Designing data engineering pipelines for real-time agricultural insights

Advanced data engineering techniques have now shown considerable potential in realizing the precision agriculture practice in crop production cycle, helping farmers take prompt and timely decisions using real-time farm data, relate it to their years of hands-on experience and monitoring, to forecast their yield and the quality of the produce. Data has also become one of the most valuable resources today in realizing realistic decision-making protocols for agriculture that draw insight from historical agricultural data, in-turn enhancing the domain knowledge with adequate modeling and analysis. All-in-all it is now possible to compute and store historical data from all seasons of a crop production life-cycle, in both on-cloud storage as well as local edge or IoT database, through advanced sensors technologies and decision-making pipelines. Further, leveraging the power of Artificial Intelligence for predictive analysis, big data tools can analyze both on-cloud and local data efficiently for providing insights into upcoming harvests (Kamilaris & Prenafeta-Boldú, 2018; Jha et al., 2019; Tsouros et al., 2019). Several data analysis frameworks also combine domain knowledge with Artificial Intelligence based models to ascertain towards improving not only the predictive yield based decisions, but also the day-to-day remedial measures to keep the yield in check. This has been further emphasized and evidenced by improvements in the farming eco-system that followed after the adaptation and understanding of the significance of data in the recent years, and the advantages that proper decisions based on right data can achieve in farming. Having adopted this approach, it is now imperative that data acquisition from modern and timely decision-making pipelines leading into accurate yields and quality of harvest, remains key to effective food production and agriculture-based research. In particular, the importance of adopting the significance of big data and data science in dealing with agricultural problems.