Plant pathogens are severe constraints to the production yield of crop fields worldwide. Current agricultural policies are aimed to minimize the use of pesticides and fertilizers through better targeting, and the integration with cultural control of weeds, pests, and diseases (Maloy, 2005). The implementation of precision agriculture relies on the development of technologies that allow the identification and mapping of constraints in the crop fields, such as imaging techniques (Mulla, 2013). They can be used to evaluate the effects of stress on plant metabolism (Cerovic et al., 1999; Barón et al., 2012, 2016). Consequently, imaging techniques are powerful non-destructive tools that have become essential: they provide crucial information for the decision-making and for the right timing of the procedures to be applied (Usha and Singh, 2013; Li et al., 2014; Mahlein, 2016).

Imaging techniques implemented on plant phenotyping provide complex and large scale spatial and temporal information, which is very difficult to analyze and interpret by conventional statistical methods. Another important contribution to precision agriculture is the development of mathematical tools that allow monitoring and classification of plants and fruits by the severity of the disease (Hahn, 2009), based on advanced statistical methods, as reviewed by Mulla (2013) and Behmann et al. (2015). Some of these mathematical tools could be used as classifiers, identifying stressed plants, or monitoring the evolution of pests. This strategy can also be applied in the same way on plant phenotyping programs (Fiorani and Schurr, 2013). The classifiers are mathematical models that are obtained by machine learning: systems that learn from data corresponding to different categories or subpopulations (Hahn, 2009; Behmann et al., 2015; Singh et al., 2016). Successful models are able to identify what category new data belong to, thus classifying them accurately. Machine learning includes a wide range of classifiers, such as ANN and LRA. ANN is a network inspired by biological neural networks that learn from input and output data (Hill et al., 1994). On the other hand, LRA is a statistical method that estimates the probability of a dichotomous outcome (“healthy” vs. “infected”) based on one or more independent variables. For this reason, LRA is of particular interest and widely used in biomedicine (Hosmer et al., 2013). Independently from the model used, part of the dataset obtained by experimental measurements (usually about two thirds of the total set of data) is used for training the model, and the remaining part is used for its validation. The goodness of the model is provided by the parameters sensitivity, specificity, and accuracy. The proportion of samples predicted to be infected that are actually “infected” is referred to as sensitivity or true positive rate, while the proportion of samples that are correctly predicted to be “healthy” is called specificity, or true negative rate. Accuracy is the proportion of right guesses, both “healthy” and “infected” samples (Parikh et al., 2008).

Reflectance and thermography are imaging techniques used widely in remote sensing and plant phenotyping. On the contrary, MCFI, otherwise very used in fundamental research on plant defense responses upon abiotic and biotic stress factors (Cerovic et al., 1999), has not been developed for its use at large scale. In the past, MCFI was applied in crop fields for some particular cases, although no systematic analysis of the images could be carried out at that time (Heisel et al., 1996; Johansson et al., 1996; Saito et al., 1998). In later years, advances have been made in its implementation at large scale (Tremblay et al., 2012; Latouche et al., 2015).

Multicolor fluorescence imaging is a non-invasive technique by which UV-excited autofluorescence is collected from plants or leaves. Fluorescence in the red and far red regions is emitted by chlorophyll a. In addition, the fluorescence in the blue and green regions (BGF) is emitted by secondary metabolites, many of them phenolics related to plant defense (Buschmann and Lichtenthaler, 1998). Therefore, the results serve as an indication of the activity of plant metabolism.

Imaging of leaf and canopy temperature by thermography has been widely used in plant phenotyping, mainly to characterize drought susceptibility (Li et al., 2014). Leaf temperature inversely correlates with transpiration and stomatal conductance (Jones, 1999), which is tightly regulated by plants as a general mechanism of defense upon abiotic stress, but also against pathogens (Melotto et al., 2008). This technique has been used in the study of infections by virus, bacteria, and fungi, as reviewed by Barón et al. (2016).

