Introduction to Plant Ecophysiology, Phenotyping and Phenomics #

Plant ecophysiology studies how physiology responds to environmental conditions. This includes plant mechanisms that sense the environment and that act in response to this sensing, but also how environmental conditions affect the overall growth and development of plants.

In this chapter, we will give a brief overview of a plant’s ecophysiology. Additionally, we also discuss the basics of phenotyping and measurement methods. The focus will be on the leaves since these are the primary interfaces with the (above ground) environment and are the easiest to monitor. Moreover, we will restrict ourselves to photosynthesis and water-related processes since monitoring focuses thereon in later chapters.

Plant Ecophysiology #

Plants are sessile organisms. This restriction has forced them to evolve a wide range of unique characteristics that enable them to survive and thrive in challenging environments all over the globe. Environmental influences are categorised as biotic or abiotic. Biotic factors involve other living organisms, including, for example, symbiotic relationships with bacteria and fungi in the soil, pests and diseases, and pollination by insects. Abiotic factors are all the other factors, such as (solar) radiation, soil and air temperature, precipitation, wind direction and speed, water and nutrient availability, and relative humidity.

Due to this sessile nature, plants cannot collect food. Instead, they synthesise their food from atmospheric carbon dioxide (CO2) and (soil) water. An organism that can synthesise food from the environment is also called an autotroph. Organisms that depend upon other organisms as a food source are called heterotroph.

Put simply, plants collect solar energy and convert it to chemical energy. This process is summarised as 6CO₂ + 6H₂O → C₆H₁₂O₆ + 6O₂. Consequently, they provide their own food from two abundant resources in nature. While light is required, not all wavelengths are equally effective for photosynthesis. Light in the range of 400-700nm can be used for photosynthesis. This is the photosynthetically active radiation (PAR), expressed in μmol/m²/s (i.e. the number of photons incident per unit of area and time).

While the chemical formula for the synthesis of sugars during photosynthesis seems simple, the processes involved in photosynthesis are far from simple. The process of photosynthesis consists of two main reactions: light-dependent reactions and dark reactions. During the light-dependent reactions, light energy is converted into chemical energy. This chemical energy cannot be stored for a long time. The energy is thus converted into glucose by the dark reactions (Citation: Taiz & Zeiger, 2010) Taiz, L. & Zeiger, E. (2010). Plant Physiology, Fifth Edition (Fifth edition). Sinauer Associates, Inc.. .

Figure 3.1 depicts a typical plant. Water is mostly absorbed in the roots but can also enter via the leaves and other organs (Citation: Rundel, 1982 Rundel, P. (1982). Water Uptake by Organs Other Than Roots. InLange, O., Nobel, P., Osmond, C. & Ziegler, H. (Eds.), Physiological Plant Ecology II: Water Relations and Carbon Assimilation. (pp. 111–134). Springer. https://doi.org/10.1007/978-3-642-68150-9_5 ; Citation: Berry, Emery& al., 2019) Berry, Z., Emery, N., Gotsch, S. & Goldsmith, G. (2019). Foliar water uptake: Processes, pathways, and integration into plant water budgets. Plant, Cell & Environment, 42(2). 410–423. https://doi.org/https://doi.org/10.1111/pce.13439 . From there, it is transported through specialised organs to sites of transpiration, located predominately on the leaves. Plants have dedicated tissues to facilitate water transport, such as xylem. Though water is used in the process of photosynthesis, most of the water is lost through evaporation in the stomata.

Stomata (Figure 3.2) are pores in the epidermis of leaves, stems and other plant organs that regulate the gas exchange of the plant. This regulation is very important, since plants continuously have to strike a balance between CO2 uptake from the air and water loss to avoid desiccation (Citation: Meeus, Van den Bulcke& al., 2020) Meeus, S., Van den Bulcke, J. & wyffels, F. (2020). From leaf to label: A robust automated workflow for stomata detection. Ecology and Evolution, 10(17). 9178–9191. https://doi.org/10.1002/ece3.6571 . The state of the stomata (open or closed) is quantitatively expressed as stomatal conductance in mol (H₂O)/m²/s (Citation: Gimenez, Gallardo& al., 2005 Gimenez, C., Gallardo, M. & Thompson, R. (2005). PLANT–WATER RELATIONS. InHillel, D. (Eds.), Encyclopedia of Soils in the Environment. (pp. 231–238). Elsevier. https://doi.org/10.1016/B0-12-348530-4/00459-8 ; Citation: , 2012) (2012). Using the LI‐6400 Portable Photosynthesis System. . This quantity is also related to the transpiration rate, expressed in mmol(H₂O)/m²/s.

The net uptake of carbon dioxide from the atmosphere per leaf area is the photosynthetic rate μmol (CO₂)/m²/s, which is influenced by water and light availability as well as other environmental inputs like temperature and relative humidity and the plant organ’s age and water availability. During the day, most plants perform photosynthesis and thus, there is a net uptake of CO2. However, during the night, plants burn part of this energy to maintain essential plant processes. As a result, there is a release of CO2 during the night.

Temperature and relative humidity jointly cause the vapour pressure deficit. This is the difference in actual water concentration in the air and the saturated concentration, expressed in kPa (is equivalent to the free energy per unit volume J/m). The larger this difference, the larger the transpiration rate becomes since water “escapes” more easily to the environment.

