Early recognition of citrus diseases is important for preventing crop losses and employing timely disease control measures in farms. Employing machine learning-based approaches, such as deep learning for accurate detection of multiple citrus diseases is challenging due to the limited availability of labeled diseased samples. Further, a lightweight architecture with low computational complexity is required to perform citrus disease classification on resource-constrained devices, such as mobile phones. This enables the practical utility of the architecture to perform effective monitoring of diseases by farmers using their own mobile devices in the farms. Hence, we propose a lightweight, fast, and accurate deep metric learning-based architecture for citrus disease detection from sparse data. In particular, we propose a patch-based classification network that comprises an embedding module, a cluster prototype module, and a simple neural network classifier, to detect the citrus diseases accurately. Evaluation of our proposed approach using publicly available citrus fruits and leaves dataset reveals its efficiency in accurately detecting the various diseases from leaf images. Further, the generalization capability of our approach is demonstrated using another dataset, namely the tea leaves dataset. Comparison analysis of our approach with existing state-of-the-art algorithms demonstrate its superiority in terms of detection accuracy (95.04%), the number of parameters required for tuning (less than 2.3 M), and the time efficiency in detecting the citrus diseases (less than 10 ms) using the trained model. Moreover, the ability to learn with fewer resources and without compromising accuracy empowers the practical utility of the proposed scheme on resource-constrained devices, such as mobile phones.

Understanding an unknown facial emotion that emerges in the future underpins significant impacts in various domains. Knowing the fact that emotional states grow in vocabulary, new emotional states need to be adapted while the existing knowledge of known emotional states is preserved. While human beings spontaneously perform this task, the challenge is, how a deep learning technique can effectively recognize an unknown emotion category in the future. Although the deep convolutional neural network has shown excellent emotion recognition performances in the past, it is conventionally a predefined multi-way classifier showing little resilience towards adding a new emotion class. Considering the aforementioned challenge, in this paper, we propose a generic deep convolutional neural network-based architecture that constantly absorbs the upcoming emotion categories and recognizes them effectively. We further propose an indicator loss, which is associated with the distillation mechanism that preserves the existing knowledge. In order to demonstrate the feasibility of our proposed method, we evaluated our model using benchmark emotion datasets. The results confirm that the proposed approach is superior in recognizing unknown emotional states compared to continual learning benchmarks. Further, our proposed method demonstrates higher accuracy, compared to the transfer learning baselines.

Vision transformers (ViTs) have recently gained traction in plant disease classification due to their strong performance in visual recognition tasks. However, their application to tomato leaf disease recognition remains challenged by two factors, namely the need for models that can generalise across diverse disease conditions and the absence of a unified framework for systematic comparison. Existing ViT-based approaches often yield inconsistent results across datasets and disease types, limiting their reliability and practical deployment. To address these limitations, this study proposes the ViT-Based Robust Framework (ViT-RoT), a novel benchmarking framework designed to systematically evaluate the performance of various ViT architectures in tomato leaf disease classification. The framework facilitates the systematic classification of state-of-the-art ViT variants into high-, moderate-, and low-performing groups for tomato leaf disease recognition. A thorough empirical analysis is conducted using one publicly available benchmark dataset, assessed through standard evaluation metrics. Results demonstrate that the ConvNeXt-Small and Swin-Small models consistently achieve superior accuracy and robustness across all datasets. Beyond identifying the most effective ViT variant, the study highlights critical considerations for designing ViT-based models that are not only accurate but also efficient and adaptable to real-world agricultural applications. This study contributes a structured foundation for future research and development in vision-based plant disease diagnosis.

In this paper, a novel Siamese network-based lightweight framework is proposed for automatic tomato leaf disease recognition. This framework achieves the highest accuracy of 96.97% on the tomato subset obtained from the PlantVillage dataset and 95.48% on the Taiwan tomato leaf disease dataset. Experimental results further confirm that the proposed framework is effective with imbalanced and small data. The backbone network integrated with this framework is lightweight with approximately 2.9629 million trainable parameters, which is second to SqueezeNet and significantly lower than other lightweight deep networks. Automatic tomato disease recognition from leaf images is vital to avoid crop losses by applying control measures on time. Even though recent deep learning-based tomato disease recognition methods with classical training procedures showed promising recognition results, they demand large labeled data and involve expensive training. The traditional deep learning models proposed for tomato disease recognition also consume high memory and storage because of a high number of parameters. While lightweight networks overcome some of these issues to a certain extent, they continue to show low performance and struggle to handle imbalanced data.