The pectinolytic Dickeya spp. are necrotrophic, Gram-negative plant pathogens that cause soft-rot disease and black-leg. A very wide range of plants are host for these species, including many economically important horticultural and ornamental plants worldwide (Czajkowski et al., 2011; Mansfield et al., 2012). Dickeya dadantii is able to infect fleshy, succulent plant parts, such as tubers, rhizomes, stems, and leaves, causing localized symptoms, thus limiting the crop yield and quality, and exerting significant losses in fields and in postharvest. D. dadantii is particularly pernicious due to its ability to live as saprophyte, epiphyte, or endophyte (Reverchon and Nasser, 2013), with great capacity for adaptation to new geographic areas and to new hosts (Reverchon et al., 2016). D. dadantii can be found in ground water, crop residues, soils, and also on other plants causing no infection, that would serve as a reservoirs (Nelson, 2009). It can also be isolated from the roots of healthy weeds in agricultural fields (Tsror et al., 2010). On the other hand, it can infect insects, which in turn may serve as dissemination vectors (Reverchon and Nasser, 2013). All these features make D. dadantii be among the top 10 most important bacterial pathogens in agriculture, according to Mansfield et al. (2012). In this scenario, early detection and appropriate farming practices are essential to minimize the spreading of infections. Bacteria enter primarily through hydathodes, stomata, and wounds to invade intercellular spaces. Once inside the plant tissue, D. dadantii produces and secretes degradative enzymes, mostly pectate lyases, which catalyze the hydrolysis of pectin, an essential component of the plant cell walls (Duprey et al., 2014). The consequent degradation of cell walls to gain access to nutrients, is the cause of soft-rot, the typical symptom of maceration (Hugouvieux-Cotte-Pattat et al., 1996).

In this work, the experimental host-pathogen systems were cucurbits (zucchini and melon) infected by D. dadantii. The main aim of this work is to explore the possible application of advanced statistical methods to data obtained by MCFI, on its own or by combination with thermography, for early disease detection. For this purpose, the predictions obtained by ANN and LRA are compared. The results show the convenience of MCFI in plant disease detection as a new approach for plant phenotyping.

Dealing with current problems in agriculture involves the development of new methodology to evaluate and monitor crops. Li et al. (2014) and von Bueren et al. (2015) have reviewed a number of techniques that are currently in use in vegetation analyses, including several techniques based on reflectance (RGB imaging, hyperspectral near infrared, multi and hyperspectral spectrometers), and thermography. Reflectance and thermography are well established approaches in precision agriculture (Oerke et al., 2014; Humplik et al., 2015). These techniques have also been applied to automated high-throughput analysis for plant phenotyping. In most cases, the screenings aimed for varieties resistant mainly to abiotic stress factors such as drought, heat and cold, salt stress, and nutrient deficiency, as reviewed by Humplik et al. (2015). A relatively small number of works have addressed the study of biotic stress, most of them focused on fungal infections. For example, Baranowski et al. (2015) reported the analysis of Alternaria infections in oilseed rape; Calderón et al. (2013, 2015) analyzed olive trees infected by Verticillium dahliae; the infection of almond trees with red leaf blotch was studied by López-López et al. (2016); and powdery mildew on tomato plants was investigated by Raza et al. (2015). However, other techniques on their own, or combined, could also be of great help in precision agriculture and plant phenotyping, as reviewed by Tremblay et al. (2012).

Thermography provides useful information in the study of biotic stress. It reflects changes in leaves temperature as a consecuence of modifications in the stomatal aperture, which regulates evapotranspiration (Jones, 1999). The activation of stomatal closure triggered by recognition of pathogen-associated molecular patterns is a widespread defense mechanism in vascular plants against bacterial invasion via abscisic acid, salicylic acid, and jasmonic acid (Melotto et al., 2006, 2008; Sawinski et al., 2013). The increase of temperature found at 3 dpi in zucchini seemed to be dose-dependent. It is worth noticing that the larger differences relative to mock-control values were found in N and D regions of the leaves, where no symptoms were visible (Figure 1). This is in agreement with Pérez-Bueno et al. (2016).

