Abstract: China is the largest fruit producer and consumer in the world, while most orchards in the south of China are located in hilly areas. The ropeways can be expected for transporting orchards in mountainous areas. But the harsh working environment can often lead to machine failures in recent years. Therefore, it is of great significance to investigate and solve the problems at the initial stage of failures. Generally, the conventional bearing fault diagnosis system includes five links: signal acquisition, feature extraction, state identification, diagnosis analysis. and decision intervention. Deep learning is widely applied for bearing fault detection in recent years. Traditional machine learning also needs to manually extract the fault features at present, depending mainly on the deep professional knowledge. However, it is high demand for the high performance of fault diagnosis, particularly for simple structures and less calculation during feature extraction. This study aims to realize the fault diagnosis of ropeway drive system bearings in the mountain orchards with poor network environment, in order to ensure the safe and stable operation of ropeway in the mountain orchards. A one-dimensional end-to-end lightweight CNN detection, 1D-MRL-CNN was established to directly detect the one-dimensional vibration signals for the mountain ropeways. Specifically, the new model was established using residual structure and depth separable convolution. The depth separable convolution was applied to greatly reduce the parameter and calculation amount of the improved model. In addition, the residual structure was applied to make up for the accuracy loss caused by depth separable convolution. The parameter amount and complexity of the model were reduced significantly while ensuring the detection accuracy. The stem block and BN layer were then introduced to improve the robustness and generalization ability of the new model suitable for the variable load working state of the ropeway. Finally, the hard_swish activation function was also adopted in the model. The channel attention mechanism was added to the last layer of the main module, in order to improve the feature extraction ability of the network model. Two datasets (Paderborn University and Case Western Reserve University) were used to verify the comprehensive performance, stability under variable load and anti-noise interference performance. The Paderborn University dataset showed that the fault classification accuracy of the improved model was 99.43%, which was 0.56, 0.99, and 1.23 percentage points higher than those of the one-dimensional classical CNN, similar optimal network, and the lightweight CNN architecture optimal network. The parameters and floating-point calculations were 83.44 kb and 0.20 M, which were 2.19%, 1.18%, 0.75%, and 0.83% of the one-dimensional classical CNN classification network architectures (such as Resnet18, Resnet34, Resnet50 and VGG16), 6.19% and 30.40% of the same type of networks 1D-Lenet5 and 1D-Inception, while 4.2%, 2.07%, 2.84%, 3.32% and 5.16% of the one-dimensional lightweight CNN architecture MobileNetV1, MobileNetV2, MobileNetV3-Large, ShuffleNetV1 and EfficientNet-1. In addition, the Case Western Reserve University dataset showed that the average accuracy rate of the improved model was 96.70% in six load scenarios, which was 9.1, 4.7, and 10.5 percentage points higher than those of Resnet18, WDCNN, and MobileNetV3-Large, respectively. The average recognition accuracy was 99.14% under four noise conditions, which was 4.74, 1.24, and 5.51 percentage points higher than those of Resnet18, WDCNN and MobileNetV3-Large, respectively. Finally, the fault classification of the improved model under actual working conditions was verified by the established ropeway dataset, where only 2 fault samples out of 1 400 samples were predicted incorrectly. The new network model was suitable for the bearing fault detection of mountain orchard transport ropeway, due to the small parameters, high accuracy and robustness under variable load and noisy working conditions.