chlorophyll complex. If that were the case, leaves would be black and not green. Instead the so called “green gap” [Second.wiki] in the visible light and those wavelengths “above” red (far red from 710 to 850 nm) are passing through the leaf undisturbed. As such the ratio of red to far red below a leaf is very different than above, with the far red ratio increased.
The phytochrome complexes are capable of detecting this ratio and can send the signal of shade down the response ladder, activating the elongation response. In dense canopies this effect can lead to stem and leaf elongation where plants are spending more energy on growing upwards faster, decreasing fruit development [Greenhouse product news].
The self shading of plants is partly prevented by the plants of leaf shape and position on the stem. This orientation is called phyllotaxis [Wikipedia].
Fixed targets, variables and control parameters
In our binary system we can only really influence the parameters that are not controlled by other parameters. The actual price on the market for example is not ours to control, we can only set the price when we sell our product on the market. As such we are left with the following parameters that we can “control”: Ecological costs, Social costs, Fixed costs, Variable costs, Harvest index, Quality, Time of harvest/sale, Loss factor, CO2 atmosphere, Water, Temperature, Light, Nutrients and, despite not mentioned in the diagram so far, the Substrate.
We further divide these into the fixed “targets”, variables and control parameters. To explain:
- Fixed targets are the settings that we can directly control, but which can’t be changed during the growth without severely affecting the entire system. As such we treat them as simply being unchangeable. These are the time of harvest/sale, the harvest index, the quality and the substrate. All of these have to be set before the growth begins and depend either on the production goal or the conditions of the greenhouse. We can’t simply just replace the substrate.
Substrate in this setup is treated as a storage for water and nutrients, the details of root growth are ignored for the moment but could be included later.
- Variables are parameters that are influenced by our setup but are only regulated indirectly. Ecological costs, Social costs, Fixed costs and Variable costs are goals on which we can develop towards, but aren’t changed directly. We can demand that our production reduces the social costs, reduces the ecological costs while allowing it to rise in the variable costs. But it is impossible to simply declare a reduction in the ecological costs directly.
- Control parameters are all those settings that we can directly control and which effect all other parameters. In our setup we count CO2, Water, Temperature, Light and Nutrients. These control parameters are not directly included in the binary tree because each affects multiple, if not all other parameters. Light effects the energy uptake, leaf development, but also the temperature in the greenhouse and the costs of energy. Same for the temperature inside of the greenhouse. Water and nutrients, their precise application time, concentration and makeup, determine quality, productivity, photosynthesis and more.
And only now are we slowly approaching the actual parameters that we can directly control in the greenhouse, the ones where the plant growth is concerned. Pests and diseases take away energy and need to be cured or prevented, assuming the cost of the cure is not higher than the loss of crop. Constant checkups and preventive measures are costly as well after all. If the risk of an infection is higher at higher temperatures, but higher temperatures mean more yield, is this an acceptable trade-off? What about the heating costs? Slowly the network needed to answer the questions “What is my profit” and “How can I maximize profit” is revealing itself. We could go even further into detail, talk about the large variety of pests and loss factors, the calculations between leaf size and leaf area index, the physical and chemical reactions that convert the light on the leaves into energy, how this energy is distributed through the plant and which stages in a plants life are the most influenceable to steer towards certain goals. The light interception for example is determined through the light in the greenhouse or field, in combination with the size and orientation of the leaves. The light use efficiency is determined through the photosynthesis, which in turn depends on water relations, which depend on transpiration and temperature, which can be controlled by heating, which influences the price for the greenhouse and so on and on.
For example, imagine a grower wants to change the nutrient setup due to high costs of the nutrient mix (Nutrients → Fixed costs). An AI could point this out, if it were to be tasked with reducing the fixed costs. The nutrient mix used is tagged with multiple stats, such as the costs for its use, its effect on the assimilation rate and the fruit quality, but also the ecological costs. Changing the nutrient mix to a different one changes these tags, with new costs for usage, effect on quality and ecological costs. With these new fixed effects the AI can recalculate the control parameters, determining new optimal light, temperature and water controls. All of which come again with new tags for fixed price, quality, assimilation rate and ecological costs.
This is the power of AI. The ability to build a network that calculates all these parameters in real time, using data from a central network and direct data from the growers. But in order to function one question has to be answered first:
What exactly are you looking to produce? And how are you planning to produce, grow, market, store, transport, sustain, defend, protect, harvest, sell, use and plant it?
(Potential GIGO section)
Each parameter (Profit, cost, benefit, external…) is further defined by two more parameters, growing larger and larger with each new layer. And at first, one might ask the question of why this detailed view is important on the larger scale. And the answer for this has been true in the past, but with the rise of automation and ai systems it is ever more important: GIGO
GIGO (Garbage in, garbage out) is a term from the field of programming and information/computer technology. It describes, in its most basic idea, that a system (Self driving car, self learning ai, automated greenhouse…) fed and/or built with wrong data (Garbage in) will never be able to filter these data points or catch the error and as such always deliver a wrong result (Garbage out). This is due to the fact that an algorithm cannot determine true from false points. For the system all data points are true, simply by the fact of being handed to it. If you want to read more about GIGO and the theory behind ai learning and scientific data security, please see
(Hyperlink to “GIGO”)
. For more information on how such a problem can manifest itself in a greenhouse see
“High interest technical debt”Hyperlink
What do a space probe crashing into mars and a computer pausing Tetris have in common? GIGO. In 1999 Nasa tried to send the “Mars Climate Orbiter” on a stable course around Mars. However, when the probe, far away from earth, was calculating the optimal height around mars a system failure approached. The company that had developed one piece of the ground software had set it to operate in feet and inches. Nasa uses metrics. As such the internal computer accepted the data, was unable to check the correct unit, and calculated a path that steered the probe straight into mars [Wikipedia].
