Improving crop image recognition performance using pseudolabels

In crop image recognition, when faced with a large quantity of unlabeled data, the traditional manual labeling method requires a large amount of human and material resources. To solve this problem, this study proposes an image recognition method based on a pseudolabeling technique. First, the data are divided into labeled and unlabeled data. The initial network model is trained on labeled data. Then, pseudolabeling of the unlabeled data is predicted, and only the data that satisfy the confidence threshold are regarded as valid pseudolabeling. To convert the unlabeled data into supervised training data, the two types of data are mixed. The training is terminated when the number of remaining unlabeled data satisfies the end condition and when the fivefold cross-validation method is used to evaluate model performance. Compared with the traditional semisupervised method, the experimental method is simpler and more applicable. Experiments were conducted on rice growth stage recognition and crop weed seedling recognition tasks. The results showed that the proposed method achieved 99.17% accuracy in rice growth stage recognition and a high AUC value of 99.93% in crop weed seedling recognition, which demonstrated excellent performance. Compared with the traditional model, this method not only improves in accuracy but also has better stability and wider applicability and is expected to provide an efficient, accurate and scalable solution for crop image recognition.