Chlorophyll a Fluorescence: A Method of Biotic Stress Detection

Abstract

Plant diseases are a major threat to food security, causing drastic alterations in plant metabolism upon infection by pathogens. This often results in decreased biomass accumulation, slowed growth rates, and diminished yield components. Pathogens, through various lifestyles such as biotrophic, necrotrophic, and hemibiotrophic, disrupt photosynthesis, the primary metabolic process, via functional and structural damages. Furthermore, the CO₂ assimilation in plants is severely altered by pathogens regardless of their lifestyles. Photosynthetic determinations allow us to establish a perspective about the physiological impairment caused by pathogens related to alterations in the CO₂ flow from the atmosphere to carboxylation sites, stomatal limitations, and photosynthetic performance of photosystem II (PSII). From the changes in the energy, dissipation is possible to establish the functional status of the photochemistry machinery under stress conditions. For the above, chlorophyll a fluorescence (CF) and CF imaging (CFI) arose as a method highly sensible to determine the damage caused by pathogens in plants. This review shows a practical perspective on CF tools using visual method and rapid fluorescence induction kinetics (OJIP-test), for disease detection associated with plant-pathogen interaction studies from the physiological viewpoint, their implications for plant pathology research, applications for the plant phenotyping field, and biotic stress detection.

1. Introduction

The photosynthetic process is one of the most important metabolic pathways through which plants transform the sunlight into chemical energy obtaining carbohydrates to be used in other metabolic events [1]. In plants, the sunlight energy may be dissipated by photochemistry (photosynthesis light reactions), chlorophyll fluorescence (photon re-emission), and heat (Figure 1) [2]. Metabolic process of photosynthesis has been studied in two stages: (i) light reactions: these processes allow the solar energy contained in photons of sunlight to be processed in the membranes of chloroplasts to produce adenosine triphosphate (ATP) and nicotinamide adenine dinucleotide phosphate (NADPH) and (ii) the Calvin-Benson cycle, which produces triose-P and, as a result, the recovery of ribulose 1,5-bisphosphate (RuBP), in which how CO₂ is reduced [3, 4]. Chlorophyll fluorescence occurs when the electron transfer from a chlorophyll molecule at the reaction center of the photosystem II (PSII) to a plastoquinone molecule within the plastoquinone transporter, results in fluorescence emission [2]. Finally, the energy cannot be dissipated by photochemistry or fluorescence, and it is dissipated by heat emission [5, 6]. Under this perspective, plants utilize the sun’s energy mainly to supply the demands of the primary metabolism; however, external (stresses) and intrinsic (diffusional and biochemical limitations) factors related to the photosynthetic process induce alternative pathways such as fluorescence and heat emission to help in the energy dissipation process to reduce functional and structural damages in the plant cell derivatives of energy accumulation [2].

Figure 1.

Energy dissipation pathways (photochemistry (A), chlorophyll fluorescence (CF), and heat (H)) in leaves under non-stress and stress conditions and its effect on plant growth. Photochemical dissipation: P_C; non-photochemical dissipation: NP_C. Design figure credit by Carlos Eduardo Aucique-Perez.

To study the photosynthesis, several methodologies have been developed involving determinations based on changes in CO₂ and water vapor differential concentrations using infrared sensors (infrared gas analyzer (IRGA)) through which it is possible calculate the leaf gas exchange parameters such as net CO₂ assimilation rate (A), stomatal closed (g_s), CO₂ intercellular concentration (C_i), and transpiration rate (E) [7]. Other methodologies developed in the biochemistry field are commonly applied in photosynthesis studies such as the quantification of the carbohydrates or enzymatic activities of the biochemical components associated with this pathway [8, 9]. Under plant stress, the photosynthetic activity is a main physiological pathway evaluated, as the functionality of the photosynthetic machinery is affected by stress factors [10].

On the other hand, the chlorophyll a fluorescence (CF) has been a non-invasive and non-destructive method successful to study photosynthetic responses from plants under different conditions [11]. Currently, the CF method is applied in diverse fields, especially for rapid diagnosis of abiotic or stress biotic [12, 13] based on changes in the energy dissipation magnitude in the photochemical reactions [10]. Basically, the CF method has been developed from “Kautsky’s effect,” which was studied when the photosynthetic sample was transferred from the dark to the light, the CF yield increased over a time span of about 1 second. This effect led to increases in the CF yield with a consequent reduction of electron acceptors in the photosynthetic pathway inducing quinone A (Q_A) reduction due to one electron reception which must be transferred to quinone B (Q_B), ensuring the linear electron flow [5]. Therefore, variations in CF characteristics can be indicative of various stresses, including nutrient deficiencies, water deficit, pathogen infections, and high light intensity [2]. By monitoring the CF, scientists, agronomists, and other professionals can gain insights into plant performance and make informed decisions regarding crop management and stress mitigation strategies. In this sense, the CF can be used as a diagnostic tool for assessing plant health and stress responses.