One of the means a plant has to actively regulate the leaf temperature is through transpiration. Yet, there are limits to the amount of transpiration. These arise due to the combined effect of limited water availability in the soil and hydraulic resistance along the water transport pathway. The leaf temperature can thus be significantly different from the surrounding air temperature (Citation: Jones, 2013) Jones, H. (2013). Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology (3). Cambridge University Press. https://doi.org/10.1017/CBO9780511845727 . This effect is illustrated in Figure 3.3 for a soy trial. Figure 3.3a depicts a field that received normal rainfall, while Figure 3.3b was subject to drought. A rainout shelter prevented rain from entering the soil on this field. As a result, soil water availability in (a) was lower than in (b). Lower water availability caused plants to close their stomata to prevent excessive water losses, causing the temperature in the canopy to rise. If this increased high temperature and low soil water availability conditions persist, plants will eventually die.

In Figure 3.3a, all soy varieties performed equally well. This is to be expected since plants were not stressed (there is enough water available in the soil). However, in Figure 3.3b this was not the case. Plants in the upper halve were more stressed than those in the lower half, as is indicated by the higher temperatures. Lower canopy temperatures are used as a proxy for improved resilience to drought stress.

All of the above quantities and relationships illustrate that plants have a complex interplay of regulatory mechanisms. As a result, a plant’s state and development are the result of both internal and external factors. Determining and/or quantifying these parameters is called phenotyping.

The term phenotyping was introduced by Wilhelm Johannsen (Citation: Johannsen, 1903 Johannsen, W. (1903). Über Erblichkeit in Populationen und in reinen Linien. ; Citation: Johannsen, 1911) Johannsen, W. (1911). The Genotype Conception of Heredity. The American Naturalist, 45(531). 129–159. https://doi.org/10.1086/279202 ; it is the quantitative or qualitative description of an organisms’ observable characteristics or traits. For instance, one can measure the total plant length of maize. This is a phenotypic trait and the result of the genotype, environmental factors and their interaction (Citation: Walter, Liebisch& al., 2015) Walter, A., Liebisch, F. & Hund, A. (2015). Plant phenotyping: from bean weighing to image analysis. Plant Methods, 11(1). 14. https://doi.org/10.1186/s13007-015-0056-8 . Different environmental factors can result in a diverse set of phenotypes, making it sometimes difficult to relate genotypic factors to specific traits (Citation: Xu, 2016) Xu, Y. (2016). Envirotyping for deciphering environmental impacts on crop plants. Theoretical and Applied Genetics, 129(4). 653–673. https://doi.org/10.1007/s00122-016-2691-5 . But even if the environmental factors are identical, plants can have very different gene expression (Citation: Cortijo, Aydin& al., 2019) Cortijo, S., Aydin, Z., Ahnert, S. & Locke, J. (2019). Widespread inter-individual gene expression variability in Arabidopsis thaliana. Molecular Systems Biology, 15(1). e8591. https://doi.org/10.15252/msb.20188591 .

In literature, phenotyping and phenomics are often used interchangeably, though there is a clear distinction between the two. Phenomics is the study of plant growth, performance and composition (Citation: Furbank & Tester, 2011) Furbank, R. & Tester, M. (2011). Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science, 16(12). 635–644. https://doi.org/10.1016/j.tplants.2011.09.005 . It provides a more holistic view than phenotyping and was coined in analogy to genotyping and genomics. Genotyping studies the properties of a limited set of genes, while genomics is interested in the genome as a whole. However, there are also clear differences. While determining the genome is a well-defined problem, the phenome cannot be uniquely characterised since it can change in time and space, even on a cellular basis. Consequently, there is always a focus on a particular aspect of the phenome (Citation: Houle, Govindaraju& al., 2010) Houle, D., Govindaraju, D. & Omholt, S. (2010). Phenomics: the next challenge. Nature Reviews Genetics, 11(12). 855–866. https://doi.org/10.1038/nrg2897 . In summary, phenotyping studies detailed properties of the plant, while phenomics is more holistic.

Recently, the term envirotyping was introduced by Citation: Xu (2016) Xu, Y. (2016). Envirotyping for deciphering environmental impacts on crop plants. Theoretical and Applied Genetics, 129(4). 653–673. https://doi.org/10.1007/s00122-016-2691-5 . It is also related to phenotyping and genotyping in that it highlights the need to accurately monitor environmental changes. A single measurement location for weather data for a field is often inadequate since many parameters differ significantly over small distances. For instance, the soil is known to be very heterogeneous, or local temperature fluctuations due to, e.g., tree shading can cause differences in phenotype that cannot be explained by just monitoring or considering the macroclimate (Citation: De Frenne, Zellweger& al., 2019 De Frenne, P., Zellweger, F., Rodríguez-Sánchez, F., Scheffers, B., Hylander, K., Luoto, M., Vellend, M., Verheyen, K. & Lenoir, J. (2019). Global buffering of temperatures under forest canopies. Nature Ecology & Evolution, 3(5). 744–749. https://doi.org/10.1038/s41559-019-0842-1 ; Citation: Bennie, Huntley& al., 2008) Bennie, J., Huntley, B., Wiltshire, A., Hill, M. & Baxter, R. (2008). Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecological Modelling, 216(1). 47–59. https://doi.org/10.1016/j.ecolmodel.2008.04.010 .