In 2013 an AI system was developed to play a variety of old Nintendo games, learning based on its final score. Actions that caused a rise in points were rewarded, actions that lost points were “punished” and failing a game was seen as a large point reduction. And the best summary of what failed is perhaps the game “Tetris”, in which the ai learned to stack the blocks as fast as possible and then pause the game right before it lost. Since the game never stopped it had never lost. (The first level of Super Mario Bros. is easy with lexicographic orderings and time travel… after that it gets a little tricky. [Murphy, 2013])
What can we learn from these amusing stories? Well, amusing to use at least, Nasa was probably not laughing quite as much. We can learn to check our data and check our model. The binary model above shows how we can define the different parameters used to describe the plant growth operation and if one of these parameters is not brought into the plan then the system will ignore it. An optimization program is a tool to let you know which constraints you forgot. And an AI is a very fast optimization program [Hofstätter, 2021]. If a system is set to increase profit but the cost of water is ignored then an AI will use this, increasing water use to near infinity simple because it “knows” that plants grow better when they have more water. Same for nutrients. When we ignore the damage of excessive light the AI will increase light intensity simply because it “knows” that plants grow better when they get more energy. When we don’t tell the AI what we harvest from a plant then the ai will ignore it. When we don’t include pests and infections into the model the AI will be unable to calculate these, instead for example reading the damages as results of heat. If we don’t calculate the social and ecological costs then AI will ignore them, pushing the maximum amount of cost on it.
The cost of perfection is infinite
But we can at least come close to it by making sure we precisely know what we are looking for when we declare our goal for plant growth. What do we grow, at what time, for what purpose, for which market, at what cost. And in order to feed these data points into the system we split each parameter into two more, creating the binary tree.
The photosynthesis process evolved 450 million years ago. With this process, plants convert carbon dioxide and water into carbohydrates using light energy. Carbohydrates synthesized in the photosynthesis process will be distributed to all plant organs to fuel their activities. The photosynthesis process can be described as a chemical equation:
is absorbed via root, and the gases and enter and leave the plant through tiny pores in the leaf called stomata.
Photosynthesis occurs within a special cell compartment called the chloroplast. When light is intercepted by leaves, individual photons (particles of light) are absorbed by a pigment called chlorophyll (also responsible for the green color in leaves). Chlorophyll is stashed in membranous sacs called thylakoids. Stacks of thylakoids fuse to form single units called grana. Thylakoids and grana are filled with lumen, and the chloroplast is filled with stroma (see figure below).
Photosynthesis can be divided into light-dependent and light-independent processes. The light-dependent process occurs within the thylakoid membrane and requires a steady photon stream. In this process, photons transfer energy to chlorophyll, and light energy is converted into chemical energy in the form of the molecules ATP and NADPH. The light-independent process (the Calvin Cycle) occurs in the stroma and does not require light. During this process, energy from the ATP and NADPH molecules is used to assemble carbohydrate molecules, like glucose, from carbon dioxide.
Transpiration is an important process within plants that occurs when water vapor leaves the plant through leaf stomata. The exit of water molecules through transpiration is responsible for the plant’s ability to pull water from its growth media up through its roots. Water’s cohesive property is responsible for this, since water molecules like to stick together; as molecules begin to evaporate through the stomata, the remaining molecules inside the plant’s vascular system are pulled upwards. This pull occurs all the way down the plant from the leaf to the root, where the plant can then pull more water up through its roots (see figure below).
Transpiration allows for the constant flow of water required by plants to perform photosynthesis and other growth processes, as well as providing turgor within the stems for the plant to stand upright and not wilt. Transpiration has a cooling effect on the plant in a similar way that sweating has on humans- heat held by water molecules leaves the plant as the molecules evaporate. Plant’s rate of transpiration can be modeled using mathematical equations, and used in data-driven growing as a growth factor.
2.2. Carbohydrate distribution and plant growth
2.3. Crop response to environmental factors
This section follows closely the order presented in Chapter 7 of [Stanghilini et al]
In this section, we will explain how the crop responds to each environmental factor such as light, temperature, carbon dioxide, humidity, etc. However, it’s critical to note that crops do not respond to these factors in a mono-factorial way or in any linear fashion.
Crops require light in order to carry out photosynthesis, produce carbohydrates, and increase biomass (grow!). Plants absorb light through their leaves, and therefore the more or bigger leaves in their canopy the more light they can absorb. As the amount of light absorbed by a plant increases, photosynthetic rate also increases to a certain point as long as other nutrients are not limited. Heat as the result of light or photosynthesis is released from the plant through transpiration.
Too much light, either in length or power, can have negative effects on plant health. Plants can experience sun damage in the form of chlorosis- a yellowing of the leaf surface where the chlorophyll is lost. Chlorosis is not only caused by too much light, but can also be a sign of nutrient deficiencies and some diseases. Another way plants can protect themselves from too much light is the buildup of anthocyanins- deep purple or red molecules that act as a physical barrier to light.
