Blast disease has been one of the most serious threats to rice production. However, the conventional evaluation of rice blast resistance cannot fully meet the large-scale production in recent years. In this study, an efficient evaluation was proposed for rice neck blast resistance via combining unmanned aerial vehicle (UAV) low-altitude remote sensing with the YOLOv7 model. 2 565 images of rice neck blast were collected using UAV and then divided into small-sized images (≤1240×1240 pixels) in the annotated area. The small-sized images were further subjected to a random combination of five operations, including rotation, scaling, translation, cropping, and changing contrast. The images with low resolution were removed after data cleaning. Finally, the dataset size was expanded with better diversity. The squeeze extinction attention and deformable convolution were introduced into the YOLOv7 model, in order to capture the fine-grain features of the rice neck blast disease spot. YOLOv7_Neckblast model was established for the rice neck blast detection. The number of affected ears was obtained for 15 rice varieties. The incidence rate was calculated for the disease grade of rice neck blast. Among them, 4, 4, 3, 5, 7, and 9 rice varieties of grades 1, 3, 5, and 7, as well as 9, 2, and 2 were assessed by YOLOv7_Neckblast, respectively. At the intersection over the union (IoU) threshold of 0.5, the mean average precision (mAP) of YOLOv7_Neckblast for rice neck blast was 66.4%, which was 4.0, 6.4, and 5.8 percentage points higher than that of the original YOLOv7, FCOS (fully convolutional one-stage object detection), and RetinaNet models, respectively. The recall rate was 75%, which was 4.0, 8.0, and 22.0 percentage points higher than those of the three models, respectively. The F1 score was 71%, which was 4.0, 5.0, and 12.0 percentage points higher than those of the three models, respectively. The training YOLOv7_Neckblast was relatively stable with the low floating-point operations per second (FLOP) under the same training conditions. The loss values remained stable with about 0.019 and 0.020 at the end of training, which was lower than those of FCOS and RetinaNet models. Furthermore, the molecular-assisted marker selection (MAS) showed that the contribution of the Pit and Pib genes to the resistance to the neck blast was 100.0% and 57.14%, respectively. The rice varieties carrying the Pit and Pib genes also exhibited a stronger resistance to disease. In addition, YOLOv7_Neckblast achieved an average accuracy of 86.67% in evaluating 15 rice varieties' resistance, compared with the actual resistance level. The low-altitude UAV remote sensing coupled with machine learning can be used to evaluate the resistance to rice neck blast for rice breeding.