Maize is one of the world’s most important crops, and maize leaf diseases can have a direct impact on maize yields. Although deep learning-based detection methods have been applied to maize leaf disease detection, it is difficult to guarantee detection accuracy when using a lightweight detection model. Considering the above problems, we propose a lightweight detection algorithm based on improved YOLOv5s. First, the Faster-C3 module is proposed to replace the original CSP module in YOLOv5s, to significantly reduce the number of parameters in the feature extraction process. Second, CoordConv and improved CARAFE are introduced into the neck network, to improve the refinement of location information during feature fusion and to refine richer semantic information in the downsampling process. Finally, the channel-wise knowledge distillation method is used in model training to improve the detection accuracy without increasing the number of model parameters. In a maize leaf disease detection dataset (containing five leaf diseases and a total of 12,957 images), our proposed algorithm had 15.5% less parameters than YOLOv5s, while the mAP(0.5) and mAP(0.5:0.95) were 3.8% and 1.5% higher, respectively. The experiments demonstrated the effectiveness of the method proposed in this study and provided theoretical and technical support for the automated detection of maize leaf diseases.

In practical applications, the intelligence of wheeled mobile robots is the trend of future development. Object detection for wheeled mobile robots requires not only the recognition of complex surroundings, but also the deployment of algorithms on resource-limited devices. However, the current state of basic vision technology is insufficient to meet demand. Based on this practical problem, in order to balance detection accuracy and detection efficiency, we propose an object detection algorithm based on a combination of improved YOLOv4 and improved GhostNet in this paper. Firstly, the backbone feature extraction network of original YOLOv4 is replaced with the trimmed GhostNet network. Secondly, enhanced feature extraction network in the YOLOv4, ordinary convolution is supplanted with a combination of depth-separable and ordinary convolution. Finally, the hyperparameter optimization was carried out. The experimental results show that the improved YOLOv4 network proposed in this paper has better object detection performance. Specifically, the precision, recall, F1, mAP (0.5) values, and mAP (0.75) values are 88.89%, 87.12%, 88.00%, 86.84%, and 50.91%, respectively. Although the mAP (0.5) value is only 2.23% less than the original YOLOv4, it is higher than YOLOv4_tiny, Eifficientdet-d0, YOLOv5n, and YOLOv5 compared to 29.34%, 28.99%, 20.36%, and 18.64%, respectively. In addition, it outperformed YOLOv4 in terms of mAP (0.75) value and precision, and its model size is only 42.5 MB, a reduction of 82.58% when compared to YOLOv4’s model size.

Maize is one of the most important crops globally, and accurate diagnosis of leaf diseases is crucial for ensuring increased yields. Despite the continuous progress in computer vision technology, detecting maize leaf diseases based on deep learning still relies on a large amount of manually labeled data, and the labeling process is time-consuming and labor-intensive. Moreover, the detectors currently used for identifying maize leaf diseases have relatively low accuracy in complex experimental fields. Therefore, the proposed Agronomic Teacher, an object detection algorithm that utilizes limited labeled and abundant unlabeled data, is applied to maize leaf disease recognition. In this work, a semi-supervised object detection framework is built based on a single-stage detector, integrating the Weighted Average Pseudo-labeling Assignment (WAP) strategy and AgroYOLO detector combining Agro-Backbone network with Agro-Neck network. The WAP strategy uses weight adjustments to set objectness and classification scores as evaluation criteria for pseudo-labels reliability assignment. Agro-Backbone network accurately extracts features of maize leaf diseases and obtains richer semantic information. Agro-Neck network enhances feature fusion by utilizing multi-layer features for collaborative combinations. The effectiveness of the proposed method is validated on the MaizeData and PascalVOC datasets at different annotation ratios. Compared to the baseline model, Agronomic Teacher leverages abundant unlabeled data to achieve a 6.5% increase in mAP (0.5) on the 30% labeled MaizeData. On the 30% labeled PascalVOC dataset, the mAP (0.5) improved by 8.2%, demonstrating the method’s potential for generalization.

Currently, self-supervised learning has shown effectiveness in solving data labeling issues. Its success mainly depends on having access to large, high-quality datasets with diverse features. It also relies on utilizing the spatial, temporal, and semantic structures present in the data. However, domains such as finance, healthcare, and insurance primarily utilize tabular data formats. This presents challenges for traditional data augmentation methods aimed at improving data quality. Furthermore, the privacy-sensitive nature of these domains complicates the acquisition of the extensive, high-quality datasets necessary for training effective self-supervised models. To tackle these challenges, our proposal introduces a novel framework that combines self-supervised learning with Federated Learning (FL). This approach aims to solve the problem of data-distributed training while ensuring training quality. Our framework improves upon the conventional self-supervised learning data augmentation paradigm by incorporating data labeling through the segmentation of data into subsets. Our framework adds noise by splitting subsets of data and can achieve the same level of centralized learning in a distributed environment. Moreover, we conduct experiments on various public tabular datasets to evaluate our approach. The experimental results showcase the effectiveness and generalizability of our proposed method in scenarios involving unlabeled data and distributed settings.