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It is of great significance to accurately obtain the crop yield distribution information of farmland, which can provide decision-making basis for fine farmland management. However, due to the serious grain falling in the harvester elevator, the traditional photoelectric sensor is prone to triggering by mistake, and the grain falling is random, so the error is difficult to correct and eliminate. In order to improve the accuracy of yield monitoring, a method of grain yield measurement based on monocular vision was developed, which can be used in the combine harvester with scraper elevator. Firstly, a more accurate geometric model of grain heap on the scraper was established according to the real images of grain pile in elevator. Then, a volume measurement method of the grain heap was developed based on vision measurement and image processing technology. Under the illumination of the auxiliary light source, the image of the scraper and grain heap in grain elevator was collected by an industrial camera. The neighborhood differentiation-based method was put forward to extract the region of interest of the image, and then the Otsu method and morphological processing were used to accurately segment the grain piles from the background. According to the camera imaging model, the actual side area of the grain pile in the world coordinate system was calculated, and the grain volume was obtained through the geometric model of the grain pile. Finally, the volume of grain pile on each scraper was accumulated to obtain the grain yield. To verify the effectiveness of the proposed method, a grain yield measurement system based on monocular vision was built, and experiments were carried out on the elevator experiment bench. The results showed that the relative error measured by the proposed method was between -4.08% and 3.41% at different elevator speeds, which can meet the accuracy requirements of grain yield monitoring for the combine harvester.