Since plants require light to grow, if a light source is placed on one side of a plant, then that plant may grow in the direction of the light. This phenomena is called phototropism.
Carbon dioxide (CO)
As described in the chapter 1.2 the CO concentration of the ambient air plays an important role for photosynthesis. In the end of the 80s an air concentration of around 330 vpm was typical. Nowadays concentration reaches ca. 0.04 % (400 vpm) (Wikipedia, 2022, Carbon dioxide in Earth's atmosphere). In a greenhouse the CO concentration decreases during daytime due to the CO uptake of the plants by photosynthesis. So, under nonventilated conditions values can drop below 200 vpm and this results in a reduction of mass production. In the moment the vents open the incoming fresh air offsets this effect. At night there is an increase of CO concentration caused by the dissimilation of the plants. But we are only talking about an increase of around 100-150 vpm.
Fig. xx shows the increase of production of greenhouse crops with increasing CO concentration (µmol mol-1 equivalent to vpm). It has to be noted that the slope decreases with increasing CO concentration. So, it always has to be calculated if an additional increase of CO is economical.
Fig. xx: Relative production (%) of greenhouse fruit vegetable crops as function of the average CO concentration (µmol mol) during the cultivation, with the production at 340 µmol mol as reference (100 %).
----- = 95 % confidence interval of the mean of the observations
—— = 95 % confidence interval of the observations (modified after Nederhoff 1994)
In practice farmers increase the values to around 800 vpm CO. Some farmers even go up to 1000 vpm even under the situation of open vents. For cucumber for example this should result in the effect that all fruits on the main stem develop to a harvestable fruit size. Normally some of the fruits on the main stem die back due to the fact that there are not enough assimilates to feed all the developing fruits.
There are several ways to enrich the greenhouse air with CO:
- Using Low-NOx heaters
To combine greenhouse heating and CO supplement, low-NOx heaters can be used. These heaters combust natural gas or propane. The exhaust fumes are directly led into the greenhouse. A disadvantage is the dependence of producing heat and CO. If low or no heating is necessary there is no CO production or the resulting concentration is too low to have a sufficient effect on photosynthesis. If a lot of heat is needed valves can lead the exhaust fumes to the outside to avoid excessive and dangerous values of CO and CO (see below: important remarks). Another disadvantage is that CO is only used by the plants at daytime, a period with relative low need of heat and therefore relative low production of CO by the heater.
Figure xx: Low NOx heater (Priva Agro 2003)
- Using exhaust fumes from a standard gas heating
To avoid these disadvantages another system has been developed. Hot water for heating is produced during daytime using a standard gas heating. The exhaust fumes are led directly into the greenhouse. The produced heated water is stored in big insolated warm water tanks. From this storage the water is used for heating during night. This way CO production and production of warm water are decoupled.
Figure xx: Insulated warm water storage tank (Fricke)
- Using technical CO
The easiest way to increase the CO concentration is the use of technical CO. This CO is stored in liquid form in tanks outside the greenhouse and can be distributed in the greenhouse by pipes which are laid out on the floor. Technical CO is free of impurities and therefore no problems of toxic gases due to combustion occur. This supply can be controlled by the climate computer using CO sensors and is independent from heating.
Figure xx: Tank for liquid CO storage (https://www.kks-trockeneis.de/co2-zylinder-kohlensaeure-flaschen/)
Important remarks: For all methods using combustion of gas it has to be remarked that the combustion produces NOx, CO, phytotoxic ethylene (CH) and hydrocarbons. So, the injection of exhaust fumes should be stopped if CO concentration in the fumes is too high. For all methods (also the use of technical CO) the enrichment should be stopped by using a setpoint of ca. 1200 vpm. Warning sensors have to be placed in the greenhouse to protect the workers from too high concentrations of CO (the maximum workplace concentration of CO is 5000 vpm).
To avoid high losses of CO the application should be stopped if high wind speeds occur. Here the losses depend mainly on the tightness of the greenhouse construction. Additionally, a stop makes sense if the temperature in the greenhouse is near to the ventilation temperature (e.g. stopping enrichment if temperature reaches 4°C below ventilation setpoint) and if the radiation is too low to reach an effective use of the CO in the photosynthesis. So, the beginning and the end of enrichment can be controlled by e.g. using sunset and sunrise time (e.g. starting 1 h before sunrise and stop 1 h before sunset) or absolute minimum radiation values.
- The role of water in the plant production process
One of the control parameters in plant production is the water supply by irrigation (Figure $$). The irrigation has an influence on the water content in the substrate and subsequently on the water content in the plant. As control mechanisms of action, decisions about the amount of water given per application and the frequency of the irrigation measures have to be made.
1.1 Leaf Expansion Rate and Stomatal conductance affected by water supply
In the water usage cascade of a production system from water in the substrate to leaf transpiration, the physiological important parameters water influences are leaf expansion rate (LER) and stomatal conductance (gs) (Figure $$). LER is determining plant leaf area and gs is one of the determinants for the CO influx into the leaf, both having a decisively impact on photosynthesis.
1.1.1 Leaf Expansion Rate
In the phase of leaf growth, we have to distinguish between cell division and cell expansion. In plant organs cell division is only active for a relative short period, it is finalised long before the organ reaches its final size by the process of expansion. For cells to expand the pressure in the cells is important. This pressure is called turgor and depends on the water status of the plant. A low turgor caused by restricted water supply decreases the turgor and decreases cell expansion. As result the leaf organ will be of a smaller area. An ample supply of water by irrigation is a prerequisite for high plant leaf area increasing the light interception of the plant canopy.
