Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.

The use of firearms, their frequency, and legitimacy through self-defence and extreme necessity are socially relevant in Czechia and Slovakia. Legal firearm ownership for defence purposes impacts overall social security, influenced by factors like firearm legislation, cultural traditions, legal awareness, and violent crime rates. Understanding this issue requires considering subjective interpretations, even among security experts. This paper explores the theoretical foundations of self-defence and extreme necessity from criminal law, alongside practical implications supported by police statistics on violent crimes involving firearms in Czechia and Slovakia. It also includes a comparison with selected EU countries. The authors’ research uses a questionnaire to assess attitudes towards choosing defensive firearms, preparation for firearms licensure, and potential support for state security forces. The findings provide insights into legal firearm owners’ behaviours and attitudes toward defence and security. The study aims to contribute to a deeper understanding of firearm use for self-defence, correlating training, weapon preferences, and willingness to enhance state security.