Rapid detection of imperfect wheat grains based on deep learning technique

The detection and identification of imperfect wheat grains are of great significance in evaluating their quality. Manual inspection and separation of imperfect grains in wheat are time-consuming and expensive. Therefore, there is a need for a fast, automated, and accurate method to detect imperfect grains in wheat. In this study, we created an image acquisition platform to capture images of wheat grains. Each image was labeled as either spotted, sprouted, moldy, broken, or perfect grains. To balance calculation efficiency and prediction accuracy, it is necessary to identify a suitable model. Thus, we applied several mainstream deep-learning algorithms, including ResNet50, ResNet50 with an attention module, MobileNetv1, MobileNetv2, MobileNetv3-small, and MobileNetv3-large, to construct a model suitable for practical application. It was found that the MobileNetV3-Small model achieved a good balance between computational efficiency and prediction accuracy. The model based on the MobileNetV3-Small architecture achieved high accuracy, recall rate, and F1-score, all exceeding 96%. Although the MobileNetV3-Small model's accuracy is slightly lower than that of the ResNet50 model with an added attention mechanism, it significantly reduces computational costs, improves detection speed, and cuts prediction time by 50%. Compared with other models, the MobileNetv3-small-based model is more suitable for practical applications due to its advantages of high speed, high precision, and stable prediction performance. This study can provide technical guidance for intelligent recognition and detection of wheat grains in the future.