From Phenotyping to Phenomics #

Phenotyping is the quantification of certain plant traits that are the result of the interaction between plant genetics and environmental conditions to which plants are exposed. Consequently, phenotyping is applied in many plant science disciplines. In fundamental plant science, phenotypic observations are used to discover novel mechanistic insights into a plant’s (eco)physiology. This is often referred to as deep phenotyping because it aims to get a deeper insight on mechanisms, and studies are generally performed on a few plants. But also, in more applied research like precision agriculture or breeding research, plant phenotyping plays a central role. Here, it is also often called high-throughput phenotyping because many genotypes are observed.

Due to the effects of climate change, meteorological conditions are expected to become more extreme (Citation: Rockström, Steffen& al., 2009 Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin, F., Lambin, E., Lenton, T., Scheffer, M., Folke, C., Schellnhuber, H., Nykvist, B., Wit, C., Hughes, T., Leeuw, S., Rodhe, H., Sörlin, S., Snyder, P., Costanza, R., Svedin, U., Falkenmark, M., Karlberg, L., Corell, R., Fabry, V., Hansen, J., Walker, B., Liverman, D., Richardson, K., Crutzen, P. & Foley, J. (2009). Planetary Boundaries: Exploring the Safe Operating Space for Humanity. Ecology and Society, 14(2). https://doi.org/10.5751/ES-03180-140232 ; Citation: Steffen, Richardson& al., 2015) Steffen, W., Richardson, K., Rockström, J., Cornell, S., Fetzer, I., Bennett, E., Biggs, R., Carpenter, S., Vries, W., Wit, C., Folke, C., Gerten, D., Heinke, J., Mace, G., Persson, L., Ramanathan, V., Reyers, B. & Sörlin, S. (2015). Planetary boundaries: Guiding human development on a changing planet. Science, 347(6223). https://doi.org/10.1126/science.1259855 . To maintain productivity and feed a growing human population, breeders have to create new crop varieties that maintain or even improve productivity under these new conditions. Moreover, farming has to become more sustainable without affecting productivity (Citation: Foley, Ramankutty& al., 2011 Foley, J., Ramankutty, N., Brauman, K., Cassidy, E., Gerber, J., Johnston, M., Mueller, N., O’Connell, C., Ray, D., West, P., Balzer, C., Bennett, E., Carpenter, S., Hill, J., Monfreda, C., Polasky, S., Rockström, J., Sheehan, J., Siebert, S., Tilman, D. & Zaks, D. (2011). Solutions for a cultivated planet. Nature, 478(7369). 337–342. https://doi.org/10.1038/nature10452 ; Citation: Xu, 2016) Xu, Y. (2016). Envirotyping for deciphering environmental impacts on crop plants. Theoretical and Applied Genetics, 129(4). 653–673. https://doi.org/10.1007/s00122-016-2691-5 .

Genetic gain, the increase in performance achieved per unit time through artificial selection, is diminishing for several key crops, including wheat, maise and rice (Citation: Fischer, Byerlee& al., 2014 Fischer, T., Byerlee, D. & Edmeades, G. (2014). Crop yields and global food security: will yield increase continue to feed the world?. ACIAR. ; Citation: Sadras, Lawson& al., 2011 Sadras, V., Lawson, C., Sadras, V. & Lawson, C. (2011). Genetic gain in yield and associated changes in phenotype, trait plasticity and competitive ability of South Australian wheat varieties released between 1958 and 2007. Crop and Pasture Science, 62(7). 533–549. https://doi.org/10.1071/CP11060 ; Citation: Acreche, Briceño-Félix& al., 2008) Acreche, M., Briceño-Félix, G., Sánchez, J. & Slafer, G. (2008). Physiological bases of genetic gains in Mediterranean bread wheat yield in Spain. European Journal of Agronomy, 28(3). 162–170. https://doi.org/10.1016/j.eja.2007.07.001 . However, developing new crop varieties is labour and time-intensive. While genotyping has evolved significantly over the past 20~years, leading to high-throughput and inexpensive sequencing, phenotyping has not experienced similar gains. This mismatch between genotyping and phenotyping throughput is often referred to as the phenotyping bottleneck (Citation: Costa, Schurr& al., 2019) Costa, C., Schurr, U., Loreto, F., Menesatti, P. & Carpentier, S. (2019). Plant Phenotyping Research Trends, a Science Mapping Approach. Frontiers in Plant Science, 9. https://doi.org/10.3389/fpls.2018.01933 . Consequently, a large scientific community is focussing on alleviating this issue.

Citation: Araus, Kefauver& al. (2018) Araus, J., Kefauver, S., Zaman-Allah, M., Olsen, M. & Cairns, J. (2018). Translating High-Throughput Phenotyping into Genetic Gain. Trends in Plant Science, 23(5). 451–466. https://doi.org/10.1016/j.tplants.2018.02.001 identified five ways to increase genetic gain through high-throughput phenotyping: (i) increasing the size of the breeding programme, (ii) faster breeding cycles, (iii) more accurate selection, (iv) adequate genetic variation and (v) decision support tools. All of these aspects of the breeding pipeline require reliable high-throughput phenotyping techniques to scale breeding efforts cost-effectively. Breeding efforts thus transition from classic phenotyping to high-throughput phenotyping. Yet, this transition alone is probably insufficient. An increasing body of research is also indicating that a more integrated look upon phenotyping is needed, leading to phenomics. In the future, it is thus expected to see a transition towards high-throughput phenomics (Citation: Yang, Feng& al., 2020) Yang, W., Feng, H., Zhang, X., Zhang, J., Doonan, J., Batchelor, W., Xiong, L. & Yan, J. (2020). Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. Molecular Plant, 13(2). 187–214. https://doi.org/10.1016/j.molp.2020.01.008 . A large population of plants is thus studied as a whole instead of as a limited set of traits (Citation: Furbank & Tester, 2011 Furbank, R. & Tester, M. (2011). Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science, 16(12). 635–644. https://doi.org/10.1016/j.tplants.2011.09.005 ; Citation: Tardieu, Cabrera-Bosquet& al., 2017) Tardieu, F., Cabrera-Bosquet, L., Pridmore, T. & Bennett, M. (2017). Plant Phenomics, From Sensors to Knowledge. Current Biology, 27(15). R770–R783. https://doi.org/10.1016/j.cub.2017.05.055 . The goal is not to characterise all traits, but instead offer more context on why these traits are observed. We thus imply that there should be less focus on linking specific traits to treatments, but instead approach the effect of a treatment on the observed traits as a whole.

