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By exposing students to the concept of digital agriculture earlier in their lives, they will be able to develop the proper mindset to advance the field further when they enter the professional world.
In order to feed the world’s growing population, farmers will need to produce 70% more food by 2050 than they did in 2006 (Bruinsma, 2009). To meet this demand, farmers and agriculture companies are turning to Internet of Things (IoT) technologies and data visualization to optimize analytic capabilities and, ultimately, enhance their production through digital agricultural practices (Jayaraman, Palmer, Zaslavsky, & Georgakopoulos, 2015). However, few students are given the opportunity to explore the potential and impacts of modern “digital” agriculture during their educational experience. Therefore, this article will provide an example instructional activity combined with the principles of IoT technology and agriculture that could be used or mimicked to present students with an advanced look at an essential field related to food production and the growing population. Specifically, the instructional context of this lesson was developed to be situated within the Grand Engineering Challenge of Managing the Nitrogen Cycle (National Academy of Engineering, 2019). The activities of this lesson directly relate to this Grand Engineering Challenge because students will develop a means of surveying farmland for nitrogen deposits and explore ways for farmers to better manage their crop production. This exercise will also enhance the rigor of engineering design and provide socially connected relevance to learning. Digital agriculture is an idea in which many students around the world, and in the Midwest U.S. specifically, can find interest, as they may be surrounded by agriculture in multiple forms. By exposing students to the concept of digital agriculture earlier in their lives, they will be able to develop the proper mindset to advance the field further when they enter the professional world. The challenge included in this lesson centers on students designing and programming a robot to monitor a field for nitrogen deposits with the intent of optimizing fertilization practices.
Agricultural Advances and Fertilization of Crops
Without advances in agricultural practices and technology, humanity’s ability to produce enough food for the entire population would have fallen short millennia ago (Zimdahl, 2015). Settled agriculture, machine-assisted practices, and the introduction of science and chemical engineering have all improved the overall yield of agricultural production. While some are quick to point out the adverse effects of these advances (Foley, DeFries, Asner, Barford, Bonan, Carpenter…& Helkowski, 2005), in his book, Six Chemicals that Changed Agriculture, Zimdahl (2015, pg. 188) closes by arguing that without such advances in agriculture, humans would have run short of space and food long ago:
“...barring a worldwide disaster, the human population will continue to increase for several decades. There is no land for agriculture to expand. That is not news to international organizations, most countries, nongovernmental organizations, companies engaged in agriculture, faculty of Colleges of Agriculture, and farmers. It may be a revelation to the vast majority of people who don’t farm. After all, how can there be a problem, the grocery store is always full.”
History of Agricultural Fertilization
People have known for more than 2000 years that the addition of certain substances to soil improved the yield of plants (Ganzel & Reinhart, 2019). The addition of manure, bird droppings, and even ground-up human bones, have all been used in an attempt to improve the production of farmland (Maxwell, 2014). Even without a full understanding of which nutrients were needed, and why these might be effective, individuals in Europe were encouraged during World War I to save the fat and bones from their food for use in fertilizer (Figure 1).
The use of human bones—and other less-than-desirable substances—to improve agricultural yield has not always been met with optimism. For example, several newspapers from the 1800s include commentary surrounding the ethical, moral, and political ramifications of using bones (some of which were reportedly dug up from battlefields, tombs, and churches) as fertilizer (Maxwell, 2014). For example, the Westmorland Gazette (16 November 1822) records:
“It is estimated that more than a million bushels of human and unhuman bones were imported last year from the Continent of Europe...It is certainly a singular fact, that Great Britain should have sent out such multitudes of soldiers to fight the battles of the country upon the Continent of Europe, and should then import their bones as an article of commerce to fatten her soil.”
Despite the storied history of fertilization, today the most common nutrients added to soil to increase agricultural yield are less controversial as they are chemically synthesized (Ganzel & Reinhart, 2019). Nitrogen, which is pulled from the soil by plants during growth, can be added back to the soil artificially through fertilizers. The addition of this fertilizer not only results in higher yields, but also provides less of a need to rotate crops (Phoslab, 2013). Originally the process of fertilizing plants with nitrogen was quite dangerous, as nitrogen is one of the main ingredients in explosives and often resulted in accidental explosions during shipping or application (Ganzel & Reinhart, 2019). This danger was in part due to the form of nitrogen, which was originally applied as pellets on the surface; however, as this method was highly dangerous, innovations were attempted until a new approach was developed wherein nitrogen was applied directly beneath the surface of the field and then covered with new soil (CropNutrition, 2019)—a practice still in use today.