For the plant pathology field, the CF tool has been used for the detection of changes in the energy dissipation pattern in several pathosystems related to fungi, viruses, bacteria, and oomycetes, for example, Bipolaris sorokiniana [14], Fusarium spp. [11, 15], Oculimacula acuformis [11], Puccinia triticina [16], Puccinia coronate [17], Bremia lactucae [18], Pseudomonas syringae [19], Grapevine leafroll-associated virus 3 [20, 21], and so on. Advances in the CF instrumentation, including portable devices and remote sensing techniques, have expanded the accessibility and applicability of this technology. In recent years, an increase of portable user-friendly and non-invasive chlorophyll fluorometers had been used for research in the plant pathology field. However, despite the simplicity of the utilization of equipment, the theory and interpretation of data arising from analysis continues to be complex [22].

The use of these devices has allowed crop monitoring on the field and under controlled conditions, plant health management, and ecosystem dynamics [23]. Additionally, the CF imaging technique enable the spatial and temporal visualization of photosynthetic activity, allowing for the assessment of plant performance and the detection of heterogeneity in responses [6]. The integration of the CF with other “omics” technologies further enhances our understanding of photosynthetic regulation and stress responses at the molecular, metabolic, genomic, and phenomic level [24]. Overall, the CF technique plays a crucial role in advancing our knowledge of plant function and resilience. In this sense, the aim of the review was describing the use of the CF as a technique of determination of biotic stress in the plant pathology field.

2. Chlorophyll a fluorescence: method and application

The CF has been used as a technique in photosynthesis research, providing valuable information on the structure and function of the photosynthetic apparatus [10, 12]. In this sense, the photosynthetic response can be estimated by fluorescence changes which study through two types of CF measurement such as continuous excitation and pulsed excitation [5].

Firstly, a method uses pulse amplitude modulation (PAM). The PAM method measures CF induced by high-frequency, modulation pulses (MP) at different stages of the system dynamics driven by saturation pulses (SP) or actinic light (AL) [25, 26]. Thus, the relative yield of fluorescence can be measured in the presence of illumination or can be conducted at field condition [5]. Fundamentally, the CF method determines values associated with basal (F₀ and F₀′) and maximal (F_m and F_m′) fluorescence on dark and light-adapted leaves from which it has been possible to derive parameters to estimate specific responses of the energy dissipation through the photochemical pathway [5]. Experimentally, F₀, F₀′, F_m, and F_m′ are obtained from a sequence of light supplying to different intensities which simulated the change from darkness to light adaptation through photosynthetic excitation (Figure 2). For obtaining F₀, the dark-adapted leaf is illuminated with weak, modulated measuring pulse (L₁ = 0.03 μmol m⁻² s⁻¹). Immediately, a light saturate pulse (SP = 8000 μmol m⁻² s⁻¹) is applied for 0.8 s to ensure maximal fluorescence emissions (F_m). Subsequently, on light-adapted leaves, the steady-state fluorescence yield (F_t) is measured following a saturating light pulse (SP = 8000 μmol m⁻² s⁻¹, 0.8 s) that was applied to achieve the light-adapted maximal fluorescence (F_m′). The actinic light (AL) is then turned off and far-red illumination was applied (2 μmol m⁻² s⁻¹) to measure the light-adapted basal fluorescence (F₀′) [7].

Figure 2.

Fluorescence determination routine. Lowing intensity light (L₁); light saturated pulse (SP); actinic light on (AL_on) and off (AL_off); basal (F₀) and maximal (F_m) fluorescence in dark-adapted leaf; basal (F₀′) and maximal (F_m′) fluorescence in light-adapted leaf; fluorescence level immediately before the SP (F_t). Figure adapted from Baker [2].

The second method is based on the increase in the CF yield observed when leaves pass through dark to light in a short time period. This induction and fast dynamic changes of CF occur when dark-adapted samples are exposed to light known as Kautsky’s effect, as explained above [27], and it is observed as a polyphasic curve, giving information about the efficiency of electron transport through PSII [28, 29]. The CF variations on the OJIP induction curve are also well-known as the OJIP-test. From the O-step to the J-step, it reflects high-order nonlinear dynamics of PSII resulting from light excitation, energy transfer, energy dissipation, and photochemical reactions [25], see Table 1 for more details. The technique is based on the theory that energy flux through PSII reaction centers can be quantified to provide information about structure and function of the photosynthetic apparatus [33].