1.1.2 Stomatal conductance
The leaf lamina of plants is equipped with stomata. These stomata function as bridge between the plant tissue and the ambient air environment. Water is transpired via the stomata to the environment (for the function of transpiration see 1.2.2). Prerequisite for this transpiration is a sufficient water potential in the leaf lamina so that the stomata of the leaves keep open to allow the water to evaporate and leave the stomata pore as water vapor. Physically this control function is expressed as stomatal conductance or as inverse stomatal resistance. As rule of thumb around 95 % of the transpiration is realized through the stomata, around 5 % via the leaf cuticula (Taiz and Zeiger 2006). To keep up a high water potential in the leaf and consequently a high stomatal conductance again the ample water supply plays the decisive role. But why is a high stomatal conductance so important?
1.2 The role of stomatal regulation for photosynthesis
In the chapter above the role of the stomata was more focused on transpiration and energy balance. There is a second and maybe even more important function of stomata: the gas exchange.
For photosynthesis plants have to take up CO into their tissue. The CO enters the plant via the stomata. Because the opening of the stomata depends on the water situation in the plant, there is a clear link to the chapter above. Besides the opening of the stomata also the concentration gradient of CO between the outside air and the air inside the stomata plays an important role. As we know the ambient CO concentration is rising due to climate change. In the moment it is ca. 0.04 % (400 vpm) (Wikipedia, 2022, Carbon dioxide in Earth's atmosphere). To increase the gradient and consequently increase the rate of CO influx, the grower can increase the ambient CO concentration in the greenhouse by adding CO via technical gas or by burning fuel using fired heaters. The desired setpoint for this increase depends on a lot of parameters, but values of 800 vpm CO are not unusual. Of course, this supply only makes sense during daytime when the photosynthetic apparatus is active. The intensity of photosynthesis controls the decline of CO in the stomata, leading to a gradient of CO between inside and outside of the leaf. In the end this concentration gradient and the stomata opening width determine the CO supply for photosynthesis.
It has to be mentioned that the CO concentration itself has an effect on the stomata opening. A decreasing CO concentration in the stomata results in an opening reaction of the stomata. Evolutionary this makes a lot of sense to keep up photosynthesis under lower CO concentration. Within limits this even counteracts closing reaction by decreasing water potential.
Plants are exposed to energy incidence by light and ambient temperature. This results in increasing temperatures of the plant surface. As long as this increase has positive effects regarding the biochemical processes of the plant metabolism this is a benefit for plant growth. To avoid excessive plant surface temperature plants are able to transpire, means they use the physical process of evaporative cooling. The evaporation of water is energy demanding. The evaporation of 1 g of water needs the energy of 2.26 kJ (Wikipedia, Enthalpy of vaporization). This energy is taken from the surrounding air or tissue leading to a decrease of their temperature. So how does this work in plants?
The extent of the transpiration depends, besides the plant’s internal water situation, on the following climatic conditions:
- solar radiation
- wind speed
- vapour pressure deficit of the air (VPD)
High temperature and high solar radiation warm up the leaf lamina. Increasing leaf lamina temperature increases evaporation in the stomatal pore and consequently transpiration. The other driving force is VPD in the air boundary layer near the lamina surface. In the stomata pore the VPD is assumed to be zero (the air is saturated with water vapor, 100 % relative humidity). A higher VPD, meaning dry air, outside the leave results in a higher transpiration rate. The transpiration itself subsequently decreases the VPD in the boundary layer of the leaf and therefore has a decreasing feedback loop on the transpiration rate. Now the role of the wind speed comes into effect: A high wind speed leads to a faster exchange of the boundary layer air volume. So, the air with the lower VPD is replaced by surrounding air which is in most cases drier. This air exchange has a positive effect on transpiration. The difference of VPD between inside and outside leaf increases faster with higher wind speed resulting in a higher transpiration rate.
More details see here:
1.4 Water in the substrate
As stated above plant production needs water. Plant tissue consists of dry matter produced by photosynthesis using air CO and water and by water itself. Both sum up to the fresh weight of a plant. But which water sources are used for this tissue production? Are there also losses in the production system? And taking the ecological footprint of plant production into account, how can we produce plants of the desired quality in a water efficient manner? The agricultural sector accounts for around 70 % of the global freshwater withdrawal (World Bank 2022). The availability of water in an acceptable quality for plant production for a reasonable price will decrease more and more due to higher use for industry and a higher municipal demand. Additionally, in some areas natural water resources are getting scarce due to climate change.
In greenhouse production many substrates are used. Crops can be planted directly in the natural soil of the greenhouse area or outside this soil. In the latter case inert substrates like e.g. rockwool or organic materials like e.g., peat, composts, coconut fibres, wood fibres, are used. One of the important characteristics of these materials is the water holding capacity (WHC). The WHC determines the amount of water which can be stored in the substrate and thereby determines the amount of water which can be given per dressing without inducing leaching and possible water loss. To avoid the need of intensive monitoring, reduce the number necessary irrigation actions and still assure an ample supply of water for the crop, substrates with high WHC are preferred.
