Wheat yellow rust is a common agricultural disease that affects the crop every year across the world. The disease not only negatively impacts the quality of the yield but the quantity as well, which results in adverse impact on economy and food supply. It is highly desired to develop methods for fast and accurate detection of yellow rust in wheat crop; however, high-resolution images are not always available which hinders the ability of trained models in detection tasks. The approach presented in this study harnesses the power of super-resolution generative adversarial networks (SRGAN) for upsampling the images before using them to train deep learning models for the detection of wheat yellow rust. After preprocessing the data for noise removal, SRGANs are used for upsampling the images to increase their resolution which helps convolutional neural network (CNN) in learning high-quality features during training. This study empirically shows that SRGANs can be used effectively to improve the quality of images and produce significantly better results when compared with models trained using low-resolution images. This is evident from the results obtained on upsampled images, i.e., 83% of overall test accuracy, which are substantially better than the overall test accuracy achieved for low-resolution images, i.e., 75%. The proposed approach can be used in other real-world scenarios where images are of low resolution due to the unavailability of high-resolution camera in edge devices.

White blood cell (WBC) classification and morphological attribute prediction are critical for automated hematological analyses. To provide detailed and interpretable predictions, this paper proposes a multimodal visual-language embedding learning approach based on the contrastive language image pretraining (CLIP) model for WBC classification and attribute prediction. First, structured natural language prompts are created around WBC types and morphological attributes to offer rich semantic context that enhances the processing of multimodal inputs. Moreover, a joint-task optimization strategy is introduced to align the generated encodings from the WBC images with their corresponding structured text prompts in a shared semantic space, thus improving interpretability and prediction accuracy. Furthermore, a multi-task loss function with an adaptive weighting mechanism is employed to address class imbalance, effectively balancing classification and attribute prediction tasks to boost model performance. Experimental evaluations on publicly available datasets demonstrate that the proposed method achieves state-of-the-art performance in both WBC classification and attribute prediction.