Development of a cut rose longevity prediction model using thermography and machine learning

Abstract

To predict the longevity of cut roses (Rosa hybrida L.), we used thermal image analysis on ‘3D’, ‘Kensington Garden’, and ‘Hera’ rose cultivars. At blooming stage, the temperatures of leaves and petals were similar to or slightly lower than the air temperature. When the temperature of leaves and petals increased by 2°C compared to the air temperature, no symptoms such as senescence were visible in the leaves and petals. However, three days after the temperature increase, significant visual senescence was observed and the temperature of leaves and petals decreased back to that of the air temperature. Based on this data, we identified three different stages of cut roses: (1) the blooming stage, (2) the last stage with no visual senescence, and (3) the stage with significant visual senescence. To embody a longevity prediction model for cut roses, the temperature difference between the leaf of ‘Hera’ and the air were chosen for the practice data for the model. After the machine learning process, a model with 100% accuracy was obtained. According to the model, when the temperature of a cut rose leaf is lower than the surrounding air, it is undergoing its blooming stage, while when it is higher it is undergoing the senescence stage. Using logistic regression with machine learning, a value of 1 indicates the senescence stage and a value of 0 indicates the blooming stage. This study suggests that current smart farming techniques used for cut roses are first-generation level, which means there are limitations in environmental control when using a remote control system and partially automatic system. To upgrade this process and overcome these limitations, an optimal model to predict the longevity of a cut rose is needed.