Technical fluorescence parameters
Area	Area between fluorescence curve and FM
M0	Slope of the curve at the origin of the fluorescence rise. It is a measure of the rate of the primary photochemistry.
Vj	Relative variable fluorescence at 2 ms
Vi	Relative variable fluorescence at 30 ms
SM = Area/FV	Normalized total complementary area above the OJIP transient (reflecting multiple-turnover QA reduction events). It expresses the total electron carriers (EC) per RC.
N	The number of rotations: number of QA reduction events between time 0 and t FM.
Energy fluxes
ABS	The photon flux absorbed by the antenna of PSII units
TR	The portion of ABS trapped by the active PSII units that leads to QA reduction
DI	The portion of ABS dissipated in PSII antenna in process other than trapping
ET	The energy flux associated with the electron transport from QA^− to the intersystem electron acceptors
RE	The energy flux associated with the electron transport from QA^− to the final electron acceptors of PSI
Quantum yields or flux ratios
ϕPo = TR0/ABS	Maximum quantum yield of primary photochemistry (at t = 0); ϕPo = (FM – F0)/FM
ψEo = ET0/TR0	Probability (at t = 0) that a photon trapped by the PSII reaction center enters the electron transport chain; ψEo = 1 – Vj
ϕEo = ET0/ABS	Quantum yield of electron transport (at t = 0); ϕEo = [(1 – F0/FM) × (1 – Vj)]
ϕDo = DI0/ABS	Quantum yield (at t = 0) of energy dissipation.; ϕDo = (F0/FM)
ϕRo = RE0/ABS	Quantum yield of an electron transport from QA^− to the final electron acceptors of PSI.; ϕRo = [(1 – F0/FM) × (1 – Vi)]
δ Ro = RE0/ET0	Efficiency with which an electron from PQH2 is transferred to final PSI acceptors; δ Ro = (1 – Vi)/(1 – Vj) = (FM – F)/(FM– Fj)
Specific fluxes or specific activities (expressed in arbitrary units)
ABS/RC	Effective antenna size of an active reaction center (RC). Expresses the total number of photons absorbed by chlorophyll molecules of all RCs divided by the total number of active RCs; ABS/RC = (M0/Vj)/ϕPo
TR0/RC	Maximal trapping rate of PSII. Describes the maximal rate by which an excitation is trapped by the RC; TRo/RC = M0/Vj
ET0/RC	Electron transport in an active RC; ETo/RC = (M0/Vj)/ψEo
DI0/RC	Dissipated energy flux per RC; DI0/RC = ABS/RC – TR0/RC
Phenomenological fluxes or phenomenological activities (expressed in arbitrary units)
ABS/CS0	Absorption per cross-section (CS); ABS/CS0 ≈ F0 and ABS/CSM ≈ FM
TR0/CS	Trapping per cross-section; TR0/CS = (TR0/ABS) × (ABS/CS)
ET0/CS	Electron transport per cross-section; ET0/CS = (ET0/ABS) × (ABS/CS)
RE0/CS	Electron flux from QA^− to final PSI acceptors per cross section of PSI; RE0/CS = (RE0/ABS) × (ABS/CS)
Performance indexes
PIABS	Performance index on adsorption basis; PIABS = (RC/ABS) × [ϕPo/(1 – ϕPo)] × [ψEo/(1 – ψEo)]
PIABS, total	Total performance index on absorption basis; PIABS, total = PIABS × [δR0/(1 – δR0)]
PICS	Performance index on cross section basis; PICS = PIABS × (ABS/CS)
PICS, total	Total performance index on cross section basis; PICS, total = PIABS, total × (ABS/CS)

Table 1.

Parameters and definition related to energy fluxes obtained from OJIP-test.

Adapted from reference [30, 31, 32].

Several CF parameters are commonly used to evaluate the performance of the photosynthetic apparatus, such as the maximum quantum yield of photosystem II (PSII), often represented as the F_v/F_m, provides information about the maximum efficiency of PSII photochemistry [5]. The effective quantum yield of PSII (Φ_PSII) reflects the actual efficiency of light energy conversion in PSII [2] and non-photochemical quenching (NPQ) that represents the dissipation of excess light energy as heat, thereby protecting the photosynthetic machinery from photodamage [34]. The increases in non-photochemical quenching (measured as NPQ and/or q_N), photoinhibition detected by decreases in F_v/F_m, and inhibition of the electron transport chain can be observed in cases of a stress condition is prolonged [10]. For a higher understanding of the fluorescence parameters, Table 2 describes the main parameters, their description, calculation, and physiological interpretation.

