=Paper=
{{Paper
|id=Vol-3132/Paper_14
|storemode=property
|title=Driving Adoption of Agricultural IoT Solutions Through Product Design
|pdfUrl=https://ceur-ws.org/Vol-3132/Paper_14.pdf
|volume=Vol-3132
|authors=Jakub Štěpán Novák,Jan Masner,Petr Benda ,Alexandr Vasilenko
|dblpUrl=https://dblp.org/rec/conf/iti2/NovakMBV21
}}
==Driving Adoption of Agricultural IoT Solutions Through Product Design==
https://ceur-ws.org/Vol-3132/Paper_14.pdf
Driving Adoption of Agricultural IoT Solutions Through Product
Design
Jakub Štěpán Novák, Jan Masner, Petr Benda and Alexandr Vasilenko
Czech University of Life Sciences, Kamýcká 129, Prague, 16500, Czech Republic
Abstract
Ecosystem of connected devices, internet of things is gaining popularity in a diverse range of
use cases. Use of IoT in the agrarian sector is nothing new. But despite this fact, the agrarian
sector does not seem to have a firm grip on how to adopt this solution in a more complex way,
on every level within the agrarian production process. Spread of use of IoT in various
environments has a great impact on production efficiency and output quality. Yet, when it
comes to agrarian enterprises, there seems to be a missing sector-wide drive to invest and use
IoT on a broader spectrum, not just within some parts of the production process. This paper
aims to identify most prominent problem areas causing problems to broader adoption of IoT
solutions in Czech agricultural enterprises. Findings of this research establish grounds for
connecting digital and physical product design patronization methods leading to conceptual
approach for IoT product design recommendations. Potential impact of applied product design
patronization on drive of usage of IoT in agriculture is also a key topic of this article. Result of
this paper leads to a framework describing fields of use of product design patronization for IoT
products in agriculture. Impact caused by well virtually and physically designed IoT solutions
in agricultural enterprises on technology adoption is also described. Combination of this leads
to methodology improving the drive of IoT usage in Czech agricultural enterprises.
Keywords 1
IoT, Agriculture 4.0, Product Design, Usability, Precision farming
1. Introduction
Emerging global trends on effectivity and sustainability influenced almost every aspect of every
industry including agriculture. Similar to Industry 4.0, the advanced use of new approaches and
technologies in agriculture directly gave rise to the field called Agriculture 4.0 [2]. In its core
Agriculture 4.0 is a combination of interconnected technologies, smart or precision farming, big data
analytics, artificial intelligence, blockchain technologies etc. [9]. Thanks to these advancements we can
talk about the fourth agricultural revolution, the same way as transfer from indigenous farming to
mechanized farming caused the past industry revolution [1]. Established framework for smart
agriculture brings the possibility of optimizing crop yields, increasing the quality and quantity of output
while utilizing less inputs resulting in cost reductions with better impact on the environment [18].
Excessive number of articles and studies have been conducted about various smart agriculture topics
ranging from security of IoT devices, quality and reliability of various sensors, whole frameworks for
solution architectures, normalizations, and many others [8]. Yet, these systems on a scale remain a
heavily specialized field of expertise, used mainly by bigger enterprises in developed countries with the
appropriate resources and motivation for dedicated specialists grasping the capacity for implementing
these systems in real life environments [20]. Thanks to the sheer amount of device types, technological
layers, diverse datasets, and technologies it is easy for smaller enterprises and farmers from rural
backgrounds to lose themselves in the ways of smart agriculture possibilities [10]. This fact is
concerning from various perspectives, mainly because groups of small/ rural farmers could heavily
benefit from implementation of these technologies [5]. The possible ecological impact of optimized
fertilizer usage, better watering management with connection to increased adoption of these
technologies could be very positive [20]. Farmers can leverage IoT in agriculture in various ways from
Information Technology and Implementation (IT&I-2021), December 01–03, 2021, Kyiv, Ukraine
EMAIL: novakjakub_stepan@pef.czu.cz (A. 1); masner@pef.czu.cz (A. 3); bendap@pef.czu.cz (A. 3); vasilenko@pef.czu.cz (A. 4)
ORCID: 0000-0001-5008-6832(A. 1); 0000-0003-4593-2306 (A. 2); 0000-0001-9651-8258 (A. 3); 0000-0002-8108-5637 (A. 4)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
146
farm management, production optimization and automation to supported decision making based on
analyzed data and predictions. Fields of IoT use in smart agriculture according to Sinha B, et al. [7]:
● Water/ irrigation management
● Soil management
● Weather management
● Nutrient management
● Crop management
● Livestock monitoring
Most of the data used comes from specialized sensors. Table 1 sums up the main types and subtypes
of IoT sensors.
