Abstract: The filling and throwing system has a major impact on the the efficiency of the silage harvester. Aiming at the material throwing process of silage harvester, an automatic throwing and filling system for silage harvester was designed in this study to realize the adaptive filling of the car-following material box.The study proposes a filling rule firstly through the definition of the desired drop point and the actual drop point. At the same time, in order to further realize the accurate detection of rotating targets, an improved YOLOv5s rotating target detection algorithm Rotation-YOLOv5 (R-YOLOv5) was proposed which based on the principle of machine vision. R-YOLOv5 could control the movement of the related parts of the throwing cylinder through the calculation of the feedback of the expected falling point information and the actual falling point information and achieve the self-adaptive control of the throwing cylinder. In order to carry out the optimization of the network structure of the baseline algorithm, a new rotation angle prediction channel was added on the basis of the structure of the Head part of the original YOLOv5 to achieve the prediction of rotating carriages. The virtual environment was built based on Python3.9, and pytorch1.6 was chosen as the framework to realize the training of the R-YOLOv5 rotating target detection model in the study on the pytorch deep learning framework. When the number of training rounds reached 60, the mAP values were all stable at 0.97, the system was stable, and the improved R-YOLOv5 algorithm recognized the result as a rectangle with a rotation angle. The recognition range could be synchronized with the angle of the carriages, and the training result is highly compatible with the carriages to achieve better recognition. In addition, the study set up the throwing control method based on the distance between the actual drop position N and the expected drop position P. The corresponding control flow was developed, and a rotating carriage identification test and a field test were carried out. The test results show that when performing rotating compartment recognition, the improved R-YOLOv5 detects compartments at different rotating angles with an average of more than 97% realism, and the detection effect was good; When the projectile barrel rotates in the range of -60° ～ 60° and the angular velocity was less than 15°/s, the average accuracy of R-YOLOv5 target detection algorithm was higher than 90%; The more material outflowed in unit time, the better recognition effect, the more accurate the actual drop position output, and the throwing cylinder material outflow in unit time was mainly related to the silage harvester cutter rotational speed and harvesting walking speed, it was determined that when the cutter rotational speed of 1200 r/min, forward speed of 6 km/h, the amount of material out of the unit between the Ⅳ level or more, the correct rate of 85% or more to meet the harvesting requirements; The visual information inputted by Arduino controller along with R-YOLOv5 could realized the control of the motion of the throwing cylinder, and with 0.533 cm/px as the ratio of the actual distance to the image pixels, the average error between the theoretical motion trajectory of the throwing cylinder of the silage harvester and the actual motion trajectory was kept within 4%, which meets the design requirements. The research results could provide a reference for the development of self-propelled silage harvester adaptive throwing and filling system.