Integrating sensor fusion with machine learning for comprehensive assessment of phenotypic traits and drought response in poplar species.

Increased drought frequency and severity in a warming climate threaten the health and stability of forest ecosystems, influencing the structure and functioning of forests while having far-reaching implications for global carbon storage and climate regulation. To effectively address the challenges posed by drought, it is imperative to monitor and assess the degree of drought stress in trees in a timely and accurate manner. In this study, a gradient drought stress experiment was conducted with poplar as the research object, and multimodal data were collected for subsequent analysis. A machine learning-based poplar drought monitoring model was constructed, thereby enabling the monitoring of drought severity and duration in poplar trees. Four data processing methods, namely data decomposition, data layer fusion, feature layer fusion and decision layer fusion, were employed to comprehensively evaluate poplar drought monitoring. Additionally, the potential of new phenotypic features obtained by different data processing methods for poplar drought monitoring was discussed. The results demonstrate that the optimal machine learning poplar drought monitoring model, constructed under feature layer fusion, exhibits the best performance, with average accuracy, average precision, average recall and average F1 score reaching 0.85, 0.86, 0.85 and 0.85, respectively. Conversely, the novel phenotypic features derived through data decomposition and data layer fusion methods as supplementary features did not further augment the model precision. This indicates that the feature layer fusion approach has clear advantages in drought monitoring. This research offers a robust theoretical foundation and practical guidance for future tree health monitoring and drought response assessment.