ArticlePDF Available

Teaching Engineering through Socially-relevant Contexts: Using data to Improve Precision in Crop Fertilization through Digital Agriculture

Authors:

Abstract and Figures

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 which 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 that many students around the world, and in the Midwest United States specifically, can find interest in, 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.
Content may be subject to copyright.
Bartholomew, S. R., Strimel, G. J., Byrd, V., Santana, V., Otto, J., DeRome, B., & Laureano, Z. (2020).
Teaching through socially-relevant contexts: Using data to improve precision in crop fertilization through
digital agriculture. Technology & Engineering Teacher, 79(7), 32-36.
Teaching Technology & Engineering Concepts through Socially Relevant Contexts:
Using Data to Improve Precision in Crop Fertilization through Digital Agriculture
Introduction
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 which
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 that many students around the
world, and in the Midwest United States specifically, can find interest in, 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 & 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
Bartholomew, S. R., Strimel, G. J., Byrd, V., Santana, V., Otto, J., DeRome, B., & Laureano, Z. (2020).
Teaching through socially-relevant contexts: Using data to improve precision in crop fertilization through
digital agriculture. Technology & Engineering Teacher, 79(7), 32-36.
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 1 to save the fat and bones from their food for use in fertilizer (see Figure 1).
Figure 1. Bone & Fat Bucket (Museum of London, 1917-1918).
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
Bartholomew, S. R., Strimel, G. J., Byrd, V., Santana, V., Otto, J., DeRome, B., & Laureano, Z. (2020).
Teaching through socially-relevant contexts: Using data to improve precision in crop fertilization through
digital agriculture. Technology & Engineering Teacher, 79(7), 32-36.
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.
Despite improving the safety around the application of nitrogen, there remains 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 additional effort may be needed to satisfy
the demands of a growing population while also balancing concerns around environment
damages.
UAVs, Data Visualization, and Agriculture.
Within recent years there has been 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 that UAV’s are now equipped with, 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 which 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 the over population, 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,
Bartholomew, S. R., Strimel, G. J., Byrd, V., Santana, V., Otto, J., DeRome, B., & Laureano, Z. (2020).
Teaching through socially-relevant contexts: Using data to improve precision in crop fertilization through
digital agriculture. Technology & Engineering Teacher, 79(7), 32-36.
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 & 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.
Figure 1. Students work with collected data to visualize patterns and make decisions
Bartholomew, S. R., Strimel, G. J., Byrd, V., Santana, V., Otto, J., DeRome, B., & Laureano, Z. (2020).
Teaching through socially-relevant contexts: Using data to improve precision in crop fertilization through
digital agriculture. Technology & Engineering Teacher, 79(7), 32-36.
Figure 2. Students test a design solution aimed at controlling fertilizer application based on data
visualization of collected data samples
Table 1 Digital Agriculture Lesson Overview
Lesson Purpose: According to the United Nations Food & Agriculture Organization, the world will need to produce 70%
more food in 2050 than it did in 2006 in order to feed the growing population of the Earth. 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).. Therefore, the goal of this lesson is to combine the principles of IoT technology and agriculture
in order to present students with an advanced look at an essential field. Digital agriculture is an idea that many students
around the world, and in the Midwest United States specifically, can find interest in, 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.
Engineering Core Concepts & Sub-Concepts:
Computational Thinking
Programming and Algorithms
Data Collection, Analysis, & Communication
Techniques of Data Collection
Data-Driven Decisions
Creating Graphs and Technical Documents
Reporting Data
Estimation
Socially Relevant Context: This lesson has been developed to be situated within the Grand Engineering Challenge of
Managing the Nitrogen Cycle. The activities of this lesson directly relate to this Grand Engineering Challenge as students
develop a means of surveying farmland for nitrogen deposits and explore ways for farmers to better irrigate and manage
their crop production.
Advanced technologies in agriculture have become increasingly common, and specifically related to STL Standard
5: Benchmark I, the use of technologies to monitor the environment has greatly increased and people are creating
new methods of monitoring the world.
An example of agricultural technologies advancing overall can be found here https://agribotix.com/
Engineering design is a consistent necessity in the modern world, so by teaching students about this process, they
will be able to more fluidly develop their abilities to assess situations and find proper means of solving real world
issues.
Bartholomew, S. R., Strimel, G. J., Byrd, V., Santana, V., Otto, J., DeRome, B., & Laureano, Z. (2020).