However, determining or quantifying traits is not easy nor always objective. Breeders often rely on manual visual scoring of plants to determine the phenotype. For instance, they often assign a score to each plant’s performance in a drought experiment. This is both time-consuming labour intensive and can introduce bias into observations (Citation: Ali, Cawkwell& al., 2016) Ali, I., Cawkwell, F., Dwyer, E., Barrett, B. & Green, S. (2016). Satellite remote sensing of grasslands: from observation to management. Journal of Plant Ecology, 9(6). 649–671. https://doi.org/10.1093/jpe/rtw005 . Additionally, destructive sampling is also often performed to obtain dry matter weight, for instance. While this yields very informative results, it poses additional restrictions on the size of studies and influences plant responses because of this intervention. To alleviate this bias and increase the overall throughput, the focus in high-throughput phenotyping is on remote sensing using imaging sensors (Citation: Araus, Kefauver& al., 2018) Araus, J., Kefauver, S., Zaman-Allah, M., Olsen, M. & Cairns, J. (2018). Translating High-Throughput Phenotyping into Genetic Gain. Trends in Plant Science, 23(5). 451–466. https://doi.org/10.1016/j.tplants.2018.02.001 .

A wide range of image sensors and image analysis techniques are used to determine traits. Both active and passive sensors are used, including laser imaging, detection, and ranging (LiDAR), synthetic aperture radar (SAR), ground penetrating radar (GPR), and visual/red green blue (RGB), hyperspectral, multispectral and thermal cameras (Citation: Araus, Kefauver& al., 2018 Araus, J., Kefauver, S., Zaman-Allah, M., Olsen, M. & Cairns, J. (2018). Translating High-Throughput Phenotyping into Genetic Gain. Trends in Plant Science, 23(5). 451–466. https://doi.org/10.1016/j.tplants.2018.02.001 ; Citation: Fahlgren, Gehan& al., 2015 Fahlgren, N., Gehan, M. & Baxter, I. (2015). Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Current Opinion in Plant Biology, 24. 93–99. https://doi.org/10.1016/j.pbi.2015.02.006 ; Citation: Li, Zhang& al., 2014) Li, L., Zhang, Q. & Huang, D. (2014). A Review of Imaging Techniques for Plant Phenotyping. Sensors, 14(11). 20078–20111. https://doi.org/10.3390/s141120078 . These image-based techniques are well-suited to analyse large fields with plants in a semi-automated fashion. The resulting datasets offer information at high spatial resolution. Which sensor is most suitable depends on the trait to be measured; for a (non-exhaustive) overview of possibilities, see Table 3.1.

In summary, there are currently two trends in phenotyping research relevant here: a shift towards a more holistic view of the plant through phenomics and a shift to larger experimental sizes by means of high-throughput techniques. High-throughput techniques have become mainstream over the last decade. Additionally, the more holistic view characterised by phenomics is also gaining traction in the community. However, a third axis can be identified, which is currently not yet as accepted as the other two: the time scale at which measurements are recorded.

The Temporal-Spatial-Trait Axis #

So far, the focus has been on the spatial resolution: the number of plants monitored (high-throughput) and the number of traits monitored (phenomics). The same is true in contemporary research in phenotyping: high temporal resolution is often lacking. It is uncommon to have information at the second to minute or even hour to daily time scale for large fields.

In high-throughput phenotyping and phenomics, image sensors are mounted on drones or mechanical systems that scan the plot one part at a time. Afterwards, images are combined into a single orthophoto. Using drone flights to collect data also introduces a new constraint: suitable weather. In conditions with precipitation and/or high wind speed, it is not possible to collect data. To increase the time-resolution, either the number of sensors has to be increased, or the number of monitored plants has to decrease. Often, the second option is preferred due to the high costs associated with the first method. To tackle the requirement for suitable weather, we can employ non-drone based monitoring platforms such as stationary platforms or phenomobiles (Citation: Araus, Kefauver& al., 2018) Araus, J., Kefauver, S., Zaman-Allah, M., Olsen, M. & Cairns, J. (2018). Translating High-Throughput Phenotyping into Genetic Gain. Trends in Plant Science, 23(5). 451–466. https://doi.org/10.1016/j.tplants.2018.02.001 .

