Moisture content is a critical factor influencing the growth and development of strawberries, and it holds significant importance for their cultivation. Traditional methods of moisture measurement, although precise, are cumbersome and destructive. Hyperspectral Imaging (HSI) has emerged as an ideal technique for plant moisture detection due to its efficiency, non-destructive nature, and multi-attribute detection capabilities. However, the large volume and redundancy of HSI data present challenges. While deep learning methods can extract deep features from the data, their reliance on large-scale annotated datasets limits their application. To address this issue, our study introduces a Broad Learning System (BLS) to solve the training problem with small sample sizes. It proposes a BLS-based method for detecting moisture content in strawberry leaves. The study first prepared samples of healthy and drought-stressed strawberry leaves, obtaining their hyperspectral images and moisture content data. By analyzing three hyperparameter tuning methods and four preprocessing algorithms, a BLS moisture determination model was constructed and its performance was evaluated against comparative models, including Partial Least Squares Regression (PLSR), Support Vector Machine Regression (SVR), Gradient Boosting Decision Tree Regression (GBDTR), and Residual Network (ResNet). The results showed that the BLS model achieved a coefficient of determination ($R_{\text{p}}^2$) of 0.797 4 and a Root Mean Square Error (RMSE) of 0.004 5 on the test set, outperforming the other models and exceeding the ResNet model by 0.039 4, demonstrating its superior generalization ability and prediction accuracy. Additionally, the optimal model was used to visualize the moisture content in strawberry leaves by generating false-color maps, providing an intuitive display of the leaves' moisture status. The findings suggest that the BLS model is suitable for analyzing hyperspectral data and detecting moisture content in strawberry leaves using small samples, providing a theoretical basis for the online detection of moisture content instrawberry leaves.