Benefits of Virtual Reality to Agricultural Education and Research Communities

Fox, Amelia A.A.A., D. Carruth, and S. Deb. 2018.

‘Millennials’ (born after the late 1980’s) have learning styles that matured in technologically oriented environment (Junco and Mastrodicasa, 2007; Ryan, 2007; Strauss and Howe, 2009). Technology in classroom has slowly evolved towards digital media, providing student’s information through sound, photos, videos, and using interactive 360° virtual media (Rose and Meyer, 2002). For successful instructor conveyance of message in virtual media to students, the correct tools and visual language must be selected (Carruth, 2017; Deb et al., 2017). The secondary entertainment factor helps capture the student's attention, and connects them playfully with topics and content (Velev and Zlateva, 2017). Engagement is especially important when introducing trainees to high-risk environments that are unattainable in real-world environments (Deb et al, 2017). O’Connor et al. (2018) found students, in multiple VR user environments who manipulated complex theoretical molecular models, learned tasks more quickly than if through conventional educational means. Even users with little or no VR experience were able to accomplish modeling tasks at accelerated rates. The study benefits appear to transfer to multiple users who were engaged and co-located. Virtual reality training provides a framework that is complementary to research activities aimed at enticing users to discover, design, and create.

Laurel (2016) proposed technologies are augmentations of human intention and that these technologies can be committed for purposes of good, especially if the technology allows meaningful insight into a practice that cannot otherwise be simulated or experienced. Virtual training experiences generate measurable gains in performance and experience especially when tools are accessible by users with appropriate but not necessarily costly technology (Carruth, 2017). While inside a VR training experience, the trainee’s accuracy of performance, response to actions required, and task completion time are recorded metrics, which subsequently permit post-training analysis of training content and its validity in an instructional system. Although agriculture education attempts to synthesize and summarize experience through traditional curriculum, the classroom effect may forestall true learning due to the lack of hands-on exposure to farming elements (Roberts, 2006).

Needed in agriculture education is an approach that moves beyond theoretical-experiential models and onto models that integrate real-world experiences with traditional learning approaches. A pedagogic cycle of a) initial training focus, b) interaction with a virtual phenomenon, c) creating generalizations about an experience, and then d) testing those generalizations is attainable in a Conventional-plus-AR/VR coursework and research environment. Although an educational pedagogy ranks higher in impact than single technology interaction in post-secondary education, benefits are gained through VR approaches when cognitive support tools simulate high-risk training experiences (Schmid et al., 2014). Virtual-reality instructional support tools can be programmed to increase exposure to unscripted conditional training, and thereby yield superior training outcomes.

Emerging Need for Environmentally-Controlled Agriculture Training and Decision Support Tools

Fox, Amelia A.A.A., D. Carruth, and S. Deb. 2018.

For the past two decades, improving farm-production efficiencies by reducing inputs and environmental perturbations has been at the forefront of agriculture technology development. “Precision Agriculture” computational tools provide framework to manage resources and improve yields in relatively uncontrollable field landscapes (Brevik et al., 2016). Machine-to-Machine communications supported by intelligent interfaces shows promise to advance and boost food production (Ray, 2017). With decreasing access to locally grown foods in large urban environments, food justice and parity may only be possible if urban farm projects are established on building rooftops and abandoned industrial lands near cities (Horst et al., 2017). Thus, a new STEM precision agriculture is borne in controlled environment agriculture (CEA).

There is a need to develop CEA training tools in order to cultivate a rapid innovation-learning paradigm that supports CEA food production on Earth and beyond. The 1967 Outer Space Treaty promotes plans to occupy lunar and interplanetary landscapes, and establish farm systems that emulate Earth models under CEA production (Chang, 2017). Nutrient recycling, oxygen production, and increased water use efficiency through CEA biomass production are critical outcomes in maintaining food production sustainability (Sprecht et al., 2014; Wheeler, 2017).

Environmentally controlled greenhouse and poultry agriculture production has risen steadily since 2007 (USDA, 2017). In 2012, roughly 54,500 nursery, greenhouse, and floriculture specialty crop farms produced over $19 billion market value of products sold; a 47% increase in value of products from 2007 (Vilsack, 2015). The majority of greenhouse production facilities were family or individually owned yet only 25% of operators were female and ethnic diversity of greenhouse/nursery operators was low. Foreign competition has placed increased pressure on American horticulture to adopt more energy efficient production methods.