Teaching through socially-relevant contexts: Using data to improve precision in crop fertilization through
digital agriculture. Technology & Engineering Teacher, 79(7), 32-36.
Connected STEM Standards:
STL Standards
Standard 5: Students will develop an understanding of the effects of technology on the environment.
Benchmark I. With the aid of technology, various aspects of the environment can be monitored to provide
information for decision-making
Standard 15: Students will develop an understanding of and be able to select and use agricultural and related
biotechnologies.
Benchmark K. Agriculture includes a combination of businesses that use a wide array of products and
systems to produce, process, and distribute food, fiber, fuel, chemical, and other useful products.
Scientific and Engineering Practices - Based on the National Science Teachers Association
Analyzing and Interpreting Data
Scientific investigations produce data that must be analyzed in order to derive meaning. Because data
patterns and trends are not always obvious, scientists use a range of toolsincluding tabulation, graphical
interpretation, visualization, and statistical analysisto identify the significant features and patterns in the
data. Scientists identify sources of error in the investigations and calculate the degree of certainty in the
results. Modern technology makes the collection of large data sets much easier, providing secondary sources
for analysis.”
Constructing Explanations and Designing Solutions
“The products of science are explanations and the products of engineering are solutions.”
Obtaining, Evaluating, and Communicating Information
“Scientists and engineers must be able to communicate clearly and persuasively the ideas and methods they
generate. Critiquing and communicating ideas individually and in groups is critical professional activity.”
Learning Objectives:
When provided with information about local agriculture, I can identify potential benefits of digital agriculture
applications within the surrounding community.
Given a dataset, I can program a microcontroller to read data to control a model of a digital agricultural system to
perform a task more efficiently.
I can describe and provide examples for how technology can be used to monitor the environment.
Enduring Understanding(s):
The use of technology can enhance the process of organizing, running, and executing agricultural processes.
With the aid of technology, various aspects of the environment can be monitored to provide information for
decision-making.
Driving Question(s):
How does the use of technology enhance the process of organizing, running, and executing agricultural processes
when it comes to traditional farming practices?
How can we use technology to monitor the environment?
Career Connections:
Precision Agriculture Specialist: Precision Ag Specialist combine computer technology with GPS and mapping
software to record information.
Agricultural Engineer: Engineering with a focus on implementing new technologies to improve agricultural
equipment and infrastructure.
Table 2 Digital Agriculture Lesson Plan
Engage: Sets the context for what the students will be learning in the lesson, as well as captures their interest in the topic
by making learning relevant to their lives and community.
Bartholomew, S. R., Strimel, G. J., Byrd, V., Santana, V., Otto, J., DeRome, B., & Laureano, Z. (2020).
Teaching through socially-relevant contexts: Using data to improve precision in crop fertilization through
digital agriculture. Technology & Engineering Teacher, 79(7), 32-36.
Begin by having drones charged and prepared to use for the lesson.
Set the Context: tell students they will later be creating machines to survey land. Introduce the ideas of the
lesson.
“How many of you have heard of the grand engineering challenges?”
A team of the world’s greatest engineers, scientists, mathematicians, and more formed a committee to
define the greatest challenges of our generation. Just like during the stone age when metal was the
biggest challenge to accomplish, to the 20th century when the internet and accessible electricity grew.
Each new generation adapts and needs new and innovative technology and ours is no different. There
are 14 challenges that were decided upon, ranging from virtual reality to urban infrastructure. But today
we are going to focus on how to manage the nitrogen cycle.
Be sure to highlight the Grand Engineering Challenge of Managing the Nitrogen Cycle, and how it will drive our
day’s lesson and challenge.
Provide students with the context of the lesson by making the challenge relevance to them.
Some potential contexts to include:
Farmers have to deal with things like the nitrogen cycle all the time, and as most of you can tell,
living in Indiana there are a lot of farming communities surrounding us at most times.
One of the hot topics in farming in today’s world is Digital Agriculture, which is defined as ICT
(Information and Communication Technology) and data ecosystems to support the development
and delivery of timely, targeted (localized) information and services to make farming profitable
and sustainable (socially, economically and environmentally) while delivering safe, nutritious and
affordable food for ALL.
Discuss with students that there are a variety of technologies used in digital agriculture. One example includes
the use of drones, which is commonly used to document the land and aspects of the soil more effectively!
Demonstrate how to fly the drone and allow students to try to fly them too!
Note: Although there is no specific challenge for students to complete, we want them to experience flying a drone.
