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Ideal wheat grain quality detection systems aim for rapid and precise recognition of flaws. Computer vision and machine learning have been widely studied as potential alternatives to human inspection. Numerous deep learning-based detection and classification methods have been proposed in the last few years. However, these methods suffer from poor performance and extremely long time consumption due to the weak feature extraction ability and high computational overhead. To solve these challenges, we present the first comprehensive study and analysis of wheat grain detection using improved YOLOv5. Specifically, we design a machine vision system and construct a Wheat Grain Detection Benchmark (WGDB) including 1746 images with 7844 bounding boxes, all of which have been independently classified. Utilizing this dataset, we conduct a comprehensive study of the most advanced objection detection methods. In addition, we propose a Wheat Grain Detection Network (called WGNet) trained on this benchmark as a baseline, aiming to solve the degradation issues by employing sparse network pruning and a hybrid attention module. Extensive experiments demonstrate the limitations of existing methods and the improved performance of our method, which achieves state-of-the-art precision with the fastest inference speed. The constructed benchmark and the improved experiments shed light on future research in wheat grain detection. The dataset and code will be available at WGNet. • We construct a large-scale wheat grain detection benchmark. • We conduct a comprehensive study with the constructed dataset. • We propose a light-weight Wheat Grain Detection network by employing sparse network pruning and a hybrid attention module. [ABSTRACT FROM AUTHOR]