13 Zentrale Universitätseinrichtungen

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/14

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Institute: search.filters.UBSInstitut.Höchstleistungsrechenzentrum Stuttgart (HLRS)Author: search.filters.author.Pejak, Branislav

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    Soya yield prediction on a within-field scale using machine learning models trained on Sentinel-2 and soil data
    (2022) Pejak, Branislav; Lugonja, Predrag; Antić, Aleksandar; Panić, Marko; Pandžić, Miloš; Alexakis, Emmanouil; Mavrepis, Philip; Zhou, Naweiluo; Marko, Oskar; Crnojević, Vladimir
    Agriculture is the backbone and the main sector of the industry for many countries in the world. Assessing crop yields is key to optimising on-field decisions and defining sustainable agricultural strategies. Remote sensing applications have greatly enhanced our ability to monitor and manage farming operation. The main objective of this research was to evaluate machine learning system for within-field soya yield prediction trained on Sentinel-2 multispectral images and soil parameters. Multispectral images used in the study came from ESA’s Sentinel-2 satellites. A total of 3 cloud-free Sentinel-2 multispectral images per year from specific periods of vegetation were used to obtain the time-series necessary for crop yield prediction. Yield monitor data were collected in three crop seasons (2018, 2019 and 2020) from a number of farms located in Upper Austria. The ground-truth database consisted of information about the location of the fields and crop yield monitor data on 411 ha of farmland. A novel method, namely the Polygon-Pixel Interpolation, for optimal fitting yield monitor data with satellite images is introduced. Several machine learning algorithms, such as Multiple Linear Regression, Support Vector Machine, eXtreme Gradient Boosting, Stochastic Gradient Descent and Random Forest, were compared for their performance in soya yield prediction. Among the tested machine learning algorithms, Stochastic Gradient Descent regression model performed better than the others, with a mean absolute error of 4.36 kg/pixel (0.436 t/ha) and a correlation coefficient of 0.83%.
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    Container orchestration on HPC systems through Kubernetes
    (2021) Zhou, Naweiluo; Georgiou, Yiannis; Pospieszny, Marcin; Zhong, Li; Zhou, Huan; Niethammer, Christoph; Pejak, Branislav; Marko, Oskar; Hoppe, Dennis
    Containerisation demonstrates its efficiency in application deployment in Cloud Computing. Containers can encapsulate complex programs with their dependencies in isolated environments making applications more portable, hence are being adopted in High Performance Computing (HPC) clusters. Singularity, initially designed for HPC systems, has become their de facto standard container runtime. Nevertheless, conventional HPC workload managers lack micro-service support and deeply-integrated container management, as opposed to container orchestrators. We introduce a Torque-Operator which serves as a bridge between HPC workload manager (TORQUE) and container orchestrator (Kubernetes). We propose a hybrid architecture that integrates HPC and Cloud clusters seamlessly with little interference to HPC systems where container orchestration is performed on two levels.