Approaches for remote sensing can be used to estimate the influence of changes in environmental conditions on terrestrial plants, providing timely protection of their growth, development, and productivity. Different optical methods, including the informative multispectral and hyperspectral imaging of reflected light, can be used for plant remote sensing; however, multispectral and hyperspectral cameras are technically complex and have a high cost. RGB imaging based on the analysis of color images of plants is definitely simpler and more accessible, but using this tool for remote sensing plant characteristics under changeable environmental conditions requires the development of methods to increase its informativity. Our review focused on using RGB imaging for remote sensing the characteristics of terrestrial plants. In this review, we considered different color models, methods of exclusion of background in color images of plant canopies, and various color indices and their relations to characteristics of plants, using regression models, texture analysis, and machine learning for the estimation of these characteristics based on color images, and some approaches to provide transformation of simple color images to hyperspectral and multispectral images. As a whole, our review shows that RGB imaging can be an effective tool for estimating plant characteristics; however, further development of methods to analyze color images of plants is necessary.

Parameters of illumination including the spectra, intensity, and photoperiod play an important role in the cultivation of plants under greenhouse conditions, especially for vegetables such as lettuce. We previously showed that illumination by a combination of red, blue, and white LEDs with a high red light intensity, was optimal for lettuce cultivation; however, the effect of the photoperiod on lettuce cultivation was not investigated. In the current work, we investigated the influence of photoperiod on production (total biomass and dry weight) and parameters of photosynthesis, respiration rate, and relative chlorophyll content in lettuce plants. A 16 h (light):8 h (dark) illumination regime was used as the control. In this work, we investigated the effect of photoperiod on total biomass and dry weight production in lettuce plants as well as on photosynthesis, respiration rate and chlorophyll content. A lighting regime 16:8 h (light:dark) was used as control. A shorter photoperiod (8 h) decreased total biomass and dry weight in lettuce, and this effect was related to the suppression of the linear electron flow caused by the decreasing content of chlorophylls and, therefore, light absorption. A longer photoperiod (24 h) increased the total biomass and dry weight, nevertheless an increase in photosynthetic processes, light absorption by leaves and chlorophyll content was not recorded, nor were differences in respiration rate, thus indicating that changes in photosynthesis and respiration are not necessary conditions for stimulating plant production. A simple model to predict plant production was also developed to address the question of whether increasing the duration of illumination stimulates plant production without inducing changes in photosynthesis and respiration. Our results indicate that increasing the duration of illumination can stimulate dry weight accumulation and that this effect can also be induced using the equal total light integrals for day (i.e., this stimulation can be also caused by increasing the light period while decreasing light intensity). Increasing the duration of illumination is therefore an effective approach to stimulating lettuce production under artificial lighting.

In environmental conditions, plants can be affected by the action of numerous abiotic stressors. These stressors can induce both damage of physiological processes and adaptive changes including signaling-based changes. Development of optical methods of revealing influence of stressors on plants is an important task for plant investigations. The photochemical reflectance index (PRI) based on plant reflectance at 531 nm (measuring wavelength) and 570 nm (reference wavelength) can be effective tool of revealing plant stress changes (mainly, photosynthetic changes); however, its efficiency is strongly varied at different conditions. Earlier, we proposed series of modified PRIs with moderate shifts of the measuring wavelength and showed that these indices can be effective for revealing photosynthetic changes under fluctuations in light intensity. The current work was devoted to the analysis of sensitivity of these modified PRIs to action of drought and short-term heat stress. Investigation of spatially-fixed leaves of pea plants showed that the modified PRI with the shorter measuring wavelength (515 nm) was increased under response of drought and heat; by contrast, the modified PRI with the longer wavelength (555 nm) was decreased under response to these stressors. Changes of investigated indices could be related to parameters of photosynthetic light reactions; however, these relations were stronger for the modified PRI with the 555 nm measuring wavelength. Investigation of canopy of pea (vegetation room) and wheat (vegetation room and open-ground) supported these results. Thus, moderate changes in the measuring wavelengths of PRI can strongly modify the efficiency of their use for the estimation of plant physiological changes (mainly photosynthetic changes) under action of stressors. It is probable that the modified PRI with the 555 nm measuring wavelength (or similar indices) can be an effective tool for revealing photosynthetic changes induced by stressors.

