In the quality analysis, alternatives for seed imaging are a quick response to something challenging and laborious. However, machine learning techniques emerge as an alternative for prediction and classification by image processing, with efficiency and speed of results under control of quality at the post-seed harvest stages. The objective was to report the insertion of image processing with artificial intelligence in the seed area. Various machine learning models are purposes of the investigation to improve the responses of laborious and data-intensive targets. Deep learning studies in seeds offer promising results and have great potential.