Water loss in a substrate occurs, besides the uptake by the roots, by drainage and evaporation. In a greenhouse drainage happens if the substrate is supplied with a water amount which exceeds the WHC. In general, this can be avoided by a correct irrigation control. Sometimes drainage is done on purpose to leach high salt concentration from the substrate. A drainage must not be a loss of water if the irrigation system is constructed as closed system in which the drainage water is recirculated. Evaporation is a real loss and should be avoided or at least reduced by measures like covering or even wrapping the substrate using foils.
The transpiration of the plant and the evaporation of the soil sum up to the so called evapotranspiration.
1.5 Water uptake by roots
The water uptake by roots follows the potential gradient between the water potential in the substrate and the water potential in the roots. Water always flows from the higher to the lower potential. Be aware that the water potentials in both elements are negative. To suck water from the substrate, the roots need a more negative water potential than the substrate. This leads to the effect that not the whole water volume of the soil can be taken up by plants. Herbaceous plants have water potentials from around -0.2 to -1 MPa, trees and bushes down to -2.5 MPa and plants in arid regions can reach -10 MPa. A substrate which is filled with water (including the pores) has a water potential of 0 MPa. There are two important statutory thresholds of substrate water status: water holding capacity and permanent wilting point. Plants can access the amount of water between these two substrate conditions. How much water this is depends of the physical substrate properties as the distribution of substrate particle sizes and proportion and size of pores in the substrate. Looking from the side of the root also the intensity of rooting (expressed as root length density) and the root age determines the volume of water available for the plant. Concerning the root age there is the fact that older roots are more ineffective in taking up water. The highest uptake rate can be found in young growing roots and there especially in the root apex.
1.6 Irrigation & Fertigation
Plants need water to keep up their tissue turgor as well as transport nutrients, assimilates, phytohormones and other organic or inorganic substances through their vessels. Water is also necessary for numerous chemical reactions in the plant. So, in addition to an adequate above ground environment it is important to supply sufficient water to the substrate so that the plants don`t experience any deficiency. Sounds easy, but in practice a lot of parameters have to be taken into account to decide when, how much and how to irrigate. It even becomes more difficult if while irrigating also nutrients should be applied. The latter is called fertigation (a composition of the expressions ‘irrigation’ and ‘fertilisation’).
1.6.1 When to irrigate?
It would be easy to start irrigation in the moment that deficiency symptoms like beginning wilting of leaves start to be visible. But this is much too late. What could be other symptoms a plant shows on the way to drought stress? A first sign is the increase of leaf temperature due to the beginning closure of the stomata followed by lower transpiration cooling. This is difficult to measure because there are often much bigger short-term changes in leaf temperature due to varying radiation by e.g. clouds. Another indication is the decreasing water transport in the vessels. There are systems available to measure the ‘speed’ of the water column in the stem, but they are very sensible in the application. The transport rate also depends on other factors like changing radiation, leading to the fact that to derive existing stress is not trivial. Additional only one or some few plants of the whole crop are measured. These facts show that the plant itself is not a good indicator to derive irrigation measures.
Crop water stress index (CWSI) => should it be included?
TO BE CONTINUED
For a farmer there are two soil conditions which should be avoided. One is the so called waterlogging. In this situation there is the danger of root damage by a too low concentration of oxygen in the root environment. Crops differ in their sensitvity against waterlogging. The opposite situation is a too low water content of the substrate so that the roots are unable to take up water from the substrate. So the water content has to be in the range between these extremes. How can we measure and evaluate the substrate water condition? A good indicator is the percentage of the water holding capacity (WHC, %). The WHC defines how much water the substrate can hold against gravity in % of its volume. For a soil this is called field capacity (FC). Due to their different physical properties different substrates have different WHC values (s. Table $$, to be added). For production a substrate with a high WHC is positive because the irrigation frequency can be reduced. There is no fixed value for the WHC to be sufficient for unrestricted water uptake of the plant, because due to the physical properties of the substrate the waterpotential of the substrate by a given WHC is different (s. Table $$, to be added). As herbaceous plants have a waterpotential of -0.2 MPa to -1.0 MPa the waterpotential of the substrate should not be more negative to avoid drought stress. Remember from above the value of -1.5 MPa as the permanent wilting point (PWP).
Be aware of the fact that the WHC of a substrate can change during production due to compaction. The substrate gets more dense, so the WHC decreases.
To be added: tables of WHC of different substrates and water potentials of different substrates at different WHCs.
As instruments for measuring the water situation in the substrate mainly the following tools are available: tensiometer (measuring the matric potential in Pa), TDR or FDR sensors (time domain reflectometry/frequency domain reflectomety, measuring the volumetric water content of a soil in Vol. %). For the latter data about the relation between the volumetric water content and the matric potential of the specific substrate is necessary.
To be added: photos of tensiometers and TDR/FDR sensors
1.6.2 How much to irrigate?
If no uncontrolled irrigation is possible (e.g.rain in open field production) it might be assumed that it is optimal to irrigate to full WHC. But to avoid a too low oxygen concentration the substrate is normally only filled up to 90-95 % of the WHC. Otherwise there would also be the danger of producing drainage causing water and nutrient losses. In open field there should always be a buffer for a possible rainfall event.
If the production system is constructed as a closed system (means that excessive water is sampled and reused), then it is possible to give more then 100% of the WHC. One advantage ist that if some single drippers release less than expected, every plant gets sufficient water. The second advantage is that high salt concentrations in the slabs by fertigation can be flushed out.