Parameter	Description	Formula/interpretation
F0; F0′	Basal or initial fluorescence from dark and light-adapted leaves, respectively	Level of florescence when all PSII center open (oxidated status)
Fm; Fm′	Maximal fluorescence from dark and light-adapted leaves, respectively	Level of florescence when all PSII center closed (reduced status)
Fv; Fv′	Variable fluorescence from dark and light-adapted leaves, respectively	Fm – F0; It is value associated with the PSII capacity to perform photochemistry
Fv/Fm	Maximum quantum yield of PSII	(Fm – F0)/Fm; Maximum efficiency at which light absorbed by PSII is utilized to reduce QA
Fv′/Fm′	Capturing efficiency of the excitation energy by the open PSII reaction centers	(Fm′ – F0′)/Fm′
qP	Coefficient for photochemical quenching	(Fm′– Ft)/(Fm′ – F0′); Actual photochemical capacity
qN	Coefficient for photochemical quenching	1 – [(Fm′ – F0′)/Fm – F0]; Non-photochemical process activation during the light period
ΦPSII	Actual quantum yield of PSII electron transport	(Fv′/Ft)/Fm′
NPQ	Non-photochemical quenching	(Fm – Fm′) – 1; Apparent heat loss rate from PSII
ETR = J	Rate of electron transport	PPFD × ФPSII × ƒ × α

Table 2.

Chlorophyll fluorescence parameters used for studies of photosystem II (PSII) photochemistry.

PPFD is the photosynthetic photon flux density. ƒ is a factor that accounts for the partitioning of energy between PSII and PSI and is assumed to be 0.5, which indicates that the excitation energy is distributed equally between the two photosystems; and α is the leaf absorbance by the photosynthetic tissues and is assumed to be 0.84.

Adapted from reference [2, 5, 10, 29, 30, 31].

The CF parameters obtained from measurements can provide valuable information about the plant’s photosynthetic efficiency, tolerance to stress (abiotic and biotic), or normal physiological condition [6, 35]. Therefore, measuring changes in fluorescence parameters such as F_v/F_m, Φ_PSII, and NPQ provides insights into the functionality and regulation of PSII and can indicate early stress detection [34]. In this context, the NPQ represents the protective mechanism employed by plants to dissipate excess absorbed light energy as heat, reducing the potential for photodamage [22]. In addition, the NPQ is influenced by factors such as light intensity, temperature, and the availability of energy sinks within the plant [36]. In practice, the NPQ determination requires leaves’ full adaptation to darkness in such a way it reflects the energy dissipation capacity in excess, especially under stress conditions [6]. Recently Tietz et al. [37] proposed the characterization a new set of parameters, NPQ _(T), that can be used without dark adaptation and can be rapidly applied during steady-state illumination, potentially even under field conditions.

On the other hand, in the last years, with the developments in optical sensors, the CF imaging (CFI) technique emerged as a powerful tool to study plants under different growth conditions, especially stress factors. In this case, the fluorescence spectral considers the wavelength from UV to infrared, allowing the examination of the spectral region’s keys for the plant’s physiology responses. In addition, the physical characteristic is related to the emission of fluorescence when the energy is absorbed in the UV region, invisible to the human eye, and posterity is emitted in the visible region and can facilitate obtaining images [38]. Basically, a CFI system utilizes a camera with a long-pass filter, while hindering shorter wavelengths. From the images captured, they are segmented and CF parameters are calculated for each pixel, finishing with the visualization of CF images [39]. With this technique, the obtaining of images from each parameter associated with the CF such as F_v/F_m, Φ_PSII, NPQ , and ETR allows the visual exploration of changes in these parameters per area unit [40] and spatial heterogeneity distribution of CF [41]. It has been such a sensibility in this technique that the CFI sensors have been coupled to plant phenotyping systems, obtaining interesting results on indoor and outdoor platforms [10].

