Weed recognition is an essential step for automatic weed control systems. Identifying weeds enables targeted control measures to be implemented, minimizing the use of chemicals and reducing the impact on the environment. Deep learning-based approaches proved to be e↵ective for addressing various complex classification problems. However, to benefit fully from their capabilities, large amounts of labeled data are required, which represents a limitation for agricultural applications, consequence of the tedious and time-consuming process of data labeling. Conversely, unlabeled data could be acquired in large quantities, with relative ease. Hence, our aim is to develop robust and precise deep learning models, to carry-out the recognition and identification of weed species, using both types of data. To this end, we propose a method, that adopts the semi-supervised learning paradigm, to optimally combine labeled and unlabeled data. The method is based on a new deep neural networks architecture, which consists of a modernized convolutional encoder belonging to the family ConvNeXt and a thoroughly designed deep decoder network. This architecture, enables a successful integration of consistency regularization. The conducted experiments on DeepWeeds and 4-Weeds, showed that the semi-supervised models trained through our proposed method provide a stable and high classification performance, compared to other state-of-the-art deep learning models, which were a↵ected negatively by the amount of labeled data available, and the presence of noise during inference. Furthermore, the e↵ectiveness of the proposed method was demonstrated in comparison to other semi-supervised learning methods. The results obtained demonstrate the benefits of adopting the semi-supervised learning paradigm, especially in scenarios with very limited labeled data.