Combining thermal imaging and soil water content sensors to assess tree water status in pear trees

Abstract

Volumetric soil water content is commonly used for irrigation management in fruit trees. By integrating direct information on tree water status into measurements of soil water content, we can improve detection of water stress and irrigation scheduling. Thermal-based indicators can be an alternative to traditional measurements of midday stem water potential and stomatal conductance for irrigation management of pear trees (Pyrus communis L.). These indicators are easy, quick, and cost-effective. The soil and tree water status of two cultivars of pear trees ‘D’Anjou’ and ‘Bartlett’ submitted to regulated deficit irrigation was measured regularly in a pear orchard in Rock Island, WA (USA) for two seasons, 2021 and 2022. These assessments were compared to the canopy temperature (Tc), the difference between the canopy and air temperature (Tc-Ta) and the crop water stress index (CWSI). Trees under deficit irrigation had lower midday stem water potential and stomatal conductance but higher Tc, Tc-Ta, and CWSI. Tc was not a robust method to assess tree water status since it was strongly related to air temperature (R = 0.99). However, Tc-Ta and CWSI were greater than 0°C or 0.5, respectively, and were less dependent on the environmental conditions when trees were under water deficits (midday stem water potential values< -1.2 MPa). Moreover, values of Tc-Ta = 2°C and CWSI = 0.8 occurred when midday stem water potential was close to -1.5 MPa and stomatal conductance was lower than 200 mmol m-2s-1. Soil water content (SWC) was the first indicator in detecting the deficit irrigation applied, however, it was not as strongly related to the tree water status as the thermal-based indicators. Thus, the relation between the indicators studied with the stem water potential followed the order: CWSI > Tc-Ta > SWC = Tc. A multiple regression analysis is proposed that combines both soil water content and thermal-based indices to overcome limitations of individual use of each indicator.

Evaluation of Computer Vision Systems and Applications to Estimate Trunk Cross-Sectional Area, Flower Cluster Number, Thinning Efficacy and Yield of Apple

“…Precision agriculture uses information obtained from temporal data to address variability within an orchard, with the objective of reducing the variability through variable management and increase the crop value [23]. As growers adopt digital technologies, it is critical to validate the precision of the new technologies [1,24].…”