Even with improved safety around the application of nitrogen, there remain looming concerns with the impacts of nitrogen fertilization on the environment. Specifically, nitrogen run-off from fertilization has been linked with “dead zones” where aquatic life cannot survive. Relatedly, nitrogen production has been linked with global warming and the associated consequences (Elliott, 2019). These concerns suggest that additional effort may be needed to satisfy the demands of a growing population while also balancing concerns around environmental damages.
UAVs, Data Visualization, and Agriculture
Within recent years there has been a surge in the popularity and prevalence of Unmanned Aerial Vehicles (UAVs), often referred to as drones, for both personal and commercial purposes (Strimel, Bartholomew, & Kim, 2017). This trend has been attributed to the technological advancements that have resulted in easier piloting and lower costs for production (AUVSI, 2013). As these UAV capabilities continue to advance, so does their application across a variety of industries. Specifically, UAVs are revolutionizing the agriculture industry, as UAV imaging capabilities are providing farmers with effective and more cost-efficient tools to track important conditions such as field drainage, crop damage and disease, and nitrogen deficiencies in soil. The sophisticated imaging and sensing technologies with which UAVs are now equipped, in combination with advanced IoT capabilities, have now enabled farmers to harness the data revolution and achieve what is now known as “digital agriculture.” Within this new world of digital agriculture, UAVs can collect and process data in a way that facilitates informed decisions and control of agricultural equipment to boost crop yields, minimize waste in practices such as irrigation and fertilization, and ultimately increase a farm’s profitability. While increasing profitability is, of course, a benefit to the industry, the societal implications can be extremely critical, as climate change, a boom in overpopulation, and other factors pose a threat to the current food supply and production levels.
One key practice toward effectively harnessing the UAV-collected data is the data-visualization process. Data visualization is the process of representing data with the help of graphs and other visual representations, which can support individuals in analyzing complex data (Mittal, Khan, Romero, & Wuest, 2018). It is intended to clearly convey and communicate information through graphical means, enabling end users to comprehend data in a much more explicit fashion (Fry, 2008; Lee, Butavicius, & 2003). Visualization is important when working with sensor data (Kubicek, Kozel, Stampach, & Lukas 2013; VanWijk, 2005). When properly selected, this approach makes working with the data more comfortable for the user, and the data can be understood more quickly and easily (Kubicek, et al., 2013; Wachowiak, Walters, Kovacs, Wachowiak-Smolíková, & James, 2017). With suitable visualization, it is possible to find patterns, connections, or similarities in observed agricultural data (Dvorsky, Snasel, & Vozenilek, 2010). This makes it much more convenient than the manual analysis of raw sensor data, which is oftentimes difficult for a person to understand (Kubicek, et al., 2013).
Sensor data usually exist as numerical values; therefore, the process of understanding or analyzing them is not trivial (Kubicek, et al., 2013). In many cases, finding patterns, differences, and commonalities is hardly possible without deeper analysis or visualization. Visualization of agriculture data (Hashem, Yaqoob, Anuar, Mokhtar, Gani, & Ullah, 2015) from sensor observations can be utilized to provide insight into what the data represents, making it easier to understand and interact with the data (Fry, 2008; Richter, 2009).
Technology and Engineering Classroom Connections
Can recent advances in technology such as UAV imaging, satellite/sensor data collection, and IoT/GPS-enabled machinery be used to engage students meaningfully in tackling such a problem? For example, can we task students with using technology, sensors, and data to devise a new solution to more effectively and efficiently apply nitrogen fertilizer? We present here a lesson plan, which challenges students to solve the problem of excess nitrogen fertilization—collected in water runoff—through advances in UAV technology, data visualization, automation, sensing, and control. Specifically, this lesson challenges students to use technology tools to collect data, visualize patterns, and make informed design decisions (Figure 2).
As students work together in teams to improve the targeted application processes and procedures of farm equipment (e.g., tractor) for nitrogen fertilizers with an intent of maintaining crop production while also protecting waterways and the environment (Figure 3), they can become intrinsically connected to this socially relevant activity.
Engaging students in activities such as this, which center on the Grand Engineering Challenges and socially relevant contexts, may provide new opportunities for engaging and inspiring students to make connections, think critically, and excel in creative opportunities. Further, enhancing TEE classrooms by drawing on the historical underpinnings and the subsequent technological advancements may increase student’s interest in, and connection to, such topics.
NOTE: Lesson plans for this activity are located below.
Bruinsma, J. (2009). The resource outlook to 2050: By how much do land, water, and crop yields need to increase by 2050? Paper presented 24-26 June, 2009 at the Expert Meeting on How to Feed the World in 2050, Food and Agriculture Organization of the United Nations, Economic and Social Development Department. Retrieved from www.fao.org/3/ak542e/ak542e06.pdf
CropNutrition. (2019). Nitrogen in plants. Retrieved from www.cropnutrition.com/efu-nitrogen
Dvorský, J., Snášel, V., & Vořenílek, V. (2010). On maps comparison methods. Presented at the 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM) in Krakow, Poland, (pp. 557-562). IEEE.