3. Photosynthetic alterations by pathogen infection

Under biotic stress, the CO₂ assimilation in plants is severely altered by pathogens infection regardless of their lifestyles (e.g., biotrophic, necrotrophic, and hemibiotrophic) [1, 42]. The biotrophic pathogens get to grow and feed on living plant tissue colonizing plant cells undetected through mechanisms that suppress the plant responses to infection [43]. The biotrophic strategy is considered obligatory parasitism, where the pathogen establishes a molecular and physiological manipulation of the plant cell, and this way it obtains the energy for the growth, such as the causal pathogens associated with rust (e.g., Hemileia vastatrix) (Figure 3A) and powdery mildew (e.g., Erysiphales sp.) diseases [44, 45]. In contrast, the necrotrophic fungi from the first infected plant cell induces rapid cell death using enzymes and toxins to degrade plant tissues, and in this way, the necrotrophic pathogen colonizes the rest of the plant structures [43]. Botrytis cinerea, Alternaria brassicicola, and Neopestalotiopsis sp. (Figure 3B) are species commonly related to the necrotrophic strategy as they have a molecule pool with a high destructive power of the plant’s tissues from the first time of the interaction with the host plant [46, 47, 48]. Finally, hemibiotrophic fungi have the capacity to combine both biotrophic and necrotrophic strategies [49]. For this strategy, a biotrophic phase allows the colonization of the first plant cell to establish the invading hypha that later deploys other invasive structures in the necrotrophic phase to cause plant cell destruction [50]. An excellent example of the hemibiotrophic strategy is Pyricularia oryzae fungi (Figure 3C) [51].

Figure 3.

Leaf symptoms caused by biotrophic (A; Hemileia vastatrix), necrotrophic (B; Neopestalotiopsis sp.), and hemibiotrophic (C; Pyricularia oryzae) fungi in coffee, wheat, and eucalyptus plants, respectively. Picture credits by Carlos Eduardo Aucique-Perez.

During the plant-pathogen interactions, plants infected by pathogens have reported reductions in CO₂ assimilation, stomatal dynamic alterations (stomatal closure), chronic inhibition of the photosystems, higher dissipation of energy by chlorophyll a fluorescence (non-photochemical mechanisms), alterations in the concentration of photosynthetic pigments, chloroplasts structural damage, losses in leaf area index, changes in the source-sink ratio, and changes in the profiles of genes, proteins, and metabolic related to photosynthesis [1, 8, 9, 52]. The pathogen infection in the foliar tissue causes loss in the sunlight conduction capacity due to the cellular damage affecting the photosynthetic activity and consequent development of chlorotic and necrotic zones product of the action of hydrolytic enzymes, non-host selective toxins, and reactive oxygen species (ROS) [1, 53]. While, the plant cell after detection of the pathogen, genes related to photosynthesis are transcript down-regulated to protect the photosynthetic apparatus against the oxidative damage [1, 42].

CO₂ assimilation by the photosynthetic process is a permanent source of primary metabolism, and their products are the main energetic nutrient for pathogens colonizing plant tissues, as well as molecules related to metabolic pathways from secondary metabolism [1, 54]. Studies performed in the last 20 years have allowed understanding how pathogens reprogram the metabolic flux of the carbohydrate’s metabolism in detriment of the plant growth and yield demands [1, 8, 42, 54]. From the metabolic perspective, several pathogens rapidly metabolize sucrose through invertase enzymes which hydrolyze sucrose to fructose and glucose increasing the hexoses pool [8, 14, 55]. Metabolites such as fructose, glucose, and hexoses are energetic substrates for other metabolic pathways more complex that under biotic stress those molecules become a nutrient source for pathogens; however, alterations in the sugar concentrations induce the expression of defense genes and cell death considering that sugars act as signaling molecules to regulate the expression of some genes [56]. In the case of foliar pathogens, increases on the invertases activities cause changes in the source-sink relationship dynamic [14, 23, 57].

On the other hand, studies have reported that the damage caused by pathogens on leaves led to significant reductions in the net CO₂ assimilation rate (A), stomatal closed (g_s), and transpiration rate (E), as well as alteration in plant water balance and foliar temperature [7, 8, 52, 58, 59, 60]. In the case of biotrophic fungi as Hemileia vastatrix, Austropuccinia psidii, and Phakopsora pachyrhizi infecting coffee, eucalyptus, and soybean, respectively, after evidencing symptoms, experiments have demonstrated reductions between 20 and 50% in A, while g_s and E showed higher reductions than A in leaves with severity oscillated between 30 and 50% [45, 61, 62].

Studies focused on energy dissipation through CF have allowed an understanding of the physiological events that happened during the asymptomatic and symptomatic disease phases. It is well-known that the F_v/F_m index is the most extensively utilized chlorophyll a fluorescence parameter stress level determination of abiotic and biotic factors [2]. Under non-stressed conditions, healthy leaves show F_v/F_m values around 0.80–0.83, while inferior F_v/F_m values indicate that a proportion of photosystem II (PSII) reaction centers have been harmed, which induces the photo-inhibitory effect on the photosynthetic machinery and is often led to oxidative stress on plants [63, 64]. In practice, the F_v/F_m parameter is a powerful tool for judging plant photochemical capacity because it acts as a stress indicator for plants; in other words, F_v/F_m measures the operational capacity of the leaf to perform photosynthesis [2, 5, 63, 64].

