New trends in precision agriculture: a novel cloud-based system for enabling data storage and agricultural task planning and automation

• Published in: Precision agriculture 2017-12, Vol.18 (6), p.1038-1068
• Main Authors: Pavón-Pulido, N., López-Riquelme, J. A., Torres, R., Morais, R., Pastor, J. A.
• Format: Article
• Language: English
• Subjects: Agriculture; Atmospheric Sciences; Biomedical and Life Sciences; Chemistry and Earth Sciences; Computer Science; Life Sciences; Physics; Remote Sensing/Photogrammetry; Soil Science & Conservation; Statistics for Engineering
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-017-9532-7

It is well-known that information and communication technologies enable many tasks in the context of precision agriculture. In fact, more and more farmers and food and agriculture companies are using precision agriculture-based systems to enhance not only their products themselves, but also their means of production. Consequently, problems arising from large amounts of data management and processing are arising. It would be very useful to have an infrastructure that allows information and agricultural tasks to be efficiently shared and handled. The cloud computing paradigm offers a solution. In this study, a cloud-based software architecture is proposed with the aim of enabling a complete crop management system to be deployed and validated. Such architecture includes modules developed by using Google App Engine, which allows the information to be easily retrieved and processed and agricultural tasks to be properly defined and planned. Additionally, Google’s Datastore (which ensures a high scalability degree), hosts both information that describes such agricultural tasks and agronomic data. The architecture has been validated in a system that comprises a wireless sensor network with fixed nodes and a mobile node on an unmanned aerial vehicle (UAV), deployed in an agricultural farm in the Region of Murcia (Spain). Such a network allows soil water and plant status to be monitored. The UAV (capable of executing missions defined by an administrator) is useful for acquiring visual information in an autonomous manner (under operator supervision, if needed). The system performance has been analysed and results that demonstrate the benefits of using the proposed architecture are detailed.