As students fly the drones, have them consider the following:
How could drones be incorporated into digital agriculture?
What could you add to a drone to make it usable for digital agriculture?
Explore: Enables students to build upon their prior knowledge while developing new understandings related to the topic
through student-centered explorations.
Have students come together to discuss their thoughts and answers.
Prompt students: “How many of you have heard about digital agriculture before today?”
We want students to realize that agriculture plays a large role in all of our lives, even if we don’t live near fields. Just
think of everything we see at a grocery store!
Transition students to the next activity.
Assign students into groups of two or three.
Students will then be asked to consider how technology can be incorporated into digital agriculture. You can
allow students to seek out resources such as books, news articles or online material.
After the allotted time, have the class come back together and share.
Have students discuss:
What did you already know?
Did anything you find about digital agriculture surprise you?
Bartholomew, S. R., Strimel, G. J., Byrd, V., Santana, V., Otto, J., DeRome, B., & Laureano, Z. (2020).
Teaching through socially-relevant contexts: Using data to improve precision in crop fertilization through
digital agriculture. Technology & Engineering Teacher, 79(7), 32-36.
What are some of the ideas that you all came up with?
Explain: Summarizes new and prior knowledge while addressing any misconceptions the students may hold.
After the discussion take time to provide the context of the lesson and transition students to the continuation of
the unit on Digital Agriculture.
“Today we will focus on The Nitrogen Cycle, and the importance it has for the growth of plants and animals.
We will also review Arduino programming. This lesson will aid you as you begin to program your robots to
“survey a field” for nitrogen levels.
Teacher will then provide background information by reviewing a PowerPoint explaining the nitrogen cycle
more in depth.
Take time to emphasize the importance of nitrogen for growth of plants and animals.
Transition to the introduction of Arduino programming and the basic commands.
Review the provided Arduino Reference Guide with students linked here:
https://drive.google.com/open?id=104o4zJ43_-WbSv7l1dIrmPCePo4yPJA2vhy8rZ1BWJg
The sheet highlights the following commands:
pinMode
digitalRead
digitalWrite
delay
Note: You can have students view this link (https://www.arduino.cc/reference/en/) instead of providing a handout,
however be sure to discuss the commands above as they will be essential during the challenge!
Explain to students that they will be tasked with programming a robot to read collected data in order to
distribute the proper amount of fertilizer (rice) in the hopes of reducing the use of excess fertilizer in local
fields. Students are expected to use collected data, just as Agricultural Engineers would when implementing
new technologies to improve agricultural equipment and infrastructure!
Take time to review directions, answer questions and clarify any misconceptions students may have.
Engineer: Requires students to apply their knowledge and skills using the engineering design process to identify a
problem and to develop/make/evaluate/refine a viable solution.
Begin by having premade robots, laptops equipped with Arduino, and the skeleton program ready to use.
Provide students with the handout of the design challenge that includes the background and constraints, as
well as the skeleton program to work off of linked here:
https://drive.google.com/open?id=1gXlSezETFnPlBXxi5OO8QQ8YMCbztrcw
Electronic Materials
Elegoo Uno R3 Starter Kit: https://www.amazon.com/ELEGOO-Project-Starter-Tutorial-Arduino/dp/B01D8KOZF4
Wingoneer Color Sensor: https://www.amazon.com/WINGONEER-TCS3200-Detector-Recognition-
Arduino/dp/B06XHL79SQ
Bartholomew, S. R., Strimel, G. J., Byrd, V., Santana, V., Otto, J., DeRome, B., & Laureano, Z. (2020).
Teaching through socially-relevant contexts: Using data to improve precision in crop fertilization through
digital agriculture. Technology & Engineering Teacher, 79(7), 32-36.
In this activity (Nutrient Deficient Farmland Design Brief provided in Table 3 with a full copy found here:
https://drive.google.com/open?id=1zzWviWWSlkRjrRxa5jt5O592M7zQmUoovsiQ4cyyNLA) students will
be challenged to program a model robot that will “survey a field” for nitrogen levels.
The robot must snake through the field and when it comes across areas of low nitrogen/nutrients, it
must drop a nitrogen supplement to benefit the soil health.
Take time to address any lingering questions students may have.
Place students into groups (max of 4 students per group) and allow them to begin working!
Students should be able to use the skeleton program and the Arduino reference manual to program the robot.
As students are working, be sure to walk around and facilitate discussions with students in order to ensure they are
on the right track.
