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%.

This study introduces an Integrated Information System (IIS) for the management, analysis, and visualization of Cultural Heritage (CH) data. Emphasizing the critical importance of digital technologies for the preservation, accessibility, and understanding of CH assets, the article presents a comprehensive case study of the Holy Aedicule of the Holy Sepulchre. It highlights the necessity for a unified system that efficiently integrates diverse data types and analytical methods. The proposed web-based platform is designed to support various stakeholders by providing advanced tools for data management, analytics, and visualization, thereby facilitating the sustainable preservation and educational utilization of CH assets. Through this framework, the study contributes to the advancement of CH management practices, addressing both current challenges and anticipating future needs.