A data-driven approach to agricultural machinery working states analysis during ploughing operations

In the field of precision agriculture, there is a significant shift towards data-driven methodologies that considerably enhance the efficiency and sustainability of agricultural operations. This research investigates the application of CAN-Bus and GNSS data to develop a comprehensive analysis of agricultural machinery's operational states during ploughing operations through advanced data analytics techniques, including machine learning. The primary tool utilized in this study is the Random Forest classifier, a robust algorithm well-suited for handling the complexity and volume of data typical in modern agricultural settings. The study evaluates Random Forest models trained on various feature subsets to accurately identify different operational states of agricultural machinery, including idle, moving, turning, and working states. By merging CAN-Bus data, which capture real-time operational parameters, with GNSS data, providing spatial and temporal context, it is possible to achieve a comprehensive understanding of machinery behaviour and its interaction with field conditions. This integration significantly enhances decision-making capabilities in farm management, leading to more effective and efficient operations.

Furthermore, the findings from this study contribute to the broader agricultural community by illustrating how data-driven approaches can harness the vast amounts of data generated by modern agricultural machinery. This research underscores the potential of machine learning modelsnot only to interpret complex data sets but also to transform these insights into actionable knowledge, which can lead to more precise and sustainable agricultural practices. Overall, this study offers a systematic approach for analysing agricultural data and lays the groundwork for future advancements in incorporating machine learning and IoT technologies into the agricultural sector. This aims to enhance productivity and sustainability in farming practices.