Design of a Non-Destructive Seed Counting Instrument for Rapeseed Pods Based on Transmission Imaging

Pod counting of rapeseed is a critical step in breeding, cultivation, and agricultural machinery research. Currently, this process relies entirely on manual labor, which is both labor-intensive and inefficient. This study aims to develop a semi-automatic counting instrument based on transmission image processing and proposes a new algorithm for processing transmission images of pods to achieve non-destructive, accurate, and rapid determination of the seed count per pod. Initially, the U-NET network was used to segment and remove the stem and beak from the pod image; subsequently, adaptive contrast enhancement was applied to adjust the contrast of the G-channel image of the pod to an appropriate range, effectively eliminating the influence of different varieties and maturity levels on the translucency of the pod skin. After enhancing the contrast, the Sauvola algorithm was employed for threshold segmentation to remove the pod skin, followed by thinning and dilation of the binary image to extract and remove the central ridge lines, detecting the number and area of connected domains. Finally, the seed count was determined based on the ratio of each connected domain’s area to the mean area of all connected domains. A transmission imaging device that mimics the human eye’s method of counting seeds was designed, incorporating an LED transmission light source, photoelectric switch-triggered imaging slot, an industrial camera, and an integrated packaging frame. Human–machine interaction software based on PyQt5 was developed, integrating functions such as communication between upper and lower machines, image acquisition, storage, and processing. Operators simply need to place the pod in an upright position into the imaging device, where its transmission image will be automatically captured and processed. The results are displayed on a touchscreen and stored in Excel spreadsheets. The experimental results show that the instrument is accurate, user-friendly, and significantly reduces labor intensity. For various varieties of rapeseed pods, the seed counting accuracy reached 97.2% with a throughput of 372 pods/h, both of which are significantly better than manual counting and have considerable potential for practical applications.