Optical remote sensing methods used in agriculture include RGB imaging, multi- and hyperspectral imaging, thermography, and fluorescence imaging. Recently, the performance and availability of these types of sensors have increased significantly. In addition, a significant number of articles has been published on their usage [73,74]. An important feature of optical sensors is their ability to quickly acquire data from large areas. This is achieved through aircraft and satellite usage. Thus, these sensors may become a solution to the problem of plant disease detection on large areas of agricultural land [9]. As for the early diagnosis of plant diseases, not all types of optical sensors are optimal for this task [8,9,74].

Thermography makes it possible to study the changes in temperature of the studied object and thus to track the qualitative changes occurring in it. Thermographic sensors are commonly thermal imaging cameras which create 2D images capturing infrared (IR) radiation [50,75]. Some authors considered this method for early plant disease diagnosis. Oerke et al. (2006) studied the detection of cucumber downy mildew caused by the oomycete Pseudoperonospora cubensis. The study discovered that infrared thermography could serve as a suitable tool for disease analysis under controlled conditions. However, outdoors, this method did not provide an acceptable accuracy due to the variability of leaf temperature modified by environmental conditions [76]. Stoll et al. (2008), on the contrary, managed to obtain high accuracy when detecting the fungal pathogen Plasmopara viticola on grapevine [77]. Another study by Oerke et al. (2011), on apple scab caused by the fungi Venturia inaequalis, showed that thermographic measurements can reveal differences in disease severity resulting from disease stage, resistance of host tissue, and differences in the aggressiveness of V. inaequalis isolates [78].

Regardless, thermography allows the examination of large areas quickly using thermal imaging cameras and is a good tool for detecting plant diseases, but it is not suitable for early diagnosis due to the fact that the symptoms of different types of biotic and abiotic stress look very similar on thermographic images. In addition, this method is negatively affected by changes in ambient temperature. For the same reasons, the use of this technology to detect diseases at early stages is hardly possible, especially in the case of latent or concomitant infections [9,51,79].

Fluorescence imaging allows the study of changes in the photosynthetic activity of plants and thus detect the presence of pathogens. Devices for fluorescence imaging are usually active sensors with an LED or laser light source. The most common method to measure chlorophyll fluorescence uses pulse amplitude modulation (PAM) fluorometry [80,81]. This method was described in the following articles. Rodrıguez-Moreno et al. (2008) was able to perform the early detection of bean infection by the bacterium Pseudomonas syringae using red chlorophyll fluorescence that was measured using the kinetic imaging chlorophyll fluorometer FluorCam (Photon Systems Instruments, Brno, Czech Republic) [82]. Baurigel et al. (2014) studied wheat head blight caused by the Fusarium spp. fungi. The authors obtained good results both in laboratory and field measurements. Under laboratory conditions, chlorophyll fluorescence imaging was able to early detect even very low levels of infection (ca. 5%) as early as the sixth day after inoculation, while visual classification was only possible beginning from seventh day after inoculation [83].

Although fluorescence imaging cannot cover large areas due to technical limitations, in laboratory studies, this method allows the detection of plant diseases at early stages before visible symptoms appear. However, plants under biotic and abiotic stresses may look very similar when using this technique. In addition, following a strict sample preparation protocol is needed for fluorometry. Thus, the difficulties and the disadvantages of the method make it nearly impossible for common early plant disease detection in agriculture [9,80].

Sun-induced chlorophyll fluorescence is a promising new direction in the remote sensing of plants. The Earth Explorer-Fluorescence Explorer (FLEX) mission, a European Space Agency (ESA) mission, can map vegetation fluorescence to quantify photosynthetic activity which will lead to better insights into crop health and stress [84,85]. Interesting new data have already been obtained in such areas of remote sensing as nitrogen uptake [86], fundamental vegetation trait quantification [87], and drought stress [88].

The advantages and disadvantages of this remote sensing technique are discussed in reviews [89,90], while studies [91,92] show the possibility of its practical application. However, authors believe that current data to assess the potential of this technique for the early detection of plant diseases are insufficient.