Multicolor fluorescence imaging has offered several parameters as good markers of infection in the zucchini-D. dadantii system. A vast number of phenolic compounds produced by the plant secondary metabolism are part of the defense response. These compounds emit BGF upon excitation with UV light (Cerovic et al., 1999; Dixon, 2001). According to the findings here reported, F440 and F520 of I areas significantly increased when compared to the mock-controls at 3 (HD) or 5 dpi (LD). Both F440 and F520 also increased in N and D areas of infected leaves. These results point to an enhancement on plant secondary metabolism in response to infection (Buschmann and Lichtenthaler, 1998; Cerovic et al., 1999). Moreover, F520 increased to a higher extent than F440, causing the decrease in their ratio F440/F520. This ratio showed statistically significant differences at 3 dpi in the three regions of interest. At later timepoints, the same trend was observed although no significant differences could be found out of the I areas. However, these results were consistent in all the experiments carried out. A decrease in F440/F520 values have been previously reported in relation to long-term stress conditions, under which the accumulation of particular green fluorescing compounds could be induced (Buschmann and Lichtenthaler, 1998).

For a long time, MCFI has been used in fundamental research to study the effects of a variety of stress factors in plant metabolism. BGF is well known to be influenced by the nutrient content in soil. Several authors reported changes in BGF related to alterations in the phenolics content in plants upon treatments with low or high nitrogen, depending on the species (Heisel et al., 1996; Langsdorf et al., 2000). MCFI has also been used in a number of works analyzing the effect of pathogens in their hosts. Viral infections in model plants have been analyzed by Pineda et al. (2008) and in crop plants by Chaerle et al. (2007) and Montero et al. (2016). Some bacterial and fungal infections have been studied by this technique (Granum et al., 2015; Pérez-Bueno et al., 2015, 2016). Currently, the use of this technique on proximal sensing is limited to the use of few available devices (Cerovic et al., 2012; Latouche et al., 2015). These devices have proved useful in the assessment of flavonoids content in grapes or kiwifruit, and in the detection of the nitrogen status for several species (Julkunen-Tiitto et al., 2014). However, the devices available up to date are not imaging sensors, which limits their applicability in field measurements and plant phenotyping programs. To our best knowledge, this work is the first one using data provided by MCFI in combination with thermal imaging to obtain statistical models able to identify infected plants at lab scale.

Data obtained from healthy and infected zucchini leaves by MCFI and thermography were used to train LRAs and ANNs. These models were validated for zucchini healthy and infected samples, achieving in some cases high values of accuracy, particularly for I areas. It is worth noticing that zucchini models also showed a very high accuracy for the classification of I areas of melon leaves. Other authors have addressed the detection of other infections by statistical models. Thus, infection by huanglongbing in citrus could be detected by support vector machine classification trees (Sankaran et al., 2013) and blight diseases on tomato leaves by extreme learning machine (Xie et al., 2015) with an accuracy ranging from 70 to 100%, respectively. On the other hand, several diseases in cucurbits have been analyzed by a combination of thermal, chlorophyll fluorescence dynamics, and hyperspectral imaging (Berdugo et al., 2014). In that work, a general linear model was able to classify plants infected with two different viruses and one fungal pathogen with an accuracy of 85–100%. Moreover, Rumpf et al. (2010) reported a support vector machine classifying beetroot leaves infected with three different fungi with an accuracy of 65–100%, based on measurements with a non-imaging spectroradiometer. The accuracy of the models in this work is comparable to those found in the literature, although none of them made use of MCFI.

The results here reported show the potential application of MCFI, in combination with thermography, particularly to classify infiltrated (symptomatic) areas as “healthy” or “infected.” The automatic detection of symptomatic areas has been carried out by other authors using image processing (Al-Hiary et al., 2011; Tian et al., 2012; Pujari et al., 2015). This pre-analysis of images was applied by Huang (2007) to isolate symptomatic areas caused by bacterial diseases in Phalaenopsis seedlings prior to their classification by mathematical models. Such an approach could constitute a possible strategy to scale up the use of MCFI to crop fields or phenotyping. Further development of the technique would be desirable in order to facilitate its applicability in plant phenotyping and breeding programs.

MP-B and MP took part in the experimental design, acquisition, analysis, and interpretation of data, and on writing up. FC contributed to the data analysis. MB took part in the experimental design, the interpretation of data and writing up.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.