## 6 Conclusions  
Food logging applications using vision-based methods for food recognition face several challenges outside the laboratory environment. Firstly, deep networks trained on pristine food images have a poor performance on distorted images affected by blur or additive noise during image acquisition. Similarly, progressive learning is vital for catering to domain variations due to quality distortions or changes in concepts. Despite that, existing progressive classifiers recognize food categories and do not detect food ingredients. We have designed a quality resilient explainable framework by keeping in view the challenges faced by the practical food logging applications. It uses the state-of-the-art deep learning model (InceptionResNetV2) for feature extraction. To study the effect of image quality on it, we have examined the model under various quality distortions. Based on our analysis, we have successfully improved the robustness of the convolutional neural network by using our novel data augmentation method, RIMixUp, during fine-tuning. The experimental results have shown a significant improvement in the robustness of visual features extracted via quality resilient CNN. After visual feature extraction via the quality resilient convolutional neural network, we have tackled the challenge of selecting the optimal subset of features from high dimension feature space. At first, we have used the Gradient Explainer to understand the contribution of the photo pixels and the area of interest to the output layer. These visualizations have shown that all pixels from the relevant portions do not have an equal or positive contribution. Based on these findings, the Gradient Explainer-based algorithm uses SHAP values to rank features and then selects reliable attributes from high dimensional data. This strategy has shown improved performance and has reduced the accumulated learning time of the PKELM classifier by 51.73% for all the food datasets. During the classification stage, our work has used a progressive classifier--PKELM--to categorize food and ingredient detection. It has dynamically adjusted its structure using the activation value to cater to domain changes and novel concepts. Moreover, unlike most previous works, it has learned both food categories and ingredients. During online learning, it has detected noisy instances and has assigned labels to unlabeled data instances. Experimental analysis of our framework has shown the competitive performance as compared to other works while having many technical advantages for practical food logging applications. To the best of our knowledge, it is the first framework that has tackled low-quality food images, has detected noisy instances during online learning, and has assigned labels to unlabeled data instances. Similarly, in contrast to other methodologies, it has employed a progressive classifier for food categorization and ingredient detection. Finally, we have integrated the framework with a mobile application to automate food image analysis. In the future, we plan to conduct research in three directions: (1) We plan to exploit unsupervised feature learning, as preparing a large comprehensive annotated dataset is a challenge; (2) We can reduce the classification space in a practical application by incorporating personal dietary preferences such as vegetarian, non-vegetarian. This approach would presumably simplify and improve the classification performance of the proposed framework; and (3) We generalize the proposed framework to other related fields such as fruits and plant disease detection and so forth. [PERSON] was responsible for writing algorithms, analysis, writing the article and literature search, and approved the final version as submitted. [PERSON] contributed to the study design, reviewed the study for intellectual content, and confirmed the final version as submitted. All authors have read and agreed to the published version of the manuscript.