1.6.3 How to irrigate?
To supply the plants with the irrigation water various methods are possible. If there is a production in the greenhouse directly in the soil sprayers can be used. This leads to a more or less all-over wetting of the soil. As a consequence there is a high loss of water by evaporation and a increase in air humidity. To reduce these effects drip irrigation is commonly used for irrigation in greenhouses. Out of a central reservoir water is pumped through main pipelines. In these pipelines valves are installed which open at a certain pressure and distribute the water through thin pipes (so called ‘spaghetti’) directly to the single pots. The irrigation event is triggered by the light sum. This light sum and also the duration of the irrigation event has to be changed from tme to time due to the growing demand of water by the growing crop.
Figure $$: Rockwool pot on a rookwool slab with two drippers for two tomato plants (Fricke).
Figure $$: Greenhouse after crop removal. Soil covered by mulch foil, slabs, main pipes and drippers still inside (Fricke)
To be added: spraying system for open soil production in a greenhouse.
Air boundary layer
The air boundary layer of a leaf is the layer of unstirred air around the leaf surface. The extend of this layer depends on windspeed (thinner layer under higher wind speed) and the leaf size (Taiz and Zeiger 2006).
The transition of liquid water (here water in the soil) to water vapour. Evaporation occurs on the soil surface, especially if the surface is wet. A dry soil surface acts as an isolation barrier against evaporation.
Field capacity (in substrates called ‘water holding capacity’) is the amount of water per volume (L m-3) or per g of soil (g g-1) which a soil can hold against gravity. It can also be expressed as suction (kPa) at this water content.
Permanent wilting point
The permanent wilting point (PWP) is defined as the minimum water volume in a substrate that the plant needs not to wilt. By convention the PWP is defined at −1.5 MPa of suction pressure. (Wikipedia, 2022, Permanent wilting point)
Leaf expansion rate (LER)
The LER is defined as increase of leaf area per e.g. unit of time (cm² d-1) or temperature sum (cm² °Cd-1)
Root length density
The Root Length Density (m m-3) decribes the intensity of rooting in the substrate volume.
A stoma (Wikipedia; Stoma) is a cell structure in leaves of plants which forms a pore. Water evaporates from the leaf mesophyll cells into the air filled pocket of the stoma inside the leaf. From there the vapour is transported to the air outside the leaf. The aperture of the stoma is controlled by guard cells.
Stomatal conductance/Stomatal resistance
Stomatal conductance (mmol m−2 s−1), expresses the net molar flux of CO2 entering or water vapor exiting the stomata of a leaf. Its inverse is called stomatal resistance (s m-1) (Wikipedia, 2022, Stomatal conductance)
Vapour-pressure deficit (VPD)
The VPD describes the difference (deficit) between the amount of water vapour in the air under saturated condition and the actual amount of water vapour (Wikipedia; vapour-pressure deficit). The unit is Pascal (Pa). It has to be taken into account that air of higher temperature is able to include a higher amount of water vapour. So the VPD depends on ambient temperature. Water vapour is transported in the air from a lower VPD to a higher VPD.
Water holding capacity
Water holding capacity (in natural soil ‘Field capacity’) is the amount of water per volume (L m-³) or per g of substrate (g g-1) which a substrate can hold against gravity. It can also be expressed as suction (kPa) at this water content.
Water transport in plants is described by the water potential concept.
The water potential Ψ (Psi) is defined as the potential energy of water per unit volume relative to pure water in reference conditions. It quantifies the tendency of water to move from one area to another due to osmosis, gravity, mechanical pressure, and matrix effects such as capillary action (caused by surface tension). (Wikipedia; Water potential)
Water always flows from a higher to a lower potential. Unit is Pascal (Pa).
Idso S B, Jackson R D, Pinter Jr. P J, Reginato R J, Hatfield J L (1981). Normalizing the stress-degree-day parameter for environmental variability. Agricultural Meteorology, 24, 45-55.
Nederhoff E M (1994). Effects of C02 concentration on photosynthesis, transpiration and production of greenhouse fruit vegetable crops. Dissertation. Agricultural University, Wageningen, The Netherlands. Page 2.
Stanghellini, C (1987). Transpiration of greenhouse crops. An aid to climate management. PhD Dissertation. Landbouwuniversiteit, Wageningen. 150 pp.
Taiz L, Zeiger E (2006). Plant Physiology, Fourth Edition. Sinauer Associates, Inc., Sunderland, MA, USA. P. 65. ISBN 0-87893-856-7.
World Bank (2022). Water in Agriculture. https://www.worldbank.org/en/topic/water-in-agriculture#1. Last retrieve 23.02.2022.
Wikipedia (2022). Different search terms as indicated in the text. https://en.wikipedia.org.
Yan H, Huang S, Zhang C, Coenders Gerrits M, Wang G, Zhang J, Zhao B, Acquah S, Wu H, Fu H (2020). Parameterization and application of Stanghellini model for estimating greenhouse cucumber transpiration. Water, 12, 517. doi:10.3390/w12020517.
3. Climate dynamics inside the Greenhouse
3.1. The Principles of Energy Balance
3.2. The Dynamics of Humidity
4. Crop-specific Physiology
4.1. Lettuce and Leafy Greens
Optimal Environment for Lettuce Production
Much research has been conducted looking into the optimal environment to produce lettuce cultivars, and key environmental values have been well established.
