In this work, we aim to introduce some modifications to the Anam-Net deep neural network (DNN) model for segmenting optic cup (OC) and optic disc (OD) in retinal fundus images to estimate the cup-to-disc ratio (CDR). The CDR is a reliable measure for the early diagnosis of Glaucoma. In this study, we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images. Our DNN model is based on modifications to Anam-Net, incorporating an anamorphic depth embedding block. To reduce computational complexity, we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network deepens. This modification significantly reduces the number of trainable parameters, making the model lightweight and suitable for resource-constrained applications. We evaluate the performance of the developed model using two publicly available retinal image databases, namely RIM-ONE and Drishti-GS. The results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD segmentation. We used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images, respectively. For OD segmentation using the RIM-ONE we obtain an f1-score (F1), Jaccard coefficient (JC), and overlapping error (OE) of 0.950, 0.9219, and 0.0781, respectively. Similarly, for OC segmentation using the same databases, we achieve scores of 0.8481 (F1), 0.7428 (JC), and 0.2572 (OE). Based on these experimental results and the significantly lower number of trainable parameters, we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.

Wheat is a critical crop, extensively consumed worldwide, and its production enhancement is essential to meet escalating demand. The presence of diseases like stem rust, leaf rust, yellow rust, and tan spot significantly diminishes wheat yield, making the early and precise identification of these diseases vital for effective disease management. With advancements in deep learning algorithms, researchers have proposed many methods for the automated detection of disease pathogens; however, accurately detecting multiple disease pathogens simultaneously remains a challenge. This challenge arises due to the scarcity of RGB images for multiple diseases, class imbalance in existing public datasets, and the difficulty in extracting features that discriminate between multiple classes of disease pathogens. In this research, a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data, thereby overcoming the problems of class imbalance and data scarcity. This study proposes a customized architecture of Vision Transformers (ViT), where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks. This paper also proposes a Model Agnostic Meta Learning (MAML) based ensemble classifier for accurate classification. The proposed model, validated on public datasets for wheat disease pathogen classification, achieved a test accuracy of 99.20% and an F1-score of 97.95%. Compared with existing state-of-the-art methods, this proposed model outperforms in terms of accuracy, F1-score, and the number of disease pathogens detection. In future, more diseases can be included for detection along with some other modalities like pests and weed.