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Abstract. Seed characterization and analysis is an important component in plant breeding applications from seed count, shape and weight estimation, to crop yield prediction. Recent research in seed characterization and analysis has adopted high-throughput image-based approaches in which seed detection is the first step. However, traditional seed detection approaches usually require the assistance of specialized hardware such as seed holder and ambient lighting, as well as intensive human support such as seed separation and parameter tuning. To address these challenges, we conducted a baseline study of seed detection using a Convolutional Neural Network based detector (RetinaNet with a backbone network of MobileNet), which is scalable with minimum hardware and human investments. We tested our approach on a benchmark image set, where we collected images of seed samples with different seed sizes and shapes including canola, camelina, and soybean seeds which comprise approximately 5,000 seeds at different density levels. Experimental results of our approach indicated superior detection accuracies for all seed types (COCO mAP@0.5=95.3%) while suggesting improvements on the detection of small seeds such as camelina seeds (COCO mAR@10=37.0%).