Agricultural Water Management (Feb 2024)
Plant-based monitoring techniques to detect yield and physiological responses in water-stressed pepper
Abstract
Today, the use of sensors and imaging techniques, which are used to obtain information about plants and soil in smart irrigation systems, is rapidly becoming widespread. This study aimed to investigate the usability of leaf turgor pressure and thermal images from plant-based monitoring techniques to detect water stress and the irrigation time of pepper (Capsicum annuum L. cv. ''California Wonder'') and to determine their relationship with physiological traits in Canakkale/Türkiye in 2017 and 2018. The four irrigation treatments (100%, 75%, 50%, and 25%) were applied in the experiment. Leaf turgor pressure (Pp), thermal images and physiological measurements were carried out during the growing season. Soil moisture and Pp were monitored in real time by remote. Thermal and physiological measurements were made before each irrigation. As a result of the study, the average evapotranspiration (ETc) was 697 mm, and the yield value was 83.7 t ha−1 under non-stress conditions. Depending on the decrease in ETc, yield values also decreased significantly. Leaf water potential and stomatal conductivity values were statistically different in all irrigation treatments. The change in the activity of catalase (CAT) due to water stress was greater than that of superoxide dismutase (SOD). In this case, it can be said that other physiological traits are more successful than SOD in distinguishing water stress. According to the regression models, significant relationships were determined between both the indices calculated from the thermal images and Pp, yield, and physiological traits. The predictive ability of Pp values has been strengthened with the addition of meteorological properties to the model in general. The highest correlation (R2 =0.63) was between Pp + meteorological properties and CAT. All the regression models between physiological traits and indices calculated from thermal images were statistically significant. The highest R2 values were obtained in August. In this month, the highest correlations were between Crop Water Stress Index (CWSIp) and leaf water potential / stomatal conductivity (R2 =0.91), IGp and stomatal conductivity (R2 =0.80). The predictive power of CWSIp was higher than Stomatal Conductivity Index (IGp). The experiment illustrated that Pp and temperature data, which are plant-based monitoring methods, have the potential to detect water stress in peppers.
