Automatic diagnosis of strawberry water stress status based on machine vision

Water stress status of plants is very important for irrigation scheduling.However,plant water stress status monitoring has become the bottleneck of irrigation scheduling.In this study,an automatic water stress status monitoring method for strawberry plant was proposed and realized using combined RGB and infrared image information.RGB image and infrared images were obtained using RGB digital camera and infrared thermal camera,which were placed in a fixed shell in parallel.In the first experimental stage,three kinds of water stress treatments were carried out on three groups of strawberry plants,and each group includes three repetitions.Single point plant temperature,dry surface temperature,wet surface temperature were measured.In the second experimental stage,the infrared and visible light images of the canopy leaves were obtained.Meanwhile,plant temperature,dry surface temperature,wet surface temperature,and stomatal conductance were measured not only for single point but also for plant area temperature measurement.Fusion information of infrared image and visible light image was analyzed using image processing technology,to calculate the average temperature of plant areas.Based on single point temperature,area temperature,dry surface temperature and wet surface temperature of the plant,single point crop water stress index(CWSI)and area CWSI were calculated.Through analysis of variance(ANOVA),the experimental results showed that CWSI measured for plants under different treatments,were significantly different.Through correlation analysis,the experimental results showed that,determination coefficient between area CWSI and the corresponding stomatal conductance of three strawberry groups were 0.8834,0.8730 and 0.8851,respectively,which were larger than that of single-point CWSI and stomatal conductance.The results showed that area CWSI is more suitable to be used as the criteria for automatic diagnosis of plants.