Multitemporal crop classification approaches have demonstrated high performance within a given season. However, cross-season and cross-region crop classification presents a unique transferability challenge. This study addresses this challenge by adopting a domain generalization approach, e.g., by training models on multiple seasons to improve generalization to new, unseen target years. We utilize a comprehensive five-year Sentinel-2 dataset over different agricultural regions in Slovakia and a diverse crop scheme (eight crop classes). We evaluate the performance of different machine learning classification algorithms, including random forests, support vector machines, quadratic discriminant analysis, and neural networks. Our main findings reveal that the transferability of models across years differs between regions, with the Danubian lowlands demonstrating better performance (overall accuracies ranging from 91.5% in 2022 to 94.3% in 2020) compared to eastern Slovakia (overall accuracies ranging from 85% in 2022 to 91.9% in 2020). Quadratic discriminant analysis, support vector machines, and neural networks consistently demonstrated high performance across diverse transferability scenarios. The random forest algorithm was less reliable in generalizing across different scenarios, particularly when there was a significant deviation in the distribution of unseen domains. This finding underscores the importance of employing a multi-classifier analysis. Rapeseed, grasslands, and sugar beet consistently show stable transferability across seasons. We observe that all periods play a crucial role in the classification process, with July being the most important and August the least important. Acceptable performance can be achieved as early as June, with only slight improvements towards the end of the season. Finally, employing a multi-classifier approach allows for parcel-level confidence determination, enhancing the reliability of crop distribution maps by assuming higher confidence when multiple classifiers yield similar results. To enhance spatiotemporal generalization, our study proposes a two-step approach: (1) determine the optimal spatial domain to accurately represent crop type distribution; and (2) apply interannual training to capture variability across years. This approach helps account for various factors, such as different crop rotation practices, diverse observational quality, and local climate-driven patterns, leading to more accurate and reliable crop classification models for nationwide agricultural monitoring.

This paper presents control strategy for a group of multi-rotor unmanned aerial vehicles (UAV). Descripted biologically inspired method was developed to solve exploration or monitoring tasks. The proposed solution was tested in real environment on a quad-rotor UAV platform. Our abstract model is a decentralized adaptive system with a shared memory which represents the environment.

Insect colony inspires scientists for years to create similar behavior in the robotic application. The main goal of our work was to develop simple and powerful algorithm which will accept dynamically changes in the size of a robot swarm due the mission. This algorithm is suitable for situations where unpredictable conditions may lead to robot fault in multi-robotics system and mission completion is endangered. In this article we would like to investigate properties of a simple pheromone based algorithm. The algorithm operates as cellular automata and partially uses an insect pheromone strategy for the robots coordination. Our abstract model is a decentralized adaptive system with a shared memory which represents the environment.

This article refers to possibility of utilization of modeling of a manufacturing system sense as a discrete event system by the optimization of a job schedule. Using software tools we can create a model, which allows simulation of system behaviour at varying parameters. The example of the manufacturing system model created by the SimEvents toolbox of software tools Matlab is illustrated.

This paper represents biologically inspired control strategy for a group of robots. A descripted method was developed to solve exploration and monitoring tasks. The main goal of our work was to develop a simple and powerful algorithm which will accept dynamical changes in the number of robot swarm in line with its mission. This algorithm is suitable for situations where unpredictable conditions may lead to failures in multi-robotics system and the completion of the mission is endangered. In this article we would like to focus on the investigation of the actual characteristics of a simple pheromone based algorithm in a situation where there is a sudden failure in communication.

This article compares two approaches by jobshop scheduling problem (JSSP) in real manufacturing system. The first approach is based on mechanisms inspired by biological evolution and the immune system. The second stochastic optimization algorithm is based on social simulation models.