RGB imaging uses the RGB range to acquire 2D images of a selected object in order to study its changes. The significant increase in the resolution of RGB cameras, along with the increase in their availability, has made it possible to obtain HD images [40,93]. Commercial satellites of the latest generations can also obtain very-high-resolution imagery [94]. Obtaining data of such a high quality allows people to confidently recognize the visual manifestations of plant diseases, both by the standard method of human expert rating or using various automation tools. However, despite all the advantages of the RGB imaging method, it is difficult to use it for early plant disease detection, due to the fact that many diseases do not have any visual symptoms at an early stage [95,96]. In addition, there is an issue of very similar changes in leaf color and texture induced by abiotic and biotic stresses, which makes their accurate diagnosis nearly impossible [97,98].

Multi- and hyperspectral imaging are the most promising among the optical imaging techniques for early plant disease detection and diagnosis. Multispectral sensors collect data from a small number (usually 3–15) of spectral ranges. Hyperspectral sensors use hundreds of channels within which high-resolution information is collected and recorded independently. These bands cover a wide range of wavelengths ranging from 400 to 2500 nm: the VIS range (400–700 nm), the NIR range (700–1100 nm), and the SWIR range (1100–2500) [26,99,100]. In recent years, a large number of different hyperspectral sensors covering these ranges has become available for scientifical and practical use, including satellite-based ones [101,102].

Hyperspectral imaging offers many more opportunities for early plant disease detection because it provides image data with very high spectral resolution that can help with the accurate and timely determination of the physiological status of agricultural crops [103]. In recent years, the number of studies on early plant disease detection using hyperspectral imaging has increased significantly [104]. These studies prove that hyperspectral imaging can detect diseases caused by fungal [105,106,107], viral [108,109,110], and bacterial pathogens [111,112,113] as well as various abiotic stresses [114,115]. The data on the most studied crops (citrus fruits, nightshades, oil palm, and wheat) were reviewed in detail by Terentev et al. (2022). The authors of the review mentioned that despite the presence of a large number of articles on early plant disease detection using hyperspectral remote sensing, no unified methods for detecting diseases in respective specific wavelength ranges have been developed yet [26].

The main disadvantage of optical imaging is the difficulty to accurately diagnose diseases, including latent and mixed infections [9]. In addition, the disadvantages of optical sensors include the high requirements for the automation of large data volume analysis [26,117]. In the case of satellites, clouds are a common problem, which can make it impossible to obtain data at the time needed [118]. Multispectral and hyperspectral sensors may be too expensive for small farm usage [119].

The most promising and relevant areas of optical imaging are RGB and hyperspectral imaging. RGB imaging can already solve many problems with detecting diseases and plant pests. There are specialized software products that make it easier for agronomists to identify plant diseases using RGB imaging. These are applications for identifying greenhouse pests such as the Syngenta Pest Management App or apps for crop protection specialists such as Agrio or AgroAI [120]. The use of high-quality satellite photos makes it possible to monitor weeds, pests, and disease outbreaks over vast areas. However, due to very similar changes in leaf color induced by plant pathogens and the absence of visible symptoms in some cases, RGB imaging cannot act as a tool for early plant disease diagnosis [9,10].

Hyperspectral remote sensing is one of the most promising tools for diagnosing plant diseases at an early stage [8,10]. Hyperspectral snapshot cameras have the potential to create systems for detecting and diagnosing plant disease in large agricultural areas. However, there are existing gaps that prevent the creation of such systems [9,26].

The low resolution on current hyperspectral cameras is one of the factors hindering successes in their application [119]. This complicates the task of using them for the early detection of diseases when receiving data from satellites and UAVs. In addition, at the moment, there are no hyperspectral snapshot cameras operating in the VIS-NIR-SWIR ranges simultaneously, and at least two different devices are required to capture data from large areas (for example, Cubert S185 for the 400–1000 nm range and Specim SWIR for the 1000–2500 nm range). Existing hyperspectral sensors which operate in all the three ranges simultaneously, such as the ASD FieldSpec 4 spectroradiometer, which operates in the wavelength range of 350–2500 nm, are push-broom cameras, with all the ensuing shortcomings [26]. This factor is technical and it is likely to be overcome in the future, the same as the current high cost of hyperspectral sensors.