Table 1
Basic IoT sensor categorization
Sensor types Sensor subtypes Demo images
Acoustic, Amperometric,
Biosensor Calorimetric, Electrochemical,
Potentiometric, Piezoelectric...
Chemical, Color, Humidity,
Luminance, Temperature,
Environment
Radiation, Gas, Magnetic
field...
Corrosion, Contact, Density,
Mass measurement
Deformation, Flow, Moisture,
Pressure, Volume, Strain...
Acceleration, Movement,
Motion
Rotation, Velocity, Vibration….
Location, Orientation,
Position
Proximity, Presence,
Inclination
Sensors are of course not the only part of smart agricultural systems. Sensors gather information and
need to work together with other hardware like transmitters, water pumps, energy management parts
and more along with supporting software [23]. This just amplifies the real complexity of building and
using these systems to some extent, especially for people with no advanced technical background [17].
Thanks to enormous technological advances these components became very inexpensive and widely
available, so long gone are the times, where the main roadblock for implementation was inaccessibility
of components and supporting software [12]. As new opportunities arise, so do the complications.
Various studies discovered key areas posing challenges for broader adoption of IoT in agriculture:
1. Standardization: To promote open and unified standards for communication across very
heterogeneous IoT device types promoting easy interoperability on high scale is an issue hopefully
covered in future studies and research.
2. Data: In IoT data there is one major topic, Data reliability, be it missing data from mechanical
parts or transmission, weather conditions, mislabeled data etc. [7]. Although multiple data mining
techniques exist [24], noisy and abnormal data can stand in the way of successful use of data mining
techniques and undistorted data analysis.
3. Regulation: As in any field, regulations can be an unpleasant roadblock, but sometimes
necessary, this topic is not easy to grasp because how it should be approached differs from country to
country and heavily impacts how data could be used, what hardware specifications and properties are
valid and much more [25].
4. Costs & awareness: Even do IoT technology is much more available and cheaper today than
a decade earlier, its implementation still represents costs not only for obtaining solution packages, but
also the runtime and maintenance costs, be it electricity, server plans etc. But a much bigger issue
impacting wider adoption of IoT in agriculture is lack of awareness in this specific market [7]. There
seems to be a missing clear message about how this technology can help every individual farmer for
their use case and how it can be achieved. Understanding that smart agriculture is not expensive, hard
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to use a piece of hardware, but rather practical technology with potential for improving their everyday
life [13].
