The estimation of water stress is critical for the reliable production of high-quality fruits cultivated using the tacit knowledge of expert farmers. Multimodal deep neural network has achieved success in the estimation of stem diameter variations as a water stress index, calculated from leaf wilting and environmental data. However, these studies have not addressed the specific role of leaf wilting in the estimation. Revealing the role of leaf wilting not only ensures the reliability of the estimation model but also provides an opportunity for improving the estimation method. In this paper, we investigated the relationships between leaf wilting and stem diameter variations without resorting to black-box approaches such as deep neural network. To clarify the role of leaf wilting, this study uses cross-correlation analysis to analyze the time lag correlation between leaf wilting, quantified by optical flow, and stem diameter variations as a water stress index. The analysis showed that leaf wilting had a significant time lag correlation with short-term stem diameter variations, which were water stress responses in plants. As the results were consistent with known plant water transport mechanisms, it was suggested that leaf wilting quantified by optical flow can explain short-term stem diameter variations.
Hygroscopic properties of thin dead outer bark layers strongly influence stem diameter variations on short and long time scales in Scots pine (Pinus sylvestris L.)