In the plant pathology field, the use of F_v/F_m has been efficient to measure the pathogen infection damage on the photosynthetic response depending on pathogen lifestyles [10]. However, CF parameters related to photochemical and non-photochemical energy dissipation also have showed interesting findings in plants infected. For instance, in coffee plants with rust symptoms, the values of the yield for dissipation by down-regulation [Y(NPQ) = (F_s/F_m′) – (F_s/F_m)] and the yield for other non-photochemical (non-regulated) losses [Y(NO) = F_s/F_m] were lower and higher, respectively, for the inoculated plants compared to non-inoculated plants, suggesting that H. vastatrix after the plant cell colonization reduces the protection capacity of the photosynthetic machinery through antioxidative pathways and inducing a high energy dissipation by heat [65]. In the case of plant virus, high NPQ values showed to be more efficient for the detection of Potato virus Y in tobacco plants overproducing endogenous cytokinins [66]. Based on studies performed until now, we can confirm that the CF is a methodology efficient for plant-pathogen interaction studies and with high potential of adaptation in the plant phenotyping approaches.

In the plant resistance field, the photosynthetic damage leads to intrinsic reductions in the defense capacity against the pathogen, and it can reach a higher intensification when abiotic stress factors such as drought, high temperature, or nutritional deficiency/excess participate along the biotic interaction [8, 67, 68]. As known, studies demonstrated that pathogens secret their effectors for linking to receptor specifics in chloroplasts looking for a reduction drastic in the ROS controlling the plant defense mechanisms, suggesting the crucial role of the photosynthetic machinery in the basal plant resistance against pathogen infection [69]. In this sense, the manipulation of photosynthetic activity using photosynthesis inhibitor-specific (DCMU) in wheat cultivars with differential basal resistance to Pyricularia oryzae showed increases in the blast severity in both cultivars as a consequence of a low photosynthetic activity affecting the enzymatic responses related to plant resistance such as phenylalanine ammonia-lyase (PAL) and reinforced the concept that photosynthesis was important for wheat resistance to blast [70].

In short, photosynthesis is affected by pathogen infections, and their effects led to reductions in CO₂ assimilation, losses in growth, and crop yield, suggesting that just like abiotic stress, biotic factors threaten food production, as has been reported from the famine in Ireland at XVIII caused by the potato blight epidemic. For the above reasons, deep knowledge about the pathogen strategies to cause disease in plants and how they affect plant metabolism is crucial to designing management alternatives in all fields of agronomy.

4. CF tool to study the plant-pathogen interaction

The use of CF tools in recent years has revealed the utility of this tool from a plant pathology perspective, and CF imaging (CFI) techniques have been used in several plant pathology interactions. However, the equipment for CFI can be expensive, and it is necessary to handle due to the dark adaptation, increasing the time required with a large numbers of plants to test [6]. Today, portable instruments are commercially available being used at field conditions. For instance, CF using the OJIP-test, that is, an induction curve from the O-step to the J-step reflects high-order nonlinear dynamics of PSII resulting from light excitation, energy transfer, energy dissipation, and photochemical reactions (Table 1) [33]. As a result, OJIP-test is fast, non-invasive, and sensitive technic [6, 11], becoming an alternative for physiological and plant pathogen studies. Also, this technique allows screening and phenotyping large number of plants (e.g., approximately 500 plants per day) under field conditions, as was reported by Ajigboye et al. [71].

Both techniques offer important information on the PSII damage caused by the infection of pathogens in plants; however, CFI has been more frequently used to detect CF changes in response to foliar diseases in different genotypes due to their visual advantages [8, 40, 72]. In this context, CFI has been used to screen resistant wheat genotypes as well as the early detection of leaf rust caused by Puccinia triticina [73]. Kuckenberg et al. [74] observed alteration in F₀ and F_m parameters in asymptomatic wheat leaves affected by powdery mildew and rust. Ralstonia solanacearum is a devastating disease for tomato seedling; in a study using CFI, F_v/F_m values between 0.55 and 0.65 were detected, and as a result, the infection was predicted 2 days before visual symptoms [75]. Likewise, CFI was used for the detection of potato virus X (PVX) in Nicotiana benthamiana plants; the study demonstrated that Φ_PSII and NPQ parameters in infected areas were different significantly starting from the second day after the detection of the virus, considering the CFI as reliable pre-symptomatic detection a viral infection [76]. Also, the CFI was able to detect biotic and abiotic stress in tomato plants, as was reported by Moustaka et al. [77]. In this study, it was able to detect early biotic stress of Spodoptera exigua after 15 min of feeding and 30 min after Botrytis cinerea inoculation on the onset of water-deficit stress determining as a tool for early stress detection.

