Molecular mechanism of in vitro rooting in conifer is not fully understood. After establishment of a regeneration procedure in eastern white pine (Pinus strobus L.) using mature embryos as explants to induce shoot formation on medium containing 3 μM IAA, 6 μM BA and 6 μM TDZ and induce root formation on medium containing 0.001-0.05 μM IAA, 0.001–0.05 μM IBA, 0.001–0.05 μM TDZ, we have investigated the changes of polyamine content and the activities of antioxidant enzymes during in vitro rooting in P. strobus. Our results demonstrated that putrescine (Put), spermidine (Spd), and spermine (Spm) did not increase in P. strobus during the first week of rooting on medium supplemented with 0.01 μM indole-3-acetic acid (IAA), whereas the levels of Put, Spd, and Spm increased during the 1st–3rd week of culture on medium with IAA, and then decreased on medium with IAA. No such a change in Put, Spd, and Spm was observed on medium without IAA. Measurement of antioxidant enzyme activity demonstrated that the activities of polyphenol oxidase, catalase, and peroxidase slightly increased in the first week of culture and reached to the highest peak in the 3rd–5th week of culture. Quantitative RT-PCR results indicated that miR160 was increased on the 7th day, miR162, miR397, and miR408 was increased from the 21th to 35th day, miR857 was increased on the 35th day, and miR827 was increased on the 49th day. These results demonstrated that enhanced polyamine biosynthesis, antioxidant enzyme activity, and microRNAs are correlated with the root induction and formation in P. strobus.

Water is a crucial element for the growth of watermelon plants, making rapid and non-destructive monitoring of plant water content vital for precision irrigation in watermelon farming. While previous research has demonstrated the sensitivity of short-wave infrared (SWIR) bands to plant water content, their high costs limit widespread application. In contrast, visible and near-infrared (VNIR) spectral instruments offer significant advantages in terms of affordability, compactness, and spectral resolution. However, their potential for predicting the leaf water content (LWC) of watermelon plants has yet to be fully investigated. This study aims to assess the efficacy of hyperspectral reflectance measured with VNIR spectral instruments in estimating the LWC of watermelon plants at various leaf layers. Hyperspectral reflectance data (350−1100 nm) were collected from three leaf layers (upper, middle, and lower) under various drought treatments. Models for estimating LWC were developed using both spectral indices and full wavelength data. The results indicated that the middle leaf layer was the most effective for estimating LWC, and using full wavelength data achieved higher accuracy in LWC estimation. Furthermore, compared to the simple regression model, the AdaBoost-based machine learning model demonstrated superior performance, achieving an R² of 0.9636 in estimating LWC through five-fold cross-validation, which indicates high predictive accuracy. Ensemble learning significantly outperforms traditional methods, providing a substantial improvement in model accuracy. The findings offer important technical assistance for the spectral monitoring of LWC and precision irrigation in watermelon cultivation.