Spectroscopy is a branch of science that studies the spectra of electromagnetic radiation as a function of wavelength or frequency, measured by spectrographic equipment and other methods, to obtain information about the structure and properties of the studied matter. There is a wide variety of spectroscopy techniques that are used in various fields of study. UV-VIS-NIR spectroscopy, infrared spectroscopy (IR), fluorescence spectroscopy (FS), and Raman spectroscopy (RS) are the most used in plant protection studies [9,73,74].

UV-VIS-NIR spectroscopy is a method used to determine the optical properties (transmittance, reflectance, and absorbance) of liquids and solids. It operates in the optical range between 175 nm and 3300 nm. The technique measures the absorption of light across the desired optical range [74,122].

In publications devoted to the detection of plant diseases, instead of UV/VIS/NIR spectroscopy, authors sometimes use the terms VIS/NIR or NIRS spectroscopy, depending on the equipment used and its optical range. In recent years, multiple works that reveal the possibility of successfully detecting plant diseases at an early stage with this method have appeared [110,123,124,125,126].

In their study, Morellos et al. (2020) were aiming to develop an algorithm for tomato chlorosis virus (ToCV) detection using VIS-NIR spectrometry. The authors mentioned that ELISA and RT-PCR were the current conventional methods for ToCV detection. The authors managed to reach up to 85% early classification accuracy of ToCV when applying ANN to VIS-NIR spectroscopy data [110]. Nijar and Abu-Khalaf (2021) were able to reach up to 100% early classification accuracy of tomato gray mold caused by the anamorph fungus Botrytis cinerea. The results of VIS-NIR spectroscopy were verified using PCR. The authors used PCA as the data analysis tool [123]. Lelong et al. (2010) used VIS-NIR spectroscopy to evaluate the severity of oil palm basal stem rot caused by the fungus Ganoderma boninense. A classification accuracy of 94% was achieved using PLS-DA [124]. Hou et al. (2022) studied tomato late blight caused by the oomycete Phytophthora infestans. The study combined VIS-NIR spectroscopy with machine learning. The classification accuracy reached up to 99% [125]. Tu et al. (2022) managed to determine early drought stress of tomato with VIS-NIR spectroscopy data. The authors used 1D-SP-Net as the main classification tool, which outperformed 1D-CNN, partial least squares discriminant analysis (PLSDA), and random forest (RF) models, demonstrating an accuracy of 96.3% [126].

The advantage of UV-VIS-NIR spectroscopy is that it is suitable for the determination of a wide analyte concentration variety in a solution. In addition, the quantification of analytes in solutions using UV/VIS/NIR is simpler and less time-consuming than chromatographic analysis [74,122]. The disadvantage of this method is that chromatographic analysis is more accurate and precise than UV/VIS/NIR. Another very important disadvantage is that some components in a sample solution may interfere with other components, which makes the research results questionable [9,122].

Fluorescence spectroscopy is a type of electromagnetic spectroscopy that analyzes the fluorescence of a sample. It uses a beam of light, usually ultraviolet (wavelength from 10 to 400 nm), that excites the electrons in the molecules of certain compounds and causes them to emit light. The devices that measure fluorescence are called fluorometers [50].

FS can be used for early plant disease detection, which has been shown in a number of studies [127,128,129,130]. Belasque et al. (2007) and Lins et al. (2008) studied citrus canker caused by the bacteria Xanthomonas axonopodis pv. citri with laser fluorescence spectroscopy (LIF) and managed to detect diseased leaves at early stages [127,128]. In their study, Sankaran et al. (2012) were able to detect citrus greening caused by the bacterium Candidatus Liberibacter asiaticus at early stages of the disease. Naïve-Bayes and bagged decision tree classifiers reached more than 85% and 94% detection accuracy, respectively [129]. Sallem et al. (2020) managed to detect citrus canker on grapefruits using LIF. Principal component analysis (PCA) and partial least square regression (PLSR) both showed excellent results at classifying the disease at early stages [130]. The main advantage of the FS method is that it can detect the concentration of a component with a sensitivity around 1000 times greater than that of most spectrophotometric methods. The major challenge for FS is photobleaching [9]. Photobleaching is a general term for any photochemical process that causes the molecule to be permanently unable to fluoresce. This phenomenon results in decreased sensitivity, and inaccurate recording and data collection, and its influence was observed in the application of FS in early plant disease detection. Although there are ways to circumvent this limitation, FS, despite its advantages, has not yet become widely used in the field of plant protection [50,131].