On the other hand, F_v/F_m and Y(II) parameters were sensible on coffee leaves inoculated with H. vastatrix and fungicide-sprayed ones (Figure 4). Those experimental results showed a significant reduction in F_v/F_m and Y(II) values, and they were identified in deep sky blue (F_v/F_m) and yellow-red areas [Y(II)] from 10 days after inoculation with H. vastatrix, suggesting that those CF parameters can be used to detect rust symptoms before reaching the incubation period. In coffee plants sprayed with fungicide, F_v/F_m and Y(II) images did not evidence changes in the color patterns and have been similar to control plants (0 days after inoculation), suggesting that the CFI method is an efficient technique for determining photosynthetic alterations caused by H. vastatrix as well as effects induced by the fungicide application on the pathogen and the plant, in parallel.

Figure 4.

The maximum quantum yield of PSII (F_v/F_m) and the effective PSII quantum yield [Y(II)] images in coffee plants infected with Hemileia vastatrix (Hv) sprayed with fungicide (Hv + Fg). The color bar represents the scale from near zero (dark orange) to near one (dark violet). Image supplied by Carlos Eduardo Aucique-Perez.

The combination of the CFI with other techniques allows an important way to study plant-pathogen interaction. In a study, combining CFI and leaf thermography from thermal infrared (TIR) imaging, among sweet potatoes infected by Sweet potato feathery mottle virus (SPFMV, genus Potyvirus) and Sweet potato chlorotic stunt virus (SPCSV, genus Crinivirus), demonstrated that the F_v/F_m and q_P were the most sensitive parameters for the quantification of virus effects [78]. In oil palms affected with bud rot and lethal wilt, using several CF parameters showed anomalies in the photosynthetic system due to reductions in F_v/F_m and Φ_PSII values, as well as increases in the leaf temperature in response to both diseases [79]. At field conditions, a study carried out by Mahlein et al. [80], using a combination of three types of sensors—infrared thermography, CFI, and hyperspectral imaging—symptoms caused by Fusarium graminearum and Fusarium culmorum were detected successfully. Additionally, the disruption in photosynthetic activity was confirmed through the measurement of F_m in spikelets at 5 dai.

The detection of some diseases in the most cases results in a difficult task due to the complexity between symptomatology observed. In this respect, the combination of CFI and ROS activities detects brown spot and anthracnose in cucumber plants. The difference of the activity levels of the ROS-scavenging enzymes and CFI parameters discriminated cucumber diseases [81]. Likewise, an important disease in citrus such as Citrus Huanglongbing (HLB) had been rapidly detected with high accuracy with the combination of reflectance images and multicolor fluorescence images, which relate to photosynthesis and secondary metabolites [82]. The study demonstrated that the combination of the techniques can effectively detect HLB disease in different infected statuses and cultivars, making it a promising tool for high-throughput HLB disease detection in citrus orchards. Currently, the increases in the interest on artificial intelligent (IA) in the recent years demonstrated the versatility of the CFI for the development of possible tools that discriminate biotic stress. Recently, Sapoukhina et al. [83] proposed a new methodology to develop a data set to generate images from CFI of plants infected by Pseudomonas syringae pv. in tomatoes for an automated lesion annotation applied in deep learning, suggesting that IA technology can help to improve the detection levels of diseases and their quantification.

On the other hand, the CF by OJIP-test has been used in several studies to determinate the effects over PSII performance. In this case, Ajigboye et al. [11] using CF determines that existing changes related to the maximum efficiency of PSII photochemistry (F_v′/F_m′), as well as flux of dissipated (DIo/RC), trapped (TRo/RC), or absorbed (ABS/RC) energy per active reaction centers during the attack of necrotrophic pathogens such as Oculimacula spp., F. culmorum, or F. avenaceum. The use of CF measurements and confocal laser-scanning microscopy was used for the detection of Rosellinia necatrix that produce white root rot (WRR) disease [84]. This study reveals early stages of WRR disease affect leaf photochemistry before aboveground symptoms appear. Notably, there were significant reductions in photosystem-II trapping efficiency (F_v′/F_m′) and the minimal fluorescence yield (F_s/F₀).