This activity will be challenging for some students, so be sure to reference the Arduino manual and consider
how you may aid them to think logically through what their robot needs to do.
If needed, take time to review the skeleton code with students. Students should only have to change certain
variables for measuring the colors, the speed of the robot’s motion, and the robot’s response to detecting low
nitrogen levels.
If students continue to struggle, be sure to help them work through the logic process. Rather than giving them the
answers, guide them to the right ideas.
As an added challenge for advanced students (or just for fun!), have them adjust their project to detect water
(this would be done using a different map with a different color scheme). This added activity would provide
students with an opportunity to learn about and work with the color sensor.
Evaluate: Allows a student to evaluate hers or his own learning and skill development in a manner that enables them to
take the necessary steps to master the lesson content and concepts.
As the class nears an end, call the students together and have them present what they have accomplished.
Have groups take turns running their programs and having their robot perform the task. As they are preparing
to test their robot, have students give a brief description of their process of achieving the goal and how they
went about finding the solution.
Student’s presentations are an informal assessment primarily for the purpose of having them vocalize what they
should have learned and to provide the instructor with feedback of the ideas presented in the lesson.
Students will be evaluated on the success of their robot’s performance and their short presentation.
Include rubric for proper grading methods
Note. Lesson format adapted from Grubbs & Strimel (2015).
Bartholomew, S. R., Strimel, G. J., Byrd, V., Santana, V., Otto, J., DeRome, B., & Laureano, Z. (2020).
Teaching through socially-relevant contexts: Using data to improve precision in crop fertilization through
digital agriculture. Technology & Engineering Teacher, 79(7), 32-36.
Table 3 Engineering Design Challenge
Conclusion
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
Bartholomew, S. R., Strimel, G. J., Byrd, V., Santana, V., Otto, J., DeRome, B., & Laureano, Z. (2020).
Teaching through socially-relevant contexts: Using data to improve precision in crop fertilization through
digital agriculture. Technology & Engineering Teacher, 79(7), 32-36.
subsequent technological advancements may increase student’s interest in, and connection to,
such topics.
References
Bruinsma, J. (2009). The resource outlook to 2050: by how much do land, water and crop yields
need to increase by 2050. In Expert meeting on how to feed the world in (Vol. 2050, pp.
24-26).
CropNutrition. (2019). Nitrogen in Plants. Retrieved on July 9, 2019 from
https://www.cropnutrition.com/efu-nitrogen
Dvorský, J., Snášel, V., & Vořenílek, V. (2010). On maps comparison methods. In 2010
International Conference on Computer Information Systems and Industrial Management
Applications (CISIM) (pp. 557-562). IEEE.
Elliott, G. (n.d.). The Effects of Fertilizers & Pesticides. Retrieved from
https://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. In 2015 IEEE
Tenth International Conference on Intelligent Sensors, Sensor Networks and Information
Processing (ISSNIP) (pp. 1-6). IEEE.
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
https://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 on
July 9, 2019 from: http://www.20thcenturylondon.org.uk/mol-nn20602
National Academy of Engineering [NAE]. (2019). Grand Engineering Challenges. Retrieved on
July 9, 2019 from http://www.engineeringchallenges.org/
Bartholomew, S. R., Strimel, G. J., Byrd, V., Santana, V., Otto, J., DeRome, B., & Laureano, Z. (2020).
Teaching through socially-relevant contexts: Using data to improve precision in crop fertilization through
digital agriculture. Technology & Engineering Teacher, 79(7), 32-36.
NGSS. (2019). Science and Engineering Practices. Retrieved on July 9, 2019 from
https://ngss.nsta.org/PracticesFull.aspx
Phoslab. (2013). How does nitrogen help plants grow? Retrieved on July 9, 2019 from
https://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 & Engineering, 21(4), 7-11
The Mosaic Company. (n.d.). Nitrogen. Retrieved from https://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 https://www-sciencedirect-com.ezproxy.lib.purdue.edu/book/9780128005613/six-
chemicals-that-changed-agriculture#book-info
Article
Full-text available
The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of ‘‘semantic similarity’’ to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a set of five defining characteristics, 11 technologies and three enabling factors that are considered relevant for the smart manufacturing scope. This article then evaluates the derived structure by matching the characteristics and technology clusters of smart manufacturing with the design principles of Industry 4.0 and cyber-physical systems. The authors aim to provide a solid basis to start a broad and interdisciplinary discussion within the research and industrial community about the defining characteristics, technologies and enabling factors of smart manufacturing.