Also, in more fundamental research, time aspects are often ignored. Many measurements are recorded at steady-state, yet in reality, the plant is rarely in steady-state conditions due to a highly fluctuating environment (Citation: Schurr, Walter& al., 2006 Schurr, U., Walter, A. & Rascher, U. (2006). Functional dynamics of plant growth and photosynthesis – from steady-state to dynamics – from homogeneity to heterogeneity. Plant, Cell & Environment, 29(3). 340–352. https://doi.org/10.1111/j.1365-3040.2005.01490.x ; Citation: Arsova, Foster& al., 2020) Arsova, B., Foster, K., Shelden, M., Bramley, H. & Watt, M. (2020). Dynamics in plant roots and shoots minimize stress, save energy and maintain water and nutrient uptake. New Phytologist, 225(3). 1111–1119. https://doi.org/10.1111/nph.15955 . As a result, plants continuously adapt their physiology in response to these environmental fluctuations. This determines their performance, both in natural ecosystems, as well as in crop systems (Citation: Schurr, Walter& al., 2006 Schurr, U., Walter, A. & Rascher, U. (2006). Functional dynamics of plant growth and photosynthesis – from steady-state to dynamics – from homogeneity to heterogeneity. Plant, Cell & Environment, 29(3). 340–352. https://doi.org/10.1111/j.1365-3040.2005.01490.x ; Citation: Arsova, Foster& al., 2020) Arsova, B., Foster, K., Shelden, M., Bramley, H. & Watt, M. (2020). Dynamics in plant roots and shoots minimize stress, save energy and maintain water and nutrient uptake. New Phytologist, 225(3). 1111–1119. https://doi.org/10.1111/nph.15955 . Their dynamic behaviour might even be more important than steady state-conditions (Citation: Kaiser, Morales& al., 2018 Kaiser, E., Morales, A. & Harbinson, J. (2018). Fluctuating Light Takes Crop Photosynthesis on a Rollercoaster Ride. Plant Physiology, 176(2). 977–989. https://doi.org/10.1104/pp.17.01250 ; Citation: Kromdijk, Głowacka& al., 2016 Kromdijk, J., Głowacka, K., Leonelli, L., Gabilly, S., Iwai, M., Niyogi, K. & Long, S. (2016). Improving photosynthesis and crop productivity by accelerating recovery from photoprotection. Science, 354(6314). 857–861. https://doi.org/10.1126/science.aai8878 ; Citation: Vialet-Chabrand, Matthews& al., 2017 Vialet-Chabrand, S., Matthews, J., Simkin, A., Raines, C. & Lawson, T. (2017). Importance of Fluctuations in Light on Plant Photosynthetic Acclimation. Plant Physiology, 173(4). 2163–2179. https://doi.org/10.1104/pp.16.01767 ; Citation: Matthews, Vialet-Chabrand& al., 2018 Matthews, J., Vialet-Chabrand, S. & Lawson, T. (2018). Acclimation to Fluctuating Light Impacts the Rapidity of Response and Diurnal Rhythm of Stomatal Conductance. Plant Physiology, 176(3). 1939–1951. https://doi.org/10.1104/pp.17.01809 ; Citation: Townsend, Retkute& al., 2018) Townsend, A., Retkute, R., Chinnathambi, K., Randall, J., Foulkes, J., Carmo-Silva, E. & Murchie, E. (2018). Suboptimal Acclimation of Photosynthesis to Light in Wheat Canopies. Plant Physiology, 176(2). 1233–1246. https://doi.org/10.1104/pp.17.01213 .

Consequently, to capture the effect of genotypic variation in plants, there is a need to measure on three different axes, identified in Figure 3.4. The point “perfect balance” is the only point where each axis is equally important, and the experiment does not sacrifice one of the aspects over the other. Three regions are indicated: high-throughput phenotyping, phenomics and deep phenotyping. In each of these regions, there is a clear dominance of one of the measurement axis.

For example, because the response of the photosynthesis biochemistry to fluctuating light conditions is faster than the kinetics of stomatal conductance, these fluctuations also impact the interplay between plant water and carbon relations (Citation: Lawson, Kramer& al., 2012 Lawson, T., Kramer, D. & Raines, C. (2012). Improving yield by exploiting mechanisms underlying natural variation of photosynthesis. Current Opinion in Biotechnology, 23(2). 215–220. https://doi.org/10.1016/j.copbio.2011.12.012 ; Citation: Lawson & Blatt, 2014) Lawson, T. & Blatt, M. (2014). Stomatal Size, Speed, and Responsiveness Impact on Photosynthesis and Water Use Efficiency. Plant Physiology, 164(4). 1556–1570. https://doi.org/10.1104/pp.114.237107 . Consequently, a mismatch arises between CO₂ assimilation and water loss (Citation: McAusland, Vialet‐Chabrand& al., 2016) McAusland, L., Vialet‐Chabrand, S., Davey, P., Baker, N., Brendel, O. & Lawson, T. (2016). Effects of kinetics of light-induced stomatal responses on photosynthesis and water-use efficiency. New Phytologist, 211(4). 1209–1220. https://doi.org/10.1111/nph.14000 . Reducing this mismatch and improving the capacity of crop photosynthesis to respond to fluctuating light environments is, therefore, a promising avenue for breeding more productive crop varieties (Citation: Salter, Merchant& al., 2019 Salter, W., Merchant, A., Richards, R., Trethowan, R. & Buckley, T. (2019). Rate of photosynthetic induction in fluctuating light varies widely among genotypes of wheat. Journal of Experimental Botany, 70(10). 2787–2796. https://doi.org/10.1093/jxb/erz100 ; Citation: Murchie & Ruban, 2020) Murchie, E. & Ruban, A. (2020). Dynamic non-photochemical quenching in plants: from molecular mechanism to productivity. The Plant Journal, 101(4). 885–896. https://doi.org/10.1111/tpj.14601 .

