The improvement of crop remote sensing identification accuracy is a key driving force for the leapfrog development of precision agriculture and smart agriculture. The accuracy of crop remote sensing identification depends on three elements: samples, image features and classification methods. Aiming to reduce the classification error caused by the bottleneck of sample data, the accuracy of crop remote sensing identification by jointly enhancing the sample quantity and quality control was improved. Taking the Wulanbuhe Irrigation District in the Hetao Irrigation Area as the study area, the time-series image of NDVI during the crop growth period in 2023 was constructed. Combined with the NDVI time-series characteristics of the crops, sampling was conducted on the image to expand the number of crop samples, and then the unqualified samples were screened and removed to achieve sample quality control. A total of 801 pixels of field samples (pre-expansion samples), 17917 pixels of image samples (expanded samples), and 18718 pixels of total samples (post-expansion samples) were selected. Four machine learning classifiers were used to compare the crop classification effects before and after sample expansion. The results showed that the classification accuracy of crops was significantly improved after sample expansion, with the overall classification accuracy increased by approximately 5 percentage points and the Kappa coefficient rose by about 0.05. Among them, the classification accuracy of RF and NNC was relatively high, while that of CART and SVM was slightly lower. The crop remote sensing recognition was carried out after sample expansion by using CNN and LSTM deep learning models. The results showed that the classification accuracy of CNN and LSTM was higher than that of RF and NNC, which had relatively high classification accuracy.