This article presents AgroNova, an intelligent and autonomous Internet of Things (IoT) platform developed for real-time monitoring and control of the microclimate in greenhouses. The system combines distributed wireless sensor nodes, actuator mod-ules, a local gateway equipped with a rule-based control agent, and a cloud infra-structure for data visualization and decision support. The platform’s hybrid architec-ture enables autonomous operation in the event of internet failures and at the same time allows the integration of a large language model (LLM) for context-based deci-sions. AgroNova was implemented in a tomato greenhouse and validated over a period of seven months, during which over 400,000 environmental data points were recorded. The system effectively kept temperature and humidity within optimal agronomic ranges and reduced deviation time compared to manual control. In experimental tests, the LLM component generated relevant recommendations under complex conditions, such as bad weather. The results show that AgroNova is a reliable and scalable solution for greenhouse microclimate management. The combination of local autonomy and cloud intelligence of the platform offers promising applications in precision agriculture. Future work in-cludes extending the scope of LLM-assisted reasoning and adapting the platform to additional crops and greenhouse environments.

The article presents the Wheat-Weed Dataset - the first dataset on weeds in wheat grown on the territory of Bulgaria. The set contains 4647 unique images belonging to 7 plant species. The images are full color and annotated. To evaluate the effectiveness of the base, two deep networks with different characteristics were trained on it. During the study, numerous experiments were performed with different combinations of the hyperparameters. The results obtained are excellent. Wheat-Weed Dataset is freely available.

This study presents AgroNova—a hybrid IoT architecture for autonomous monitoring and management of microclimate in greenhouse environments. The system combines a capillary wireless sensor network, gateway-level rule-based agents, a server agent, cloud services and an advisory component based on a large language model (LLM) that supports local decision-making by incorporating external contextual information from meteorological services. The proposed architecture was validated through a seven-month deployment in an unheated tomato greenhouse, during which more than 380,000 environmental measurements were collected from five sensor nodes. The system operated continuously under real agricultural conditions, including during temporary internet connectivity interruptions, due to the autonomous gateway-level control and deterministic fallback mechanisms. The analysis of the collected data includes 3110 environmental threshold exceedance events, in which recovery dynamics, reaction latency, and actuator activation frequency were evaluated. The results show that the architecture supports stable autonomous operation under limited actuation conditions, with an average local reaction latency of less than 1 s, while physical actuator operations occur in approximately 2.3% of all control decisions. This behavior reflects a conservative control strategy that limits unnecessary mechanical operations and contributes to stable system operation. The experimental integration of a consultative LLM module within the server-side agent demonstrates the potential for context-enriched decision support using external meteorological data, while final control decisions remain under the authority of the gateway-based deterministic control mechanism. The main contribution of this study is the demonstration of a hybrid IoT architecture that combines edge-level autonomy with context-assisted reasoning, validated through deployment in a real greenhouse environment.

The rapid growth of course-based online platforms has expanded access to learning in non-formal educational contexts. However, many existing systems primarily emphasize content delivery and transactional functionality, with limited pedagogical coordination between activity tracking, assessment, and learner guidance. This paper proposes a multi-layered architectural framework for orchestrating course-based online learning through a structured separation of content management, learning activity tracking, competency modeling, adaptive support, and credentialing. A key contribution of the proposed model is the distinction between content completion and competency mastery. While completion indicators reflect engagement, mastery estimation is grounded in assessment evidence aligned with formally defined learning outcomes. The architecture introduces an orchestration mechanism realized through distributed coordination across layers rather than through a centralized autonomous controller, positioning artificial intelligence as a supportive tool for assessment item generation and feedback provision. A functional web-based prototype, SoftLearner, has been developed to validate the feasibility of the infrastructure and learning layers. The higher layers are defined architecturally and provide a foundation for future empirical validation. The proposed framework contributes to the development of pedagogically oriented AI-supported systems in non-formal online learning environments and supports cumulative, competency-based recognition of learning outcomes within lifelong learning contexts.