Given the importance of plant physiological responses to environmental fluctuations, it is essential that new field phenotyping technologies specifically focus on capturing such fast-changing dynamics (Citation: Murchie, Kefauver& al., 2018) Murchie, E., Kefauver, S., Araus, J., Muller, O., Rascher, U., Flood, P. & Lawson, T. (2018). Measuring the dynamic photosynthome. Annals of Botany, 122(2). 207–220. https://doi.org/10.1093/aob/mcy087 . Yet, it remains difficult to capture plant photosynthetic and water status responses to fluctuating conditions in the field. Gas exchange devices based on infrared gas analyser (IRGA) allow continuous measurements of transpiration and CO₂ assimilation and capture detailed dynamics (Citation: Kromdijk, Głowacka& al., 2016) Kromdijk, J., Głowacka, K., Leonelli, L., Gabilly, S., Iwai, M., Niyogi, K. & Long, S. (2016). Improving photosynthesis and crop productivity by accelerating recovery from photoprotection. Science, 354(6314). 857–861. https://doi.org/10.1126/science.aai8878 . However, this approach does not allow for high-throughput measurements and requires expensive devices. Furthermore, these systems monitor individual leaves and do not provide concurrent data at the plant scale, while recent evidence points out that plants display systemic responses under fluctuating light conditions (Citation: Shimadzu, Seo& al., 2019) Shimadzu, S., Seo, M., Terashima, I. & Yamori, W. (2019). Whole Irradiated Plant Leaves Showed Faster Photosynthetic Induction Than Individually Irradiated Leaves via Improved Stomatal Opening. Frontiers in Plant Science, 10. https://doi.org/10.3389/fpls.2019.01512 .

Chlorophyll fluorescence imaging is a powerful method to monitor the photosynthetic capacity of plants (Citation: Baker, 2008 Baker, N. (2008). Chlorophyll Fluorescence: A Probe of Photosynthesis In Vivo. Annual Review of Plant Biology, 59(1). 89–113. https://doi.org/10.1146/annurev.arplant.59.032607.092759 ; Citation: Murchie & Lawson, 2013) Murchie, E. & Lawson, T. (2013). Chlorophyll fluorescence analysis: a guide to good practice and understanding some new applications. Journal of Experimental Botany, 64(13). 3983–3998. https://doi.org/10.1093/jxb/ert208 . However, these measurements typically require a dark adaptation period of one hour, which limits the applicability to study short-term dynamics. New developments in chlorophyll fluorescence imaging methods like light-induced fluorescence transient (LIFT) or sun-induced fluorescence (SIF) overcome this dark adaptation period and can be used as proxies. These methods can be applied at different scales and show great promise, though they do not enable the acquisition of absolute photosynthesis biochemistry data and still require extensive calibration (Citation: Murchie & Ruban, 2020 Murchie, E. & Ruban, A. (2020). Dynamic non-photochemical quenching in plants: from molecular mechanism to productivity. The Plant Journal, 101(4). 885–896. https://doi.org/10.1111/tpj.14601 ; Citation: Bandopadhyay, Rastogi& al., 2020) Bandopadhyay, S., Rastogi, A. & Juszczak, R. (2020). Review of Top-of-Canopy Sun-Induced Fluorescence (SIF) Studies from Ground, UAV, Airborne to Spaceborne Observations. Sensors, 20(4). 1144. https://doi.org/10.3390/s20041144 .

Moreover, chlorophyll fluorescence imaging is unable to monitor stomatal conductance. Because stomatal conductance is closely related to leaf temperature, thermal sensors can be used to monitor it by applying basic energy balance equations (Citation: Jones, 2004 Jones, H. (2004). Irrigation scheduling: advantages and pitfalls of plant-based methods. Journal of Experimental Botany, 55(407). 2427–2436. https://doi.org/10.1093/jxb/erh213 ; Citation: Maes & Steppe, 2012) Maes, W. & Steppe, K. (2012). Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review. Journal of Experimental Botany, 63(13). 4671–4712. https://doi.org/10.1093/jxb/ers165 . These equations require the assessment of the micro-environmental conditions of the leaf and the boundary layer resistance to water vapour (Citation: Jones, Stoll& al., 2002) Jones, H., Stoll, M., Santos, T., Sousa, C., Chaves, M. & Grant, O. (2002). Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. Journal of Experimental Botany, 53(378). 2249–2260. https://doi.org/10.1093/jxb/erf083 . Although most studies with thermal sensors use single time point observations, continuous monitoring of dynamic stomatal conductance in response to a fluctuating environment is possible and can be combined with chlorophyll fluorescence imaging to link plant water relations and photosynthesis (Citation: McAusland, Vialet‐Chabrand& al., 2016) McAusland, L., Vialet‐Chabrand, S., Davey, P., Baker, N., Brendel, O. & Lawson, T. (2016). Effects of kinetics of light-induced stomatal responses on photosynthesis and water-use efficiency. New Phytologist, 211(4). 1209–1220. https://doi.org/10.1111/nph.14000 .