In another study, the use of OJIP-test showed that DIo/RC parameter is the best indicator for the diagnosis of Bursaphelenchus xylophilus that is a pinewood nematode (PWN) of Pinus thunbergia [85], suggesting that this method may assist in disease detection localized in other plant organs, such as roots. In other interaction, it was demonstrated that F_v/F₀, F₀, F_m, and ABS/RC parameters were useful for identifying the physiological changes produced by F. verticillioides infection in maize plants [86]. Furthermore, the structural and functional features from OJIP-test was used for the development of a discriminant model for rapid HLB detection [87]. In this study, healthy, asymptomatic, and symptomatic HLB-infected leaves of two different citrus cultivars produced specific changes in the fluorescence patterns. Specifically, HLB-infected symptomatic leaves showed significant reduction in F_v/F₀, Φ_Eo, ψ_Eo, Φ_Ro, Φ_Po, PI_ABS, PI_total, Fm, RE₀/RC and ET₀/RC, and a significant increase in TR₀/RC, F₀, ABS/RC, and DI₀/RC, see Tables 1 and 2 for details. This research can potentially be used for high-throughput HLB detection when combined with advanced machine learning techniques, offering a promising approach to address the challenges posed by this devastating citrus disease. Overall, these studies have shown that the OJIP-test is a powerful tool to know with higher detail the functional damages caused by pathogens on the photosynthetic machinery.

The CF methodology has been successful in several studies to determine the effect of fungicides on PSII activity during disease control, as mentioned above (Figure 4). For instance, the use a fungicide such as isopyrazam and epoxiconazole increased PSII efficiency, ameliorate biomass and grain yield, and reduce disease pressure. Also, the two fungicides increased the efficiency of PSII photochemistry (F_v’/F_m′), as well as improved photosynthetic gas exchange and increased rates of electron transport [71]. In another study, the application of dimethyl sulfoxide (DMSO) in Solanum lycopersicum and Lactuca sativa plants for the control of Botrytis cinerea did not change the chlorophyll fluorescence (F_v/F_m and Φ_PSII) [88]. However, one research determined negative alterations on photosynthetic parameters in coffee plants sprayed with tebuconazole and trifloxystrobin, which caused reductions in F_v/F_m, Φ_PSII, and ETR, suggesting that there are fungicides that limit the photosynthetic capacity of plants despite disease management [45]. Therefore, the CF methodology contributed to the determination of the physiological effects of fungicides on plants.

Additionally, this tool can be used for phenotyping in breeding programs. In this context, a study conducted by Suarez et al. [89] using CF determination showed that common bean lines induced by biotic stress can be classified as resistant or susceptible phenotypes according to the changes in F₀, F_m, and consequent alteration in F_v/F_m. In barley genotypes infected with Fusarium culmorum, alterations in the energy flux associated with the energy dissipation at the PSII (<DI₀/RC), limitations in the energy absorption by PSII reaction centers (<TR₀/RC), and low energy used for electron transport (<ET₀/RC) were determined factors to demonstrate that this response pattern can be used for the selection of barley doubled haploid lines resistance [90].Those studies indicated the adaptability of CF or CFI techniques associated with plant pathology for the identification of biotic stress, phenotyping, diseases management, and how those technics can be combined with others.

5. Conclusions

The CF is a versatile technique that offers valuable insights into the efficiency and performance of the photosynthetic apparatus. With advancements in methodology and instrumentation, the CF has become an indispensable tool in plant science research, specifically in the plant pathology field.

The measurement setup, parameters obtained, and data interpretation are crucial aspects of the Chl a fluorescence methodology. Researchers employ specialized fluorometers, measure fluorescence parameters such as F_v/F_m, Φ_PSII, and NPQ , and interpret the obtained data in conjunction with other physiological measurements for biotic stress detection. However, other parameters have shown high sensitivity depending on the pathosystem, so more studies are necessary for confirming responses and developing new research approaches to improve our knowledge about pathogen infection strategies. Plant physiologists and plant pathologists, as well as plant breeders, hope that, in the next few years, new advances in CF technology will allow a higher understanding of plant responses under pathogen infection, as well as become a powerful tool to support the disease management in issues related to fast detection, evaluation of efficiency in control strategies, and also improving the selection criteria for resistant phenotypes to disease within plant breeding programs.

Acknowledgments

The authors are thankful to Fondo de Investigación para la Agrobiodiversidad, Semillas y Agricultura sustentable (FIASA), grant number FIASA-CA-2023-010.

Conflict of interest

The authors declare no conflict of interest.