The Simultaneous Localisation And Mapping (SLAM) problem is an open fundamental problem in autonomous mobile robotics. One of the latest most researched techniques used to enhance the SLAM methods is instance segmentation. In this thesis, we implement an instance segmentation system using Swin Transformer combined with two of the state of the art methods of instance segmentation namely Cascade Mask RCNN and Mask RCNN. Instance segmentation is a technique that simultaneously solves the problem of object detection and semantic segmentation. We show that depth information enhances the average precision (AP) by approximately 7%. We also show that the Swin Transformer backbone model can work well with depth images. Our results also show that Cascade Mask RCNN outperforms Mask RCNN. However, the results are to be considered due to the small size of the NYU-depth v2 dataset. Most of the instance segmentation researches use the COCO dataset which has a hundred times more images than the NYU-depth v2 dataset but it does not have the depth information of the image.

Background. This Thesis addresses the critical issue of disease management in ricecrops, a key factor in ensuring both food security and the livelihoods of farmers. Objectives. The primary focus of this research is to tackle the often-overlooked challenge of precise disease localization within rice plants by harnessing the power of deep learning techniques. The primary goal is not only to classify diseases accurately but also to pinpoint their exact locations, a vital aspect of effective disease management. The research encompasses early disease detection, classification, andthe precise identification of disease locations, all of which are crucial components of a comprehensive disease management strategy. Methods. To establish the reliability of the proposed model, a rigorous validation process is conducted using standardized datasets of rice plant diseases. Two fundamental research questions guide this study: (1) Can deep learning effectively achieve early disease detection, accurate disease classification, and precise localizationof rice plant diseases, especially in scenarios involving multiple diseases? (2) Which deep learning architecture demonstrates the highest level of accuracy in both disease  diagnosis and localization? The performance of the model is evaluated through the application of three deep learning architectures: Masked RCNN, YOLO V8, and SegFormer. Results. These models are assessed based on their training and validation accuracy and loss, with specific metrics as follows: For Masked RCNN, the model achieves a training accuracy of 91.25% and a validation accuracy of 87.80%, with corresponding training and validation losses of 0.3215 and 0.4426. YOLO V8 demonstrates a training accuracy of 85.50% and a validation accuracy of 80.20%, with training andvalidation losses of 0.4212 and 0.5623, respectively. SegFormer shows a training accuracy of 78.75% and a validation accuracy of 75.30%, with training and validation losses of 0.5678 and 0.6741, respectively. Conclusions. This research significantly contributes to the field of agricultural disease management, offering valuable insights that have the potential to enhance crop yield, food security, and the overall well-being of farmers