Generally, many phenotyping methods use imagery to extract traits. Examples include, but are not limited to, detection of biotic and abiotic stress and estimation of nitrogen content and yield. Citation: Mir, Reynolds& al. (2019) Mir, R., Reynolds, M., Pinto, F., Khan, M. & Bhat, M. (2019). High-throughput phenotyping for crop improvement in the genomics era. Plant Science, 282. 60–72. https://doi.org/10.1016/j.plantsci.2019.01.007 provides an overview of current methods. In this respect, broadband RGB cameras are often used in phenotyping experiments because they are inexpensive and can be used to monitor plant growth at the scale of days and weeks or to develop spectral indices referring to the greenness or canopy cover (Citation: Borra-Serrano, De Swaef& al., 2020) Borra-Serrano, I., De Swaef, T., Quataert, P., Aper, J., Saleem, A., Saeys, W., Somers, B., Roldán-Ruiz, I. & Lootens, P. (2020). Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials. Remote Sensing, 12(10). 1644. https://doi.org/10.3390/rs12101644 . However, these sensors are not useful for providing information on dynamic responses of photosynthesis over time scales of seconds or minutes. Therefore, increased exploitative work will be needed to capture plant dynamics in a non-invasive cost-effective way. Only in this way can the full extent of phenotypic variation be captured both within a single plant and across plants.

Trait Measurement Technologies #

Based on the sensor technology, three kinds of attributes can be attributed to a sensor: (i) contact or non-contact based, (ii) active or passive and (iii) direct or indirect measurement.

Contact sensors are directly mounted on the plant, while non-contact sensors measure through the air or water at some distance. A leaf thickness sensor is a contact sensor, while an RGB camera is a non-contact sensor. The advantage of a non-contact sensor is that is has little to no influence on responses. The disadvantage is that due to the indirect nature of the measurement, environmental noise is more easily picked up. Contact measurements do not suffer from these issues, yet their interaction can cause unwanted side effects.

Active sensors transmit a signal that interacts with the tissue and measure the response or alteration of the reflected or transmitted signal. Passive sensors do not transmit a signal and directly measure the response of the plant to environmental conditions. A distance sensor or fluorescence meter are examples of active sensors, while all cameras are passive devices.

Direct measurements can measure the trait directly. For instance, a leaf temperature sensor connected to the leaf measures the temperature without intermediate. In comparison, many other traits are measured indirectly, such as stomatal conductance by an IRGA. Based on the leaf temperature, water vapour and CO2 concentrations, the stomatal conductance is computed. Each sensor type has its own merits and pitfalls. Thus there is a sensing trade-off that one has to be aware of.

Image sensors are gaining importance due to the increasing focus on high-throughput phenotyping, but this also poses additional challenges, especially in experiments where plant responses are not extreme. Indeed, most image sensors listed in Table 3.1 are passive sensors, meaning that they acquire indirect signals from what they observe. These signals arise from the crop under investigation but can also arise from the surroundings. For increasingly subtle responses that have to be captured, this can pose new challenges as one has to ensure that the responses of the crop and its surroundings are separated.

Active imaging systems such as some depth cameras and LiDAR do not suffer from this issue but instead transmit a signal that interacts with the crop. Thus, the effects of the surroundings are often negligible. However, this also creates a new issue: the transmitted signal can affect plant properties. For instance, if modulated light is needed to obtain a measurement, this can affect the physiological response, like e.g., in chlorophyll fluorescence.

Employed Sensor Technologies #

In the experiments discussed later, there is a strong focus on dynamic aspects of plants. As a result, there is a need to measure the sensor readout with a sufficiently high temporal resolution and follow multiple traits in parallel. We are mainly focussing on the first and third classes ((non-) contact and (in)direct measurements, respectively). More specifically, we are employing a snapshot hyperspectral camera. Hyperspectral imaging sensors capture reflectance in many wavelengths and are increasingly applied in phenotyping research. This imaging technique has already been applied to various settings that benefit from higher spectral resolutions to detect biotic and abiotic influences on plants (Citation: Khan, Khan& al., 2018) Khan, M., Khan, H., Yousaf, A., Khurshid, K. & Abbas, A. (2018). Modern Trends in Hyperspectral Image Analysis: A Review. IEEE Access, 6. 14118–14129. https://doi.org/10.1109/ACCESS.2018.2812999 . Examples of studies on biotic factors include blight caused by Alternaria solani in potato (Citation: Van De Vijver, Mertens& al., 2020) Van De Vijver, R., Mertens, K., Heungens, K., Somers, B., Nuyttens, D., Borra-Serrano, I., Lootens, P., Roldán-Ruiz, I., Vangeyte, J. & Saeys, W. (2020). In-field detection of Alternaria solani in potato crops using hyperspectral imaging. Computers and Electronics in Agriculture, 168. 105106. https://doi.org/10.1016/j.compag.2019.105106 , late blight caused by _Phytophthora infestans_in potato (Citation: Franceschini, Bartholomeus& al., 2019) Franceschini, M., Bartholomeus, H., Apeldoorn, D., Suomalainen, J. & Kooistra, L. (2019). Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato. Remote Sensing, 11(3). 224. https://doi.org/10.3390/rs11030224 , or tracking the development of three foliar diseases in barley (Citation: Wahabzada, Mahlein& al., 2016) Wahabzada, M., Mahlein, A., Bauckhage, C., Steiner, U., Oerke, E. & Kersting, K. (2016). Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants. Scientific Reports, 6(1). 1–11. https://doi.org/10.1038/srep22482 . Citation: Mahlein (2015) Mahlein, A. (2015). Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. Plant Disease, 100(2). 241–251. https://doi.org/10.1094/PDIS-03-15-0340-FE and Citation: Lowe, Harrison& al. (2017) Lowe, A., Harrison, N. & French, A. (2017). Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods, 13(1). 80. https://doi.org/10.1186/s13007-017-0233-z provide comprehensive overviews of plant disease detection using imaging sensors and hyperspectral sensors specifically. Studies in which hyperspectral imaging was used to investigate plant responses in interaction with abiotic factors include, for example, detection of green citrus fruits on trees (Citation: Okamoto & Lee, 2009) Okamoto, H. & Lee, W. (2009). Green citrus detection using hyperspectral imaging. Computers and Electronics in Agriculture, 66(2). 201–208. https://doi.org/10.1016/j.compag.2009.02.004 , nitrogen deficiency in sorghum (Citation: Zhao, Reddy& al., 2005) Zhao, D., Reddy, K., Kakani, V. & Reddy, V. (2005). Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum. European Journal of Agronomy, 22(4). 391–403. https://doi.org/10.1016/j.eja.2004.06.005 , seasonal structural changes and a heterogeneous architecture in an olive orchard (Citation: Zarco-Tejada, Morales& al., 2013) Zarco-Tejada, P., Morales, A., Testi, L. & Villalobos, F. (2013). Spatio-temporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance. Remote Sensing of Environment, 133. 102–115. https://doi.org/10.1016/j.rse.2013.02.003 , nitrogen and water distribution quantification in wheat (Citation: Bruning, Liu& al., 2019) Bruning, B., Liu, H., Brien, C., Berger, B., Lewis, M. & Garnett, T. (2019). The Development of Hyperspectral Distribution Maps to Predict the Content and Distribution of Nitrogen and Water in Wheat (Triticum aestivum). Frontiers in Plant Science, 10. https://doi.org/10.3389/fpls.2019.01380 , and drought stress in barley and saxaul (Citation: Behmann, Steinrücken& al., 2014 Behmann, J., Steinrücken, J. & Plümer, L. (2014). Detection of early plant stress responses in hyperspectral images. ISPRS Journal of Photogrammetry and Remote Sensing, 93. 98–111. https://doi.org/10.1016/j.isprsjprs.2014.03.016 ; Citation: Jin & Wang, 2016) Jin, J. & Wang, Q. (2016). Hyperspectral indices based on first derivative spectra closely trace canopy transpiration in a desert plant. Ecological Informatics, 35. 1–8. https://doi.org/10.1016/j.ecoinf.2016.06.004 . As a result, it is an interesting sensing technology that can capture a wide range of plant states. Moreover, a sensor was available at ILVO at the start of our work on physical reservoir computing (PRC) with plants. Thus, experimentation could start early on. More details on sensor performance and experimental results are documented in Reservoir Computing with a Snapshot Hyperspectral Camera.

