Corn Seed Screening System Based on Machine Learning Research on Key Technologies

Food security and seed security are core components of national security and the foundation for ensuring people's livelihood and promoting the steady progress of the country; Corn is an important food crop and is widely grown around the world. It is not only used for human and animal consumption, but also an important industrial raw material. The traditional screening of corn and its seeds mainly relies on manual sorting based on experience, using only simple tools, which is not only inefficient and inaccurate, but also has significant limitations. In response to these problems, this study presents a corn seed screening scheme based on machine learning. First, images of corn seeds were obtained and processed using MATLAB software. Then, the HOG features and labels of the images were extracted to build a database and SVM was trained to determine the quality of the seeds. Finally, parallel robots were used to sort the seeds based on the discrimination results, eliminating bad seeds and retaining good seeds. Experiments have shown that the screening system can accurately identify the quality of corn seeds, which can provide some reference for subsequent research and practical applications.