Corn canopy organs detection is critical in obtaining high-throughput phenotypic data. Accurate identification of each organ can provide a reliable data source for canopy phenotype determination, which has significant theoretical and practical value for corn variety breeding, cultivation management, and high-quality and high-yielding production. Due to the difficulty in quickly identifying corn canopy organs in the natural environment of the field, it is challenging to obtain high-throughput phenotypic data. Therefore, this paper proposed a method for corn canopy organs detection based on an improved network model (DBi-YOLOv8). Firstly, the Raspberry Pi 4B was used as the sensor control center to construct an embedded system for corn canopy image acquisition and collected 987 images of corn plants. Secondly, the improved deformable convolution and Bi-level routing attention were embedded into the backbone and neck structures of the YOLOv8 network. With training the improved network, a corn canopy detection model was obtained, which enabled the rapid detection of corn canopy organs. Finally, the LTNS algorithm and TBC algorithm were proposed for counting of the number of leaves, ears, and tassels. On the testing set data, the detection performance of the model was analyzed through different evaluation metrics. The results showed that the mAP and FPS of the detection model were 89.4% and 65.3, which increased by 12% and 0.6 compared to the original model. In addition, both algorithms have high reliability, with the coefficient of determination R² for counting crown leaves, ears, and tassel branches being 0.9336, 0.8149, and 0.917, respectively. This achievement proposed an accurate, non-destructive, and fast corn canopy organs detection model, providing reliable technical support for quantifying various traits of corn plants, field crop growth monitoring, and elite variety breeding.