Final Year Projects (FYPs) - Undergraduates
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/37
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
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Item Vehicle Tracking System(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Rao Mubashar Ali , Ali Suhaib Shahid , Ammar Yasee; FA17-BCS-124 , FA17-BCS-082 , FA17-BCS-033; Dr. Zeeshan Gillani; LHR TP 7519The increase in population in metropolitan cities has given rise to severe traffic management problems and security issues. The rise in CCTV (Closed-circuit television) based solution has enabled us to monitor traffic but the sheer number of the cameras has given rise to another challenge of monitoring these systems and analyse this rich information. The advancement in computer vision techniques and GPU (Graphical processing unit) has now enable techniques than can automatically task which was only possible by humans in the past. CNN (Convolutional neural networks) based models can enable us to use real time videos of traffic data to aid in analysing the traffic conditions which in turn helps us in effective decision making regarding our cities to make them intelligent and safe. We will employ futuristic algorithms on the NVIDIA AI city datasets to evaluate the traffic detection and tracking systems.Item Artistify – Artist Identification of an Artwork using CNN(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Usama Anwar Butt; SP17-BCS-128; Dr. Zeeshan Gillani; LHR TP 7169Identifying the artist of the painting is a particularly difficult problem if there are no other metadata concerning the accessible image. Historians and Librarians in the past used to catalogue of notable artworks and used to debate over possible painters that might have originally painted the piece. This is a topic of active research in the computer vision field because of the challenging nature of the problem. We will apply a class of deep neural networks which is a Convolution Neural Network to analysing visual imagery. Using a dataset that contains artwork from the most creative artists of all time like Andy Warhol and Vincent Van Gogh, my network will learn the pattern and characteristics of the art and classify the given artwork to best predict the likely artist. Our goal is to create a model that learns to identify the artist analyzing new pictures.Item Plant Disease Phenotype Identification and Classification via Deep Learning(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Haider Ali Khichi , Muhammad Abdullah Aqib; SP17-BCS-038 , SP17-BCS-062; Dr. Zeeshan Gillani; LHR TP 7163Humans face global food shortages in the upcoming years, and we must maximize the yield of common crops to feed the growing population. Plant diseases are a major threat to small and large farm owners alike. These diseases reduce the potential yield of the crops, and in some severe but frequent cases, up to 100%. The identification of these diseases remains a challenge despite government efforts to educate farmers. There is a need for identification of these diseases early on so that they can be treated quickly. The pervasiveness of smartphones among farmers around the world offers the potential of adopting recent technological developments in computer science to develop a solution that can help in plant disease phenotype identification. This rich ecosystem of diverse communities can be a great advantage to crop heath and consequently the human race. To utilize the potential of these technologies we use a dataset curated by PlantVillage that contains over 50,000 images of 14 different crops that can be utilized to perform disease identification using modern deep learning algorithms. The models can then be employed by smartphones to identify the diseases and recommend possible treatmentsItem Deep learning approach for mango varieties identification using UAV imagery(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) ANISH HASSAN; SP19-BCS-009; Dr. Zeeshan GillaniIn this study, we propose a deep learning approach for the detection, variety identification, and health assessment of mango trees using UAV imagery. We used a DJI P4 Multispectral camera to collect images of mango orchards which were then used to train YOLOv5, YOLOv7, and Detectron2, three state-of-the-art object detection models. Our models were able to accurately detect trees in an image and classify the variety of the mango tree, as well as identify whether the tree is healthy or not. This approach can be useful for monitoring and managing mango orchards, as it allows for the efficient and accurate identification of tree variety and health status. This can be helpful for farmers as it can assist in early identification of diseased or unproductive trees, enabling timely action to be taken, such as providing targeted treatment or replacing the unproductive trees. Additionally, identifying the variety of the tree can help farmers in making better decision regarding harvesting, pruning and other orchard management tasks. The use of DJI P4 Multispectral camera enabled us to acquire both RGB and NDVI data, which helped us to differentiate the healthy trees from the unhealthy ones. The results of our study showed that the YOLOv5 model performed the best, achieving an overall accuracy of more than 80% in tree detection, variety identification and health assessment, demonstrating the potential of UAV imagery, multispectral sensor and deep learning in fruit orchard management. The proposed approach can be easily scaled and applied to other crop fields as well, providing farmers with a reliable and efficient tool for monitoring and management of their orchards.Item CNN based techniques for Detecting Anomalies in the Crop(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Rimsha Urooj , Shifa Allah Baksh , Fatima Batool; FA17-BSE-154 , FA17-BSE-164 , FA17-BSE-017; Dr. Zeeshan Gillani; LHR TP 7027The population across the globe is increasing at an alarming rate across the globe. This is giving rise to many challenges and one of the primary challenges is to feed an ever-increasing population with the same resources availed. This will lead to a food security and food crisis if we are not able to adapt our farming methods to modern technologies. With the advancement in computer vision techniques by using advanced CNN-based methods we can now analyse crops to detect anomalies in the crop. These applications could provide a foundation for the development of expertise aid or automatic screening tools. Such tools could contribute to further sustainable agricultural traditions and greater food production safety.