- The optimal daily light integral (DLI) for lettuce cultivars is between 15-17 mol. For lettuce grown in the winter/short day season, supplemental lighting will be beneficial to reach this DLI value. Of course, DLI can be lower in production but crop cycle may be lengthened as a result.
- Optimal temperature for lettuce production is around 23 C during the day, and around 17C at night. Lettuce is a cool-weather plant, and therefore its temperature requirements are cooler than most other indoor-grown crops. This can be difficult to maintain in greenhouses or glasshouses in warmer climates.
- Rising temperature can become a problem as it promoted bolting, when the lettuce begins to grow tall instead of wide and leads to flowering. Bolting also affects the flavor of the crop, often resulting in bitter taste.
- Low temperature can result in slowing the crop cycle. When temperature and light are optimal, lettuce can grow in 35-day crop cycles. The more temperature and light deviate, the longer the crop cycle can take, up to 80 days or more.
To learn more about lighting and temperature’s effects on different cultivars of lettuce, check out this publication.
Most Common Problems in Lettuce Production
Tipburn is primarily caused by the calcium deficiency at the tip of young leaves. This frequently happens to the young inner leaves. The underlying cause is two fold:
- Lack of airflow around the inner leaves. This causes high boundary layer resistance and hence poor transpiration. Poor transpiration leads to poor nutrient (including
Ca) transfer to the new inner leaves.
- Strong growth relatively to the transpiration capacity. Strong growth promote the formation of new tissues. However, since they don't receive enough nutrients to survive, they die young and become tipburn.
Tipburn is not a burn. It's a common myth, fueled by the term, that the plant got burned and hence growers solve it by reducing/turning off supplemental light or deploy shading so that the plants don't get burned. While there is a confounding factor between lighting and photosynthesis/transpiration and hence tipburn, lighting is not the primary cause of tipburn and suppressing it isn't always the optimal way of addressing the tipburn problem.
How to avoid tipburn
The most common nutrient deficiency in lettuce grown using hydroponics is iron deficiency. This deficiency appears as chlorosis (yellowing) of the leaves around the veins, usually affecting younger leaves first. The underlying cause can be two-fold:
- Lack of iron in the nutrient solution.
- High pH of nutrient solution, which negatively affects the absorption of iron. When pH rises too high (>6.5), iron may oxidize or precipitate, thus reducing its availability for absorption.
How to avoid iron deficiency:
To learn more about iron deficiency in lettuce, read this article.
4.3. Other Vine Crops
Most of the sections for tomato are also applicable for other vine crops such as cucumber, bell pepper, etc. Here, we only highlight the key differences.
Differences between Cucumber and Tomato
- Cucumbers don't grow in truss. There is 1 fruit and 1 leaf for each node of the cucumber plant.
- The fruit development rate of cucumber is relatively faster than the the fruit development rate of tomato
- Fruit aborting is a common phenomenon in cucumber. Fruit pruning is a common practice to avoid that.
- Temperature sum for the fruit to reach full potential: (compared to of tomato)
Specific leaf area index (m2 of leaf per mg of dry matter): 3.78e-5
(higher than that of tomato, which is 2.66e-5)
- Temperature: cucumbers enjoy a higher temperature than tomatoes do. According to [Marcelis], the daily averaged temperature for cucumbers is: 10 (lower bound), 20-25 (ideal), 35 (upper bound)
- Humidity: cucumber plants are also more tolerant to higher humidity [Hao]
6. Hardware tech to improve crop production
In this chapter, we focus on improvements that can be added to your existing greenhouse to achieve better productivity. For that reason, we will not discuss technologies that you can't easily adopt, such as greenhouse structure and glazing materials.
6.1. Movable Screens
6.2. Supplemental Lighting
6.3. Vertical Fans
Greenhouses and indoor farms require constant monitoring and environmental regulation to ensure optimal plant growth. Many factors such as humidity, temperature, light levels, irrigation and more must be constantly kept in check or your plants may suffer. Manually checking these conditions in your growing environment can be time-consuming and tedious. Not to mention the paranoia over whether a door was left open or leaks in irrigation equipment causing a climate catastrophe. Sensors are an excellent tool to help you monitor your growing environment quickly from anywhere and begin automating your indoor growing environment.
There are many different types of sensors available for greenhouses and indoor farms to constantly monitor and measure such variables. Sensors equipped with Internet of Things (IoT) technology are especially capable of transmitting the data collected to a data management system for remote access. For example, instead of manually checking a thermometer, a temperature sensor could be used to automatically collect this data and report it to an online server or data management system. The data can now be accessed remotely instead of having to walk into your greenhouse and check a wall-mounted thermostat.
Sensors can also increase the amount of observations you can make about your greenhouse. For example, temperature sensors can check the micro-climate of every growing zone in your greenhouse. This can be important because although it is optimal for the entire greenhouse to have the same exact temperature, you likely know that is not always the case realistically. Clouds blocking the light over certain sections, uneven drafts from fans and ventilation equipment, and uneven plant crowding as your crop matures can all play a part in offsetting the balance and consistency in your greenhouse climate variables. Realizing such differences, via the data collected by sensors, means that each area can be handled exactly as it needs to be to restore optimal climate for plant growth. This is how sensors are paving the way for precision agriculture.
