Leaf area index (LAI) is an important parameter for evaluating crop growth, rapid, accurate and low-cost acquisition of LAI has great significance for guiding crop field management. To achieve low-cost acquisition of LAI for multiple crops, the general LAI prediction models were built based on multi-source information and deep learning. The field experiments were carried out in six growth periods of soybean, wheat, peanut, and maize to obtain multi-source information for modeling. In addition, relevant one-dimensional data were collected, including UAV flight attitude angles, image capture height, crop growth states and environmental illumination. With the help of the excellent image and data processing ability of deep learning, the LAI prediction models were built accurately based on complex input information. Considering that the one-dimensional data also participated in the training process of the models, the combined network architecture was adopted in the design of the models. Based on extracting image depth features by the convolutional neural network (CNN) algorithm, the LightGBM (Light Gradient Boosting Machine Method) algorithm was added to realize the final prediction of crop LAI by combining image features and one-dimensional data. Four common network structures, VGG19, ResNet50, Inception V3 and DenseNet201, were used in four CNN models. In order to better illustrate the ability of CNN models to extract image features, the crop classification results of the four models under different image inputs were analyzed. The results showed that the classification accuracyof the four models with inputs using multispectral images was better than that of inputs using visible images only. The classification accuracy of the models based on Inception V3 and DenseNet201 was more than 99%, which proved the validity of the CNN model in extracting multispectral image features. Taking the image features as inputs of the LightGBM model to predict LAI, the results were shownthat the maximum R2 betweenthe measured value and the predicted value of LAI is 0.819 2. After one-dimensional data were addedtothe inputs, the R2 of the models can reach more than 0.9, which indicates that multi-source information inputs play an important role in improving the accuracy of the LAI prediction models. The models established in this study can predict LAI for multiple crops without the complex processing for multispectral images. Therefore, this study can realize the low-cost and rapid prediction of LAI and have high LAI prediction accuracy.