Due to the poor site conditions of mountainous orchard monorail transporter, it is difficult to obtain, feedback and centralized management of real-time operation information. In order to monitor the proceeding of deliveries by 7SYDD-200 mountainous orchard monorail transporter and reasonably dispatch transportation equipment, the detection method of citrus fruit basket carried by the transporter is established based on the improved YOLOv5s model. Images of the citrus fruit baskets carried by the transporter were collected by the RGB camera of HSK-200 under the natural light environment of mountainous orchards. The YOLOv5s model was established and optimized, which was deployed into the embedded device to detect the states of “empty fruit basket”, “citrus” and “full fruit basket” during the loading process. convolutional block attention module(CBAM) is introduced into neck network of the model to strengthen the ability to extract semantic information and solve the problem of “double labels” in the detection process. The sparse scale factor of the batch normalization(BN) layer was used to measure the representation ability of each channel of the model. The channels with weak representation ability were pruned and compressed to overcome the problem of slow detection speed of the model based on YOLOv5s. The multi-scale training strategy is used to fine-tune the model to improve the detection accuracy. The test results show that the mean average precision of the improved detection method is 93.3% on the fruit dataset. The floating point operation and the size of the improved models were 9.9 G and 3.5 M, respectively, which were 60.3% and 21.3% higher than that of YOLOv5s. The detection speed of the improved model was 78 ms/img, when it was deployed into the Jetson Nano embedded platform.