Abstract:

The project described in this paper builds on exciting technological developments in real time biophysical data gathering that are currently happening at The University of Queensland (UQ) ’s regional campus (UQGatton ~1000ha of prime agricultural land ~85km SW of Brisbane in SE Queensland), via the A$650K Internet of Things (IoT) UQ Smart Campus Project, and the A$1.7M Darbalara Beef Handling Training Centre Project.

The Internet of Things ‘Smart Campus’ project at UQ commenced in December 2014 and provides a wireless enabled multisensor mesh network measuring every biophysical variable useful to agricultural information systems (and including remotely located remote cameras for imagery collection). It is currently designed to encompass everywhere on UQGatton that is covered by WiFi (e.g. Cattle backgrounding paddocks and Feedlot, Horticultural fields, Equine Foaling unit and collocated equine paddocks, Wildlife enclosure, Piggery, Dairy and the “Built Environment” (particularly for radiance, dust, noise etc)). The mesh network also includes water level and chemical content of the Farm ring tanks, dams and the Piggery effluent and associated water management lake, Lake Galletly. Data is collected continuously in real time (a virtual server is set up through UQ Information Technology Services with database and online GIS currently accepting data for further data streaming.

The ‘Plug and Play’ technology currently being used comes out of the company Libelium (http://www.libelium.com/products/waspmote/) and enables the customisation of data gathered and the use of the data gathered as a “Big Data” resource for any teaching, research, industry or sustainability monitoring that might be required. It also allows upgrading of the network to extend the network and/or create a denser mesh for data collection at minimal cost and reduced implementation issues.

This paper will describe a Teaching & Learning Project to develop multifaceted web-based interfaces (including mobile phone apps and problem based learning modules) using the real time streamlining data acquired through the IoT technology. The idea is to produce innovative teaching and assessment modules for (in this initial project), 33 courses (16 Academics) across 9 Degree Programs in the UQ Science Faculty, and across 2 Campuses separated by 85kms of highway.

The Academics’ interests encompass a wide variety disciplines (for example Agronomy, Agribusiness, Equine Science, Animal Production (various aspects), Maths and Statistics, Waste water science and management, Soil science, Chemistry, Wildlife Monitoring, Animal Reproduction, Sustainability monitoring, Plant pathology, etc etc). An unexpected benefit is that while most academics started their involvement with the project with a very discipline focus, it is pleasing to note that they are now all talking about integration across the broad spectrum of content to better enable students to see the relevance of an individual course in the context of their whole Program/ learning experience.

Challenges, work-arounds and some examples of “live” problem based learning examples using the data will be discussed with a final section on thoughts as to what these technologies could provide for further Teaching & Learning developments in the “E” space – particularly in terms of potential collaborations with other academic institutions that might find freely accessible subtropical agriculture information useful for their own teaching.

Keywords:

Internet of Things, Real Time, Biophysical data, Problem based, Learning Modules.