To address the current issues of low intelligence and accuracy in seed-sorting devices, an intelligent seed sorter was developed in this study using machine-vision technology and the lightweight YOLOv5n. The machine consisted of a transmission system, feeding system, image acquisition system, and seed screening system. A lightweight YOLOv5n model, FS-YOLOv5n, was trained using 4756 images, incorporating FasterNet, Local Convolution (PConv), and a squeeze-and-excitation (SE) attention mechanism to improve feature extraction efficiency, detection accuracy, and reduce redundancy. Taking ‘Zhengdan 958’ corn seeds as the research object, a quality identification and seed sorting test was conducted on six test groups (each consisting of 1000 seeds) using the FS-YOLOv5n model. Following lightweight improvements, the machine showed an 81% reduction in parameters and floating-point operations compared to baseline models. The intelligent seed sorter achieved an average sorting rate of 90.76%, effectively satisfying the seed-sorting requirements.