Stresses

Understanding the impact of hydric stress on medicinal plants in the context of climate change is becoming increasingly important. This study aimed to assess the quality of a seed lot of Agastache mexicana subsp. mexicana (Amm) through a novel calculation of the Vigour Index on time basis (${VI}_T$). The evaluation was based on relationships among plant height, leaf number, survival time, and plant density across six irrigation regimes, referred to as stages, which differed in the timing and quantity of water, designed to impose water stress from seedling emergence until plant death. To maximise growth and survival time, we utilised two input factors: Artificial Shade Levels (ASLs) of 38%, 87%, and 94%, as well as Silicon Dioxide Levels (SDLs) of 0.0%, 0.2%, 0.4%, and 0.8%. The effects of these treatments were measured using the Survival Index (SI) and the ${VI}_T$. The plants achieved their highest SI and ${VI}_T$ values influenced by minimum mortality and maximum height and leaf number in stage three. This behaviour aligned with the field capacity of the substrate, supporting the evaluation of stages one and two as waterlogging stress, while the remaining stages were classified as drought stress. The ${VI}_T$ results showed statistically significant effects from ASL, particularly at 94%. However, the ${VI}_T$ in relation to SDL was not statistically significant. The ${VI}_T$ measurements were visualised using spline interpolation, a method that provides an effective approach to quantify adverse conditions affecting Amm’s development and that it can support to identify the hydric stresses type.

The development of remote methods for identifying plant light stress (LS) is an urgent task in agriculture and forestry. Evergreen conifers, which experience winter light stress (WLS) annually, are ideal subjects for studying the mechanisms of light stress and developing identification methods. Proximal hyperspectral imaging (HSI) was used to identify WLS in Platycladus orientalis. Using the random forest (RF), the spectral characteristics of P. orientalis shoots were analysed and the conditions ‘Winter Light Stress’ and ‘Optimal Condition’ were classified with high accuracy. The out-of-bag (OOB) estimate of the error rate was only 0.35%. Classification of the conditions ‘Cold Stress’ and ‘Optimal Condition’—with an OOB estimate of error rate of 3.19%—can also be considered successful. The conditions ‘Winter Light Stress’ and ‘Cold Stress’ were more poorly separated (OOB error rate 15.94%). Verifying the RF classification model for the three states ‘Optimal condition’, ‘Cold stress’ and ‘Winter Light Stress’ simultaneously using data from the crown field survey showed that the ‘Winter Light Stress’ state was well identified. In this case, ‘Optimal condition’ was mistakenly defined as ‘Cold stress’. The following vegetation indices were significant for identifying WLS: CARI, CCI, CCRI, CRI₅₅₀, CTRI, LSI, PRI, PRI_m1, modPRI and TVI. Therefore, spectral phenotyping using HSI is a promising method for identifying WLS in conifers.

Sugar beet (Beta vulgaris L.) is very sensitive to fluctuations in micronutrient availability, and either an excess or a shortage of boron (B) may reduce the plant’s development and its ability to withstand stress. B is essential for photosynthesis and cell wall integrity, but the physiological requirements for an optimal supply during early development remain unclear. The photosynthetic efficiency and oxidative stress reactions of sugar beet seedlings were tested under five different B concentrations: 0, 50, 500, 1000, and 2000 µM H₃BO₃. Integrating non-invasive methods like SPAD, delayed fluorescence (DF), and maximum quantum efficiency of PSII (Fv/Fm) with red–green–blue (RGB) imaging enabled the detailed processing of both the initial and decay phases of DF. According to the results, SPAD and Fv/Fm were not sensitive indicators of early B stress; however, DF decay slopes and red–green–blue pixel distribution distinguished between optimum (500 µM), inadequate (0 µM), and hazardous (2000 µM) treatments. Moreover, lipid oxidation-related biochemical analyses were used to evaluate the ferric reducing antioxidant capacity (FRAP) and malondialdehyde (MDA) concentration. At the extremes of insufficiency and toxicity, MDA levels demonstrated enhanced lipid peroxidation, while FRAP increased with B concentration. The outcome of the research revealed optimum (500 µM) and toxicity-inducing (2000 µM) concentrations at early stages of sugar beet development. The study highlights that the combined use of DF kinetics and RGB analysis provides valuable, non-invasive markers for the early identification of B-stress, which is also confirmed by biochemical indicators, thereby promoting more efficient micronutrient management in sugar beet cultivation.