Soil drought and salinization are key abiotic stressors for agricultural plants; the development of methods of their early detection is an important applied task. Measurement of red-green-blue (RGB) indices, which are calculated on basis of color images, is a simple method of proximal and remote sensing of plant health under the action of stressors. Potentially, RGB indices can be used to estimate narrow-band reflectance indices and/or photosynthetic parameters in plants. Analysis of this problem was the main task of the current work. We investigated relationships of six RGB indices (r, g, b, ExG, VEG, and VARI) to widely used narrow-band reflectance indices (the normalized difference vegetation index, NDVI, and photochemical reflectance index, PRI) and the potential quantum yield of photosystem II (Fv/Fm) in wheat and pea plants under soil drought and salinization. It was shown that investigated RGB indices, NDVI, PRI, and Fv/Fm were significantly changed under the action of both stressors; changes in some RGB indices (e.g., ExG) were initiated on the early stage of action of drought or salinization. Correlation analysis showed that RGB indices (especially, ExG, VARY, and g) were strongly related to the NDVI, PRI, and Fv/Fm; linear regressions between these values were calculated. It means that RGB indices measured by simple and low-cost color cameras can be used to estimate plant parameters (NDVI, PRI, and Fv/Fm) requiring sophisticated equipment to measure.

Salinization is a significant adverse factor that suppresses productivity of agricultural plants. Multispectral imaging and calculation of reflectance indices, including the typical photochemical reflectance index (PRI), can be used for early revealing of changes in plants under salinization. However, the direction of change in typical PRI is dependent on the type of stressor and the experimental conditions. Earlier, we proposed modified PRIs with shifted measuring wavelengths and analyzed their changes under excess light, water deficit, and heating. In the current work, we investigated the sensitivity of these indices to changes in pea plants under salinization (100, 200, and 400 mM NaCl treatment). It was shown that short-wavelength PRIs were increased under this treatment; in contrast, long-wavelength PRIs were decreased. Most of the modified PRIs were strongly related to the maximal yield of photosystem II and the normalized difference vegetation index. Long-wavelength PRIs were more sensitive to plant changes than short-wavelength PRIs, typical PRI, and the normalized difference vegetation index because their stable decrease under moderate salinization (the 200 mM NaCl treatment) was initiated earlier. Our results show that long-wavelength PRIs, which also decreased under the action of the excess light, water deficit, and heating, can be potentially used as a universal tool for early revealing of stress changes in plants.

Remote sensing plays an important role in plant cultivation and ecological monitoring. This sensing is often based on measuring spectra of leaf reflectance, which are dependent on morphological, biochemical, and physiological characteristics of plants. However, interpretation of the reflectance spectra requires the development of new tools to analyze relations between plant characteristics and leaf reflectance. The current study was devoted to the development, parameterization, and verification of the analytical model to describe reflectance spectra of the dicot plant leaf with palisade and spongy mesophyll layers (on the example of pea leaves). Four variables (intensities of forward and backward collimated light and intensities of forward and backward scattered light) were considered. Light reflectance and transmittance on borders of lamina (Snell’s and Fresnel’s laws), light transmittance in the palisade mesophyll (Beer–Bouguer–Lambert law), and light transmittance and scattering in the spongy mesophyll (Kubelka–Munk theory) were described. The developed model was parameterized based on experimental results (reflectance spectra, contents of chlorophylls and carotenoid, and thicknesses of palisade and spongy mesophyll in pea leaves) and the literature data (final R² was 0.989 for experimental and model-based reflectance spectra). Further model-based and experimental investigations showed that decreasing palisade and spongy mesophyll thicknesses in pea leaves (from 35.5 to 25.2 µm and from 58.6 to 47.8 µm, respectively) increased reflectance of green light and decreased reflectance of near-infrared light. Similarity between model-based and experimental results verified the developed model. Thus, the model can be used to analyze leaf reflectance spectra and, thereby, to increase efficiency of the plant remote and proximal sensing.