Elliott, G. (n.d.). The effects of fertilizers & pesticides. Retrieved from www.livestrong.com/article/139831-the-effects-fertilizers-pesticides/
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., ... & Helkowski, J. H. (2005). Global consequences of land use. Science, 309(5734), 570-574.
Fry, B. (2008). Visualizing data. Sebastopol, California: O'Reilly Media.
Ganzel, B., & Reinhardt, C. (n.d.). Fertilizer explodes. Retrieved from https://livinghistoryfarm.org/farminginthe40s/crops-3/fertilizer-explodes/
Grubbs, M. E. & Strimel, G. (2015). Engineering design: The great integrator. Journal of STEM Teacher Education, 50(1), 77-90.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information systems, 47, 98-115.
Jayaraman, P. P., Palmer, D., Zaslavsky, A., & Georgakopoulos, D. (2015). Do-it-yourself digital agriculture applications with semantically enhanced IoT platform. Presented at 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) (pp. 311-316).
Kubicek, P., Kozel, J., Stampach, R., & Lukas, V. (2013). Prototyping the visualization of geographic and sensor data for agriculture. Computers and Electronics in Agriculture, 97, 83-91.
Lee, M. D., Butavicius, M. A., & Reilly, R. E. (2003). Visualizations of binary data: A comparative evaluation. International Journal of Human-Computer Studies, 59(5), 569-602.
Mas, J. (2013). How does nitrogen help plants grow? Retrieved from www.phoslab.com/how-does-nitrogen-help-plants-grow/
Maxwell, M. (2014). Fertilisers for turnips and the great British backbone. Retrieved from http://dustyheaps.blogspot.com/2014/07/fertilisers-for-turnips-and-great.html
Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2019). Smart manufacturing: Characteristics, technologies and enabling factors. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233(5), 1342-1361.
Museum of London. (2019). Bone and Fat Bucket Poster. S.H. Benson Limited. Retrieved from www.20thcenturylondon.org.uk/mol-nn20602
National Academy of Engineering [NAE]. (2019). Grand engineering challenges. Retrieved from www.engineeringchallenges.org/
NSTA. (2019). Science and engineering practices. Retrieved from NGSS@NSTA: https://ngss.nsta.org/PracticesFull.aspx
Phoslab. (2013). How does nitrogen help plants grow? Retrieved from www.phoslab.com/how-does-nitrogen-help-plants-grow/
Richter, C. (2009). Visualizing sensor data. In Media Informatics Advanced Seminar on Information Visualization.
Strimel, G. J., Bartholomew, S. R., & Kim, E. (2017). Engaging children in engineering design through the world of quadcopters. Children’s Technology and Engineering, 21(4), 7-11.
The Mosaic Company. (n.d.). Nitrogen. Retrieved from www.cropnutrition.com/efu-nitrogen
Van Wijk, J. J. (2005). The value of visualization. In VIS 05. IEEE Visualization, 2005. (pp. 79-86). IEEE.
Wachowiak, M. P., Walters, D. F., Kovacs, J. M., Wachowiak-Smolíková, R., & James, A. L. (2017). Visual analytics and remote sensing imagery to support community-based research for precision agriculture in emerging areas. Computers and Electronics in Agriculture, 143, 149-164.
Zimdahl, R. L. (2015). Six chemicals that changed agriculture. Academic Press. Retrieved from www-sciencedirect-com.ezproxy.lib.purdue.edu/book/9780128005613/six-chemicals-that-changed-agriculture#book-info
Scott Bartholomew, Ph.D., is an assistant professor of Engineering/Technology Teacher Education at Purdue University, West Lafayette, IN. He can be reached at firstname.lastname@example.org.
Greg J. Strimel, Ph.D., is an assistant professor of Technology Leadership and Innovation and the coordinator of the Design & Innovation Minor at Purdue University. He can be reached at email@example.com.
Vetria Byrd is an assistant professor of Computer Graphics Technology and Director of the Byrd Data Visualization Lab in the Purdue Polytechnic Institute at Purdue University Campus in West Lafayette, Indiana. She can be reached at firstname.lastname@example.org.
Vanessa Santana an undergraduate Engineering/Technology Teacher Education student at Purdue University. She can be reached at email@example.com.
Jackson Otto is an undergraduate Engineering/Technology Teacher Education student at Purdue University. He can be reached at firstname.lastname@example.org.
Zach Laureano is an undergraduate Engineering/Technology Teacher Education student at Purdue University. He can be reached at email@example.com.
Brian DeRome is an undergraduate Engineering/Technology Teacher Education student at Purdue University. He can be reached at firstname.lastname@example.org.
This is a refereed article.
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