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In this manuscript, a deep learning approach is used to research wheat seed variety identification and a fast and efficient wheat seed variety identification method (IRB-5-CA Net) is proposed based on the characteristics of wheat seeds and a self-constructed dataset, which provides ideas for wheat seed variety identification. Twenty-nine wheat varieties grown under natural light conditions were selected as the research objects, and a wheat seed dataset with the number of 4,385 seed photos was constructed by integrating sunny, cloudy, and rainy conditions with a blue hard paper as the background plate, and using a Nikon COOLPIX B700 digital camera for dataset collection. Based on the above self-constructed dataset, improving the MobileNetV2 model proposed a new lightweight method (IRB-5-CA Net) for wheat seed recognition. IRB-5-CA Net specific improvements are listed below: adding <inline-formula> <tex-math notation="LaTeX">$5\times 5$ </tex-math></inline-formula> convolution to the bottleneck without using the shortcut structure and adding the Coord Attention to the bottleneck with using the shortcut structure. After training IRB-5-CA Net on the self-built dataset, the average accuracy, average recall, and F1 values are 99.5&#x0025;, 99.6&#x0025;, and 99.6&#x0025;. The model improves the average accuracy by 6.8&#x0025;, 5.6&#x0025;, 5.8&#x0025;, and 8.3&#x0025; compared to MobileNetV2, ResNet34, Efficientnetv2&#x005F;s, and GoogLeNet. The IRB-5-CA Net was visualized using the pytorch&#x005F;grad&#x005F;cam method, in the output heat map, it can be seen that the model focuses more attention on wheat seeds, resulting in higher accuracy. Applying IRB-5-CA Net to other public datasets such as wheat seed disease, apple leaf disease, and AI Challenger 2018 crop disease detection, the average accuracy was 98.06&#x0025;, 96.15&#x0025;, and 94.02&#x0025;. This study provides a theoretical basis for seed variety identification, disease identification, and other crop disease identification in wheat.