To meet demand for agriculture products, researchers have recently focused on precision agriculture to increase crop production with less input. Crop detection based on computer vision with unmanned aerial vehicle (UAV)-acquired images plays a vital role in precision agriculture. In recent years, machine learning has been successfully applied in image processing for classification, detection, and segmentation. Accordingly, the aim of this study is to detect rice seedlings in paddy fields using transfer learning from model Faster R-CNN. This study relies on a significant UAV image dataset to build a model to detect tiny rice seedlings. The model was also measured with three additional datasets acquired on different dates to evaluate model applicability with various imaging conditions. The results demonstrate that the adoption of transfer learning allows for rapid establishment of object detection applications with promising performance.