Article
Full-text available
The interest in engineering education for K-12 students has been rising (Carr, Bennett IV, & Strobel, 2012; Grubbs & Strimel, 2015; Strimel, Grubbs, & Wells, 2016), and the importance of engineering education is discussed as early as the elementary school level (Hegedus, 2014). Petroski (2003) claims that children are ready to learn engineering because their play activities are similar to engineering and design activities, such as making, moving, and rearranging things. Studies have examined how elementary school students perceive engineering or engineers (Cunningham, Lachapelle, & Lindgren-Streicher 2005) and found that elementary-aged students associated engineering with repairing, installing, driving, constructing, and improving machines and devices. Similarly, Capobianco, Diefes-Dux, Mena, and Weller (2011) found that elementary school students in grades 1 through 5 perceive engineering as fixing, building, making, and using vehicles, engines, and tools. However, Capobianco et al (2011) found that students in grades 4 and 5 could recognize that engineering activities include designing—a hallmark characteristic of engineering (Dym, Agogino, Eris, Frey, & Leifer, 2005). In that context, engineering education is important to elementary-aged students because engineering design-based learning can help younger students to expand their limited perceptions of engineering beyond just using, fixing, and improving things (Cunningham & Hester, 2007) to include the practices of informed design (Grubbs & Strimel, 2015). Engineering activities can also help students foster teamwork and collaboration skills as they work together in open-ended design environments (Hammack, Ivey, Utley, & High, 2015). Furthermore, engineering learning activities can support children to acquire abilities to understand problems, plan and develop solutions, and share their ideas with others (McCullar, 2015). Studies also show that engineering activities for children can indirectly influence their learning and achievement in science and mathematics (Katehi, Pearson, & Feder, 2009). In the long term, exposure to engineering education can assist elementary school students in developing career aspirations for engineering and other STEM careers (Capobianco, French, & Diefes-Dux, 2012; Hammack, Ivey, Utley, & High, 2015; Hegedus, 2014; Katehi, Pearson, & Feder, 2009; McCullar, 2015). There have been multiple attempts to integrate engineering education into the elementary school curriculum. One example is Engineering is Elementary (EiE), a curriculum for grades 1 through 5. The curriculum, developed by the Boston Museum of Science, aims to increase students’ awareness of engineering and technology concepts. The curriculum has 20 units, and each individual unit has an engineering design challenge related to a specific science topic. As teachers use these units their students are provided opportunities to learn science and other STEM concepts through engineering design activities (EiE, 2004). Another example of elementary engineering curriculum is the Project Lead The Way (PLTW) Launch program. This program was also developed to support STEM education for grades 1 through 5 and is comprised of 25 interdisciplinary modules to help develop a student’s design thinking, communication, and collaboration skills while engaging in learning activities associated with topics in computer science, engineering, and biomedical science (Project Lead The Way, 2013). Furthermore, the International Technology and Engineering Educators Association’s STEM Center for Teaching and Learning offers the Engineering byDesigntm curriculum program which includes curriculum for grades K through 5 that integrates concepts of science, technology, engineering, and mathematics through the context of grand societal challenges. It is evident from these initiatives that engaging students in engineering at an early age is important to help children understand engineering-related career opportunities, involve students in integrated STEM learning, and aid in developing student skills for solving the challenges of the future. Whether or not a school chooses to implement one of the established elementary engineering curriculum programs, all elementary teachers can develop engaging engineering design-based activities that spark students’ interest in engineering-related careers and develops their abilities to design, tinker, make, invent, and innovate. This article will explore one engaging way to expose students to engineering at an early age through the context of quadcopters. As stated by Sutton, Busby, & Kelly (2016) quadcopters “represent an intersection where science, technology, engineering, and mathematics come together in a practical way” (p. 8).
Article
Full-text available
There is much support in the research literature and in the standards for the integration of engineering into science education, particularly the problem solving approach of engineering design. Engineering design is most often represented through design-based learning. However, teachers often do not have a clear definition of engineering design, appropriate models for teaching students, or the knowledge and experience to develop integrative learning activities. The purpose of this article is to examine definitions of engineering design and how it can be utilized to create a transdisciplinary approach to education to advance all students' general STEM literacy skills and 21st century cognitive competencies. Suggestions for educators who incorporate engineering design into their instruction will also be presented.
