This project aims to develop effective software for decision-making support, i.e. to facilitate the business process of entities in the food business in our country, as well as competent authorities, and from the aspect of support and more effective strengthening and ensuring of food safety and research of disease outbreaks.
Researchers from the FoodHub CoE and HPC NCC Montenegro work on the FoodDecide project to identify the most important data necessary for software development such as visualisation of the food value chain.
FoodDecide – collaboration between FoodHub CoE and HPC NCC Montenegro
Ms Zoja Scekic, a student of the Faculty of Applied Sciences, defended her BSc Thesis in Electrical Engineering and Computer Science. The topic of the thesis work was the use of machine learning to detect and classify the parking spaces by processing images from camera sensors. Such a solution could find application in Smart city solutions. The work focused on the creation of a prediction model as well as validation with images collected at the UDG parking. She has done her thesis work under the supervision of prof. Tomo Popovic, PhD, and mr Stevan Cakic, MSc.
BSc Thesis – AI/ML Computer Vision for Smart Parking
ABSTRACT – Smart city is one area with the growing use of Internet of Things and Artificial Intelligence. The concept of smart cities relies on making quality of life better, and solving important problems, such as global warming, public health, energy and resources. Smart parking management is one of the smart city use cases. This paper describes the use of deep learning algorithms to process images of parking lots and determine their current occupancy. The development of prediction models was done using PKLot dataset with 12417 images, Detectron2 software library, and Faster R-CNN algorithm. The resulting models can be integrated into parking space sensors and used for building smart parking solutions, and thus lead to more efficient use of space in urban areas, reduced traffic congestion, as well as reducing parking surfing to minimum.
Another excellent graduate from the Faculty of Applied Sciences
FF4EuroHPC Experiment 1003 focused on AI/ML Based Computer Vision for Next Generation Poultry Farms was presented at the OC1 Workshop today. We discussed the project objectives, experiment approach, the benefits of the use of HPC and Deep Learning. Learn more about FF4EuroHPC project and HPC experiments at https://www.ff4eurohpc.eu/.
The fourth workshop took place on October 19th, 2022 and included presentations of the experiments related to Maintenance, Agriculture & Assets Management Sectors. University of Donja Gorica and NCC Montenegro were part of the experiment presentation related to the use of HPC to develop AI/ML computer vision solutions for smart agriculture.
Researchers from EuroCC Montenegro presented two papers at the IEEE International Conference on Omni-Layer Intelligent Systems (COINS). IEEE COINS (link) is the right place to be. IEEE COINS brings together experts in Digital Transformation (from AI and IoT to Cloud, Blockchain, Cybersecurity, and Robotics) from around the globe. IEEE COINS includes a multi-disciplinary program from technical research papers, to panels, workshops, and tutorials on the latest technology developments and innovations addressing all important aspects of the IoT & AI ecosystem. The conference took place 1-3 August in Barcelona.
This paper was a result of the collaboration on FF4EuroHPC application experiment project called AIMHiGH that focuses on computer vision and the use of HPC to develop object detection prediction models for the use in smart agriculture, more specifically in the poultry sector. The title of the paper is “Developing Object Detection Models for Camera Applications in Smart Poultry Farms”.
ABSTRACT – This paper proposes the use of high-performance computing and deep learning to create prediction models that can be deployed as a part of smart agriculture solutions in the poultry sector. The idea is to create object detection models that can be ported onto edge devices equipped with camera sensors for the use in Internet of Things systems for poultry farms. The object detection prediction models could be used to create smart camera sensors that could evolve into sensors for counting chickens or detecting dead ones. Such camera sensor kits could become a part of digital poultry farm management systems in shortly. The paper discusses the approach to the development and selection of machine learning and computational tools needed for this process. Initial results, based on the use of Faster R-CNN network and high-performance computing are presented together with the metrics used in the evaluation process. The achieved accuracy is satisfactory and allows for easy counting of chickens. More experimentation is needed with network model selection and training configurations to increase the accuracy and make the prediction useful for developing a dead chicken detector. (link)
Researchers from UDG and NCC Montenegro will participate in the upcoming IEEE COINS2022 conference in Barcelona, Spain. Two research papers will be presented at the conference presenting the research activities on the use of HPC and machine learning in agriculture and medicine. The conference is taking place 1-2 August 2022. More information on the conference is available here.
NCC Montenegro team members will present two papers on HPC/AI applications in medicine and agriculture
NCC Montenegro researchers at UDG won another NVIDIA Academic Grant that will provide additional classroom equipment (Jetson Nano). This equipment will be used for implementing Edge AI classroom and realization of training in AI applications in IoT. The grant was awarded to prof. Tomo Popovic. Researchers participating in the Edge AI training are Stevan Cakic, MSc, Ivan Jovovic, Dejan Babic, and Zoja Scekic and it will be organized in with the support from NCC Montenegro.
Prof. Popovic awarded NVIDIA Academic Grant for Edge AI ClassroomPreparing the classroom for new ML course for Edge AI
Mr Stevan Šandi, a PhD candidate at UDG and NCC Montenegro team member, won NVIDIA Academic Hardware Grant for his PhD research under DASCAP project. He was awarded an advanced GPU hardware that will be used for AI and Data Science applications in agriculture domain. He will be experimenting with various computer vision prediction models. Doing experiments on a better GPU will significantly reduce the time to get all of the planned experiments done.
Mr. Stevan Šandi was awarded NVIDIA Academic Hardware Grant