In the third class of direct and indirect sensors, we mainly employ leaf thickness, leaf length and gas exchanges sensors. Leaf thickness is a very interesting variable to measure since it is strongly correlated to the water status of a plant (Citation: De Swaef, Vermeulen& al., 2015 De Swaef, T., Vermeulen, K., Vergote, N., Van Lommel, J., Van Labeke, M., Bleyaert, P. & Steppe, K. (2015). Plant sensors help to understand tipburn in lettuce. International Society for Horticultural Science (ISHS). https://doi.org/http://dx.doi.org/10.17660/ActaHortic.2015.1099.3 ; Citation: McBurney, 1992 McBurney, T. (1992). The Relationship Between Leaf Thickness and Plant Water Potential. Journal of Experimental Botany, 43(3). 327–335. https://doi.org/10.1093/jxb/43.3.327 ; Citation: Afzal, Duiker& al., 2017) Afzal, A., Duiker, S. & Watson, J. (2017). Leaf thickness to predict plant water status. Biosystems Engineering, 156. 148–156. https://doi.org/10.1016/j.biosystemseng.2017.01.011 . The water status of plants can change rapidly due to internal and external changes (variability of sunlight, temperature and water availability). Measuring leaf thickness is also practical since it is a direct measurement, and clips are inexpensive compared to other methods.

Like thickness sensors, leaf length sensors provide information on dynamics in plant water status but additionally capture the irreversible growth of leaves. Leaf length measurements are also easy to set up, though less so than leaf thickness measurements. Leaf length measurements used in this dissertation are the E100 sensors (Chauvin Arnoux, France) and are constructed around a Linear Variable Displacement Transducer (LVDT). As a result, the measurement is more involved since a sine wave has to be applied at the input terminals, and the amplitude and phase shift characterise the displacement of the sensor. Measurements are always relative to the initial distance, which has to be determined using an alternative method.

A final type of contact sensor employed here is a gas exchange sensor (IRGA). The specific device used here is the LI6400XT (LI-COR Biosciences, Lincoln, NE, USA). This device measures various plant-traits related to photosynthesis based on measurements of CO2 and water vapour.

An overview of all sensors used and their characteristics are provided in Table 3.2.

Table 3.2: Overview of sensors used in the experiments and their characteristics. The typical sensor size is also included.

sensor	contact	active	direct	image
leaf thickness (small, 1-2cm)	yes	no	yes
leaf length (large, 30-50cm)	yes	no	yes
hyperspectral camera (medium, 15cm)	no	no	yes
LI6400XT (large, 2x 30cm)	yes	yes	yes

Summary #

In this chapter, we introduced the basic concepts of plant ecophysiology and how important aspects of photosynthesis and transpiration can be monitored. Moreover, we also highlighted a need in research targets from phenotyping to phonemics. With the impacts of climate change, phenotyping will have to become more holistic, leading to phenomics. Important sensor technologies and their merits were also featured in this chapter. Finally, the most important sensor devices of the subsequent studies were introduced.