IR spectroscopy refers to vibrational spectroscopy. It utilizes the concept that molecules tend to absorb specific frequencies of light that are characteristic of the corresponding structure of the molecules. IR radiation is absorbed by the molecules at specific frequencies depending on the molecular bonds between atoms and the types of atoms at the ends of the bonds. Analysis of the infrared spectrum of absorption or emission allows a determination of the chemical composition of the sample [132].

Fourier-transform infrared (FTIR) spectrometers are the most common instruments used for IR spectroscopy. FTIR measures the absorbance of infrared light of a sample and generates a spectrum based on the functional groups in the material. The difference between IR and FTIR is that IR is constructed from a raw signal and FTIR is constructed from an interferogram. IR takes a single spectrum, whereas FTIR employs an interferometer and takes a number of scans. IR uses monochromatic light and FTIR uses polychromatic light [133].

In the last decade, only a small number of works have been published on early plant disease detection using IR and FTIR spectrometry [134,135,136,137]. In their study, Sankaran et al. (2010) used an InfraSpec VFA-IR spectrometer (Wilks Enterprise Inc., East Norwalk, CT, USA) to collect the mid-infrared spectra in the range of 5.15–10.72 m (1942–933 cm⁻¹) with a 0.04 m resolution. The authors used quadratic discriminant analysis (QDA) and k-nearest neighbors (kNN) as a classifier tool to detect citrus greening caused by the bacteria Candidatus liberibacter spp. The performance of the kNN-based algorithm (higher than 95%) was better than the QDA-based algorithm [134]. Salman et al. (2010) and Erukhimovitch et al. (2010) used an FTIR spectrometer (Bruker Tensor 127) to study if it is possible to detect four different soil fungi genera: Rhizoctonia, Colletotrichum, Verticillium, and Fusarium oxysporum, which may cause serious damage to a large number of crops. The authors used pure fungi cultures for FTIR spectroscopy measurements. The measurements were performed using FTIR–ATR with a liquid-nitrogen-cooled mercury-cadmium-telluride MCT detector in the wave region 600–4000 cm⁻¹, with a spectral resolution of 4 cm⁻¹ [135,136]. Hawkins et al. (2010) made a comparison of citrus greening with other citrus diseases using the FTIR technique. ATR spectra were collected using a Thermoelectron Nicolet (Madison, WI) Magna 850 FTIR spectrometer with a deuterated triglycine sulfate (DTGS) detector. The spectra were acquired at 2 cm⁻¹ resolutions [137].

One of the major gaps of IR-based spectroscopy techniques is the need for complicated sample preparation. The removal of water is a typical step in the sample preparation for IR spectroscopy because water is highly IR active. This complicates the work, increases the personnel requirements, and thus practically eliminates the method’s advantages [9,73,138]. For this reason, although handheld FTIR spectrometers already exist, their application in agronomy does not include the field of plant protection, but is limited to the analysis of soil conditions [139].

The advantage of this area of spectroscopy is that IR and FTIR spectrometers are non-destructive and highly sensitive. They are capable of identifying organic functional groups and often specific organic compounds. IR spectroscopy can be quantitative with appropriate standards and uniform sample thicknesses. There are handheld FTIR spectrometers that can be used for field diagnostics. IR spectroscopy is complementary to Raman spectroscopy [9,133,140].

The downsides of IR spectroscopy are limited surface sensitivity and the requirement for standard usage for sample quantitation. The identification of mixtures/multiple sample components may require additional laboratory preparations and analyses. The biggest disadvantage of the method is that water strongly absorbs infrared light which may interfere with the analysis of dissolved, suspended, or wet samples. This makes it extremely difficult to obtain data from the cytoplasm and extracellular fluid of plant tissues, and thus makes it almost impossible to use handheld IR and FTIR devices in the field [9,132,139].

