Detection of Yield and Physiological Responses to Water Stress using Leaf Turgor Pressure and Thermal Images in Pepper

Abstract

Abstract Today, the use of sensors and imaging techniques 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 to detect water stress and the irrigation time of pepper and to determine their relationship with physiological traits (leaf water potential, stomatal conductivity, superoxide dismutase, and catalase activity). For this purpose, the pepper (Capsicum annuum L. cv. "California Wonder") experiment was carried out in Canakkale, Turkey in 2017 and 2018. A total of four treatments were applied in the experiment, control (100%) and three different water stress levels (75%, 50%, and 25%). Leaf turgor pressure (Pp), thermal images and physiological measurements were carried out during the growing season. Soil moisture and leaf turgor pressure (Pp) were monitored in real time by remote. 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. The physiological traits of pepper were also adversely affected by water stress. 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 the catalase activity. All the regression models between CWSI and IGp calculated from thermal images were statistically significant. The highest R2 values were obtained in August. In this month, the highest correlations were between CWSI and leaf water potential and stomatal conductivity (R2 = 0.91), IGp and stomatal conductivity (R2 = 0.80). The predictive power of CWSIp was higher than IGp. The experiment illustrated that Pp and temperature data, which are plant-based monitoring methods, have the potential to detect water stress in peppers.