Article
Full-text available
Cloud computing is a powerful technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software. Massive growth in the scale of data or big data generated through cloud computing has been observed. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. The rise of big data in cloud computing is reviewed in this study. The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced. The relationship between big data and cloud computing, big data storage systems, and Hadoop technology are also discussed. Furthermore, research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance. Lastly, open research issues that require substantial research efforts are summarized.
Conference Paper
Full-text available
The mutual similarity of two maps can be most easily compared visually. In this case, the degree of similarity is very subjective. It is therefore necessary to find an objective method for measuring similarity. This chapter presents a method based on Singular Value Decomposition (SVD).
Article
Full-text available
Data visualization has the potential to assist humans in analysing and comprehending large volumes of data, and to detect patterns, clusters and outliers that are not obvious using non-graphical forms of presentation. For this reason, data visualizations have an important role to play in a diverse range of applied problems, including data exploration and mining, information retrieval, and intelligence analysis. Unfortunately, while various different approaches are available for data visualization, there have been few rigorous evaluations of their effectiveness. This paper presents the results of three controlled experiments comparing the ability of four different visualization approaches to help people answer meaningful questions for binary data sets. Two of these visualizations, Chernoff faces and star glyphs, represent objects using simple icon-like displays. The other two visualizations use a spatial arrangement of the objects, based on a model of human mental representation, where more similar objects are placed nearer each other. One of these spatial displays uses a common features model of similarity, while the other uses a distinctive features model. The first experiment finds that both glyph visualizations lead to slow, inaccurate answers being given with low confidence, while the faster and more confident answers for spatial visualizations are only accurate when the common features similarity model is used. The second experiment, which considers only the spatial visualizations, supports this finding, with the common features approach again producing more accurate answers. The third experiment measures human performance using the raw data in tabular form, and so allows the usefulness of visualizations in facilitating human performance to be assessed. This experiment confirms that people are faster, more confident and more accurate when an appropriate visualization of the data is made available.
Article
Agriculture in northern Ontario, Canada, has not yet reached the level of development of the southern regions of the province. In spite of the increasing desirability of the former region for agricultural expansion, northern agricultural producers-as well as other producers in "emerging" areas-have less access to information and decision support services relative to more established agricultural regions. At the same time, geographic information systems (GIS) are now being integrated into precision agriculture to assess field variability, to ensure optimal use of information, to maximize output, and to increase efficiency. To address this trend, a community-based research initiative based on an interactive web-based information visualization and GIS decision support system has been deployed with the aim of providing northern Ontario producers with access to the data they need to make the best possible decisions concerning their crops. This system employs citizen science and community-based participatory research to build a mutually beneficial partnership between agricultural producers , researchers, and other community stakeholders.
Book
Six Chemicals That Changed Agriculture is a scientific look at how the chemicals used in today's food production were developed, evaluated, and came to be in wide-spread use. From fertilizers to pest management, antibiotics to DNA, chemicals have transformed the way our food is grown, protected, and processed. Agriculture is the world's most important environment interaction, the essential human activity, and an increasingly controversial activity because of its use and presumed misuse of chemistry. The major characteristics of US agriculture for at least the last six decades have been rising productivity, declining number of mid-size farms, increasing farm size, an increasing percentage of farm production on fewer, large farms, increasing dependence of chemical technology and more developmental research being done by the agricultural chemical industry rather than by independent land-grant universities. Another equally important feature of modern agriculture is wide-spread suspicion of its technology by the public. The book will recount examples of this suspicion related to specific chemicals and present the essence of the suspicion and its results. • Offers an historical analysis of the discovery and development some aspects of the chemistry of modern agriculture. • Addresses the advantages, disadvantages, desirable and undesirable results of the use of each of the chosen chemicals and compares and contrasts the real and frequently assumed problems of their use. • Provides valuable insights into the history and application of these focused chemicals, enabling readers to apply the lessons to new agricultural chemical developments.
Article
Visualizing sensor data is not a trivial task. This work tries to find a matching between sensor data and visualizations. Sensors became very popular over the last decade, they got smaller, more efficient and cheaper, that is why they spread in many fields of applications. Sensor taxonomy and the dependency of space and time will help to find visualizations for sensor data. Besides this there are problems with visualizations that will be worked out and with the results of researchers it is possible to find a mapping between sensors and visualizations. This mapping is discussed in the end of the work. It is a mapping from sensor data dimension and the dependency of the spatio-temporal aspect to the dimension of the data of the resulting visualization. With this resulting dimension it is possible (according to Shneiderman [17]) to create a fitting visualization.