Accurate and timely monitoring of leaf nitrogen content (LNC) is crucial for citrus tree growth control and precision fertilization. Unmanned aerial vehicle (UAV) remote sensing can provide stronger support for rapid, flexible, large-scale monitoring of LNC distribution in orchards compared with traditional LNC chemical detection methods. However, time-consuming and high-cost sample collection leads to a limited number of samples for constructing remote sensing LNC estimation models, thereby limiting the accuracy of LNC estimation. We use the semi-supervised twin neural network regression (STNNR) model to make full use of a considerable portion of unlabeled and labeled data to extend the sample data for training the model and effectively improve the accuracy of nitrogen content estimation. First, multiple spectral indices are constructed using high-resolution multispectral UAV imagery, and the relationships of these spectral indices with the LNC are analyzed to select the spectral indices sensitive to changes in the LNC. Then, the unlabeled data are used for model training under the loop consistency condition. Three different loss functions are employed for training the STNNR model on the labeled and unlabeled data. Finally, the STNNR model is evaluated through comparison experiments with other supervised learning models. Results show that the STNNR model has a high LNC estimation accuracy with a root mean square error of only 0.59 g/kg and an R2 of 0.71. The proposed method is capable of effectively obtaining LNC in the whole orchard with limited samples, thus providing support for the scientific management of citrus planting.