Prospects for the use of IoT technologies in agricultural machinery monitoring: development and testing of the «AgroTrack» platform

The article examines the prospects for implementing IoT technologies and platform-based solutions in the practice of agricultural machinery monitoring, justified by the need to improve productivity, manageability, and reduce dependence on human factors in the agro-industrial complex. The aim of the study is to substantiate the feasibility and effectiveness of using the Internet of Things for collecting and analyzing real-time data on the condition of agricultural machinery. The paper presents an overview of domestic and international experiences in integrating IoT devices into agricultural production processes, demonstrating the need to develop accessible domestic solutions based on IoT technologies. It is shown that such systems can function even in the absence of a well-developed ICT infrastructure and sufficient financial resources. In particular, the article presents the developed “AgroTrack” platform, designed for small and medium-sized agricultural enterprises, which enables real-time monitoring of agricultural machinery and the collection of data on its performance (routes and location; operational parameters such as speed, engine hours, fuel consumption, engine start-stop cycles; technical condition, etc.). The system architecture, equipment components, and functional capabilities for telemetry visualization, predictive analytics, and remote diagnostics are described. The prospects for the platform and its further development are substantiated. The following current application benefits are highlighted: enhanced tracking capabilities and improved machinery operation processes; minimization of downtime, prevention of critical wear and failure through the use of predictive diagnostics; reduction of maintenance and repair costs for agricultural machinery; and extension of machinery service life. Based on the conducted research, conclusions are drawn regarding the scalability potential of the solution due to the integration of failure-prediction algorithms and its preliminary impact on the readiness of agricultural enterprises to adopt data-driven management.