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With the development of precision agriculture, the demand on rapidly obtaining the area and geographical distribution of greenhouses, plastic-mulched landcover is increased. However, using the interpretation method for satellite remote sensing images to process unmanned aerial vehicle (UAV) images is not ideal, due to the complex feature extraction, low recognition accuracy, long processing time and so on. To circumvent this issue, a UAV aerial monitoring method was proposed based on deep learning for greenhouses and plastic-mulched landcover monitoring. The six-rotor UAV equipped with Sony NEX-5k camera captured aerial photographs in the Wangyefu town of Chifeng City. The 558 UAV images were orthographically corrected and stitched. The five fully convolutional network (FCN) variants, i.e. the FCN-32s, FCN-16s, FCN-8s, FCN-4s and FCN-2s models were built by multi-scale fusion. The modes were trained end-to-end by the stochastic gradient descent algorithm with momentum. The features were extracted and learned from the photographs automatically. The FCN models were compared with two economic softwares, i.e. the pixel-based classification method of ENVI and the object-oriented classification method of eCognition. The results showed that the FCN-4s was the best model on the identification of greenhouses and plastic-mulched landcover. The average overall accuracy of test area was 97%, while that of pixel-based classification method and the object-oriented classification method was 74.1% and 81.78%, respectively. The average runtime of the FCN-4s was 16.85s, which was 0.06% and 5.62% of those of pixel-based classification method and the object-oriented classification method, respectively. The proposed method demonstrated high recognition accuracy and fast speed, which can meet the demand on UAV monitoring of facilities agriculture.