Raman spectroscopy is based on inelastic photon scattering, known as Raman scattering. Laser light interacts with the vibrations of atoms in molecules, phonons, or other excitations in the system, as a result of which the energy of laser photons is shifted to the region of high or low values. This energy shift provides information about the vibrational modes in the system. Infrared spectroscopy usually provides similar but additional information [132,141]. The main difference between Raman and IR spectroscopy is that Raman spectroscopy depends on a change in polarizability of a molecule, whereas IR spectroscopy depends on a change in the dipole moment. Raman spectroscopy measures the relative frequencies at which a sample scatters radiation, whereas IR spectroscopy measures absolute frequencies at which a sample absorbs radiation [133,141].

In general, most of the molecules that have symmetry manifest themselves in both infrared and Raman spectra. Molecules with an inversion center are a special case. If the molecule has an inversion center, then Raman and IR will be mutually exclusive, that is, the bond will be active in either Raman or IR spectra. There is a general rule that functional groups with strong changes in the dipole moment are clearly visible in IR spectra, while functional groups with weak changes or with a high degree of symmetry are more visible in Raman spectra [132].

Raman spectroscopy has a number of advantages over IR and FTIR for plant disease studies. It can more easily investigate carbon bonds in aliphatic and aromatic rings. In addition, RS can be used to identify molecules with bonds that are difficult to see in IR spectra (for example, O–O, S–H, C=S, N=N, C=C, etc.). Raman spectroscopy is more suitable for studying reactions in aqueous media (water has a very small Raman cross-section, allowing for spectral acquisition from cytoplasm and extracellular fluid) [9,140].

In most RS-based plant disease studies, the authors used hand-held Raman spectrometers, as this is the most suitable for a future practical application. A number of authors have proved that Raman spectroscopy usage can determine plant diseases caused by all types of pathogens, e.g., viral [142,143,144,145], bacterial [146,147,148,149], and fungal [150,151,152]. The main aspects of Raman spectroscopy usage for plant disease detection were discussed in detail in the review by Farber et al. (2019). However, a unified system for detecting plant diseases via this method has not yet been developed [9].

The advantage of Raman spectroscopy is that it is non-destructive and highly sensitive. There are handheld Raman spectrometers that can be used for in-field diagnostics. Raman spectrometers are capable of identifying organic functional groups and specific organic compounds. Raman spectroscopy can be quantitative with appropriate standards and uniform sample thicknesses. IR spectroscopy is complementary to Raman spectroscopy, which is important for the development of the method.

The main disadvantage of Raman spectroscopy is that it can hardly be used to study highly fluorescent samples. The identification of sample components in some cases may require laboratory preparations and analyses and cannot be performed with a handheld device. Although there are some libraries for compound and mixture identifications, the information on plant metabolites is not yet exhaustive.

New technical methods used in plant sciences generate a huge amount of data about the studied objects, both plants and pathogens. Therefore, automatic means of data processing and analysis are used for their adaptation to the needs of agriculture [73], wherein different methods and approaches are suitable for different purposes. When creating primers and probes for PCR, bioinformatics approaches are used [153]. When analyzing spectrometry, chromatography, and optical sensor data, diseased plant health data may not be analyzed correctly using parametric approaches such as simple or multiple regression and functional analysis; therefore, non-parametric approaches are used [154]. The commonest types of non-parametric classifiers used for diseased and healthy plant determination are principal component analysis (PCA), support vector machine (SVM), cluster analysis (CA), partial least-square (PLS), and artificial neural network (ANN) [155,156]. When processing chromatographic and spectrometric data, in addition to non-parametric classifiers, databases are also used to determine recognizable substances and compounds [157,158,159]. The choice of an analysis algorithm depends on many factors, such as data amount, the presence of a visible feature’s ability to be distinguished, and so on [160]. Therefore, the correct approach to the choice of instruments for classification is one of the most important success factors in early plant disease remote sensing.

When analyzing data from optical sensors, spectral vegetation indices (SVI) are often used. The SVI obtained from remote sensing are simple and effective tools for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics. Specific SVI can be used to detect certain plant diseases based on formulae including disease-specific wavebands [114,161].

The big data obtained through spectrometry, chromatography, and optical sensors contain everything necessary for early plant disease detection. At the same time, the methods of analyzing this data such as machine learning, neural networks, and statistical and manual analysis, despite their huge applied role, are only automation methods and do not make a significant contribution to solving the problem of early plant disease detection [26,162].