In order to conduct rational management of watering lettuce, the model of detecting lettuce leaves' moisture was built. First of all, the hyperspectral images of lettuce leaves were acquired and simultaneously the moisture proportions of leaves were measured. Meanwhile, hyperspectral images were analyzed and the characteristic bands of lettuce leaves' moisture were found. Then the images in characteristic bands were processed and the image features of lettuce leaves' moisture were computed. The image features highly relevant to moisture were obtained through correlation analysis. Furthermore, due to the possible correlation among image features, the principal components of the images were extracted by principal components analysis and were used as BP neural network's inputs to establish PCA-ANN model. At the same time, other models were constructed by using BP neural network and traditional MLR (multiple liner regression) method respectively. Prediction examinations of the three models were made based on the same sample data. The experimental results show that the average prediction error of PCA-ANN prediction model of tillering stage reaches 9.323% which is improved compared with BP-ANN and MLR prediction models.