Not only can sensors monitor your grow environment, but many also include the capability to respond to changes in the environment either automatically or manually. For example, you may set your temperature system to automatically begin heating if your temperature sensors register a lower-than-optimal temperature. On the other hand, you may be able to review your temperature sensor data remotely and make the decision to begin heating based on your observations, likely from a desktop or mobile app. Using sensors to create a “smart greenhouse” can save a lot of time and effort from constant manual climate monitoring, mitigate response time to and losses from unpredictable climate interruptions (i.e. leaving a door open), and collect valuable data about your grow system and how your plants respond to environmental changes.
7. Software tech and data-driven growing
In this chapter, we focus on the software technology that can unlock your sensor data’s true value, and the basics of data-driven growing. The data collected by your sensors is valuable on its own for the ease of remotely monitoring several growing conditions. However, analysis of this data can do so much more for your greenhouse production, such as improve and increase your yield, reduce your resource usage, and overall increase your profits. The name of this game is data-driven growing: using data in a proactive way to guide your growing. We outline the steps below to achieve data-driven growing, which are data collection and visualization, data analysis, and digital decisions, all of which are taken care of by software technology such as Koidra’s.
7.1. Data Collection and Visualization
The first step to improve your data-driven growing game is having a good data platform. You need to be able to collect and unify your data under one (digital) roof. A large spreadsheet of data points is not useful, we want easy-to-read charts that convey a message. The data needs to be visualized with tools such as operational dashboards, which growers can directly use for crop monitoring and decision making. Figures 2 and 3 provide examples of customizable dashboards with critical growing metrics such as light levels, CO2, temperature and more.
Before investing in a data platform, there are some important questions to ask: Is it another silo in your existing mix of data silos (i.e. isolated, unintegrated data)? Can it be integrated with your existing or new control systems? Can the data be turned into actionable insights? Once you find the data useful, can you operationalize upon that data (i.e. can the control system use it)?
Manually registered data
In addition to sensor data, we also recommend manually registering crop measurements on a low-frequency basis (such as day, week).
7.2. Advanced Decision Support System
7.2.1. Digital Decisions
Enterprises rise or fall based on the collective efficacy of all decisions made ... by leaders, ... by employees, ... by decision logic embedded in applications.
The decision logic embedded in applications are called
digtal decisions. In a nutshell, digital decisions are operational decisions in real-time or near-real-time. They are optimized by
- expert knowledge (this book!);
- sophisticated models to distill actionable insights from data (aka. AI);
- and data collection at scale (fueled by IoT technologies).
For greenhouse operations, digital decisions are primarily the automated climate control decisions although they may include some other actions in special settings (such as as the decision to move the gutters in a lettuce greenhouse to optimize the plant density in real time). Below is a schematic of a digital decision making system for a greenhouse.
7.x. Be aware of the high-interest technical debt
[to be written]
A data silo is a collection of data that is kept in a system that is not easily or openly accessible, sometimes even to those who generated the data. Data silos are unfortunately common in smart greenhouses that use sensors. Oftentimes sensor companies will collect the data and “silo” it into their own private management system, while only giving consumers access to select data points. For example, a temperature sensor may show you the temperature for the last 24 hours, but will not allow you to access the temperatures it recorded last season or even last week.
Another way data can be “siloed” is if data is not kept altogether in a central location. This case occurs commonly in greenhouses or indoor farms that use different types of sensors from multiple companies. One company may store the temperature and humidity data in their silo, but the lighting company stores their data in their own silo. Even when the data is accessible, it is often “spaghetti-ed”, for example in long excel sheets that require massive amounts of scrolling. No simple cut-and-paste could seamlessly combine the data from different silos.
We know the climate and crop variables inside greenhouses interact with and influence each other. Therefore, in order to make any type of data-driven growing decision, growers need access to all of their data in one place.
Digital Horticulture Roadmap
8. About Authors
Andreas Fricke, Senior lecturer, Leibniz Universität Hannover · Institute of Horticultural Production Systems
Andreas is a senior lecturer at the University of Hannover, Institute of Vegetable Systems Modelling. He studied Horticultural Sciences and received his Ph.D. in 1992. He has specialized himself on stress physiology and harvest prediction models, both in the field of vegetable crops. As lecturer he teaches courses in the study programs ‘Molecular and Applied Plant Science’ (B.Sc.), ‘Plant Biotechnology’ (M.Sc.) and ‘International Horticulture’ (M.Sc.).
Simon Schmitz, Ph.D. candidate, Leibniz Universität Hannover · Institute of Horticultural Production Systems
Simon is a Ph.D student at the University of Hannover, Institute of Vegetable Systems Modelling. He studied Biotechnology (B.SC) and Plant-Biotechnology (M.Sc). He has specialized himself on short term stress response in the physiology of plants and remote sensing technology to detect such stresses.
Kenneth Tran, CEO, Koidra Inc.
Kenneth (Ken) is the founder and CEO of Koidra Inc. Before Koidra, Ken was a Principal Applied Scientist in the Machine Learning Group, Microsoft Research (MSR). While at MSR, he led the Sonoma team, winning the first autonomous greenhouse challenge (2018), becoming the only artificial intelligence (AI) team that outperformed expert Dutch growers. Ken recently led the Koala team comprised of Koidra and Cornell University researchers to win the first phase of the 3rd autonomous greenhouse challenge in 2021.
Ken’s research expertise and experience include Reinforcement Learning, Physics-informed Machine Learning, and Deep Learning. Tran received his Ph.D. in Computational & Applied Mathematics from The University of Texas at Austin.