Abstract: Agriculture plays a significant role in the global economy, and there has been a trend in industrializing agriculture machinery and equipment. This paper proposes a framework that integrates data engineering and machine learning for the predictive maintenance of smart agriculture machinery. This framework is built upon existing state-of-the-art solutions, concepts, and techniques for data engineering and machine learning, along with the innovations of new solutions. This paper highlights the major elements of the collaboration. Challenges, societal impact, and future works are further discussed.
The agriculture sector is a key contributor to the global economy, with an estimated total value of more than 3 trillion dollars per year and corresponding employment of over 1 billion people worldwide. The rapid growth of population increases the demand for basic agricultural supplies. In the era of IoT, pollution prevention, and food safety assurance, agriculture has also been trending towards intelligence, automation, and standardization. There has been a trend in industrializing agriculture machinery and equipment, where a wide range of data sources from working conditions are equipped on agricultural machinery. These data sources may include 1D, 2D, 3D, and 4D data from cameras, LIDARs, radars, and sensor networks. These data sources by design have the potential of being a bridge to connect agriculture and smart city. However, the wide-scale deployments of data-driven smart agriculture have been hindered by the challenge of data engineering for the large-scale, heterogeneous, and sparsely-distributed agriculture data, and insufficient integration of data exploitation and exploration technologies including machine learning for deep analysis, insight mining, and knowledge discovery of agriculture data.
Moreover, a variety of application scenarios in smart agriculture have appeared in recent years, including but not limited to predictive maintenance of agriculture machinery, soil monitoring with sensor networks, and enviromonitoring with remote sensing data for crop estimation. The laboratory research has made promising achievements in devising precise models with techniques from data analytics, machine learning, artificial intelligence, computer vision, and other similar fields. However, these achievements are hardly used in practice because of integration challenges. Integrating data engineering and machine learning for the collaborative, collaborative-wise, and process-wise predictive maintenance of smart agriculture machinery is yet to be studied comprehensively and in-depth.

Keywords: Predictive Maintenance,Smart Agriculture,Machine Learning,Data Engineering,IoT Sensors,Time Series Analysis,Remote Monitoring,Failure Prediction,Remaining Useful Life (RUL),Condition-Based Maintenance,Edge Computing,Big Data Analytics,Sensor Fusion,Agricultural Machinery,Maintenance Optimization.

| DOI: 10.17148/IJIREEICE.2021.91215