There are much more defined challenges for agricultural IoT usage but listed above are the most
relevant to specific problems driving adoption, especially between smaller farmers with fewer resources
and motivation or awareness [15]. Another whole new breakpoint for advancement of agricultural IoT
adoption is scientific research focus. Many studies are focused on finding the best communication
protocols, circuitry architecture, UAV frameworks and other deeply technological problems [4], but
few actually use design driven approaches and take into consideration the end user. This is one of the
reasons smart agriculture seems to be a very advanced topic only experts and few technology enthusiasts
really understand [11]. This sets an opportunity to think about how or if it is possible to create
framework for approaching the whole user lifecycle from product idea onboarding to actually using the
product in the easiest and most pleasant manner possible, so the use of smart agriculture could be
adapted beyond large enterprises and experts, but also to a small farmer just wanting to automate his
vineyard watering workflow [22]. This is the primary motivation for the focus of this article, to examine
product design approaches to IoT in agriculture, rather than already well documented research on
various functional and technological backgrounds. Some of the most popular smart agriculture solutions
were compared based on criteria most relevant to setting up new use cases and rated by end users. Table
2 shows ratings in various segments sorted by importance. Ease of use: most important, performance:
less important. These discovered criteria were used in framework design and the solution testing
scenarios. Mentioned design driven development focuses primarily on ease of use, which as could be
seen in table 2 is the criteria with highest priority amongst smaller farmers. What could be also seen
from table 2 is the fact that average ease of use value for selected solutions is around 5.63/10 and this
pattern was observed in most, even unclassified solutions. Ease of use is one of the most important
aspects to take into account when creating adoptable, broadly used products [28].
Table 2
Comparison of smart agriculture solutions by small farmer criteria
Criteria Farm Works AgDNA AgroSense Sentera
Ease of use 4.2/10 7.3/10 6.2/10 4.8/10
Features 7.8/10 5.4/10 7.5/10 5.0/10
Integration 7.9/10 8.4/10 8.3/10 5.5/10
Performance 5.1/10 7.6/10 5.3/10 4.4/10
Focus groups were used to determine most prominent issue areas and set the base for comparative
testing. Combination of public data, custom survey and deep dive interviews are also the supporting
cornerstone of this research. Survey is designed for 30 individuals from the agriculture sector with
various specializations, regions, and technological advancement. Deep dive interviews are conducted
with selected participants to understand specific problems in depth. In person user testing is conducted
to study the thought processes linked to certain tasks like creating new use cases, configuring devices,
or managing existing setup. Gathered data are then synthesized to indicate most workable areas and
ideate solutions. These solutions can be then evaluated from the perspective of feasibility and real
benefit to the framework of the problem.
2. Challenges and findings
When designing an adaptable system for people, design approaches should be used [28]. The same
way design driven development skyrocketed use of smartphones shifting the paradigm from niche
PDAs to mass smartphone usage driven by the revolutionary iPhone [27]. What Apple did to promote
adoption of this technology is exactly what is needed to happen in the smart agriculture field, employing
design thinking instead of bare technology focus. During pre-research frame definition, a set of 10 deep
dive interviews revealed the first area of challenge. For research purposes, a generic use case was
created based on a 4-layer design model proposed by Yang, et al [18]. When users were first introduced
with the physical hardware and setup workflow, it made no sense to them. As expected, human
behavioral research were true, people are deeply visual minded, clinging to any possibility of visual
associations for better adaptation to problems [28]. In this research case, end users interact primarily
with first and last layers. First, it's the physical sensor deployment, for example moisture sensor in soil,
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and then the last layer represents fe. dashboard for device management, configuration, and reports. This
is where the first problem occurred: farmers don't think in sensor names and port numbers, but rather
in concrete problems and ideal solutions with as little effort as possible. In short, users did not know
where the device (sensor) was represented in the application UI. Based on this knowledge, a middle
layer, linking visually the first and last layer was added.
In practice this means physical devices are visually updated with e.f., 3d printed colored casings.
These designs align with visual device representation in app UI with possibility to sort device types and
use cases based on these visual properties. Two main distinguishing parameters have been discovered
to be most considered by end users. Colors, for example: orange devices are specialized to work with
water, red are for electrical detection and shapes: if its sensor or other type of mechanical part, for
demonstration see Figure 2, which demonstrates updated visuals of physical IoT device and
corresponding enhanced setup UI, working visually instead of technically and guides the user through
setup process instead of causing information overload with excessive number of non-guiding, generic
text fields, which the users have no chance of working successfully with [26]. This visual framework
was the first step in improving users' chances to successfully solve the problem.
The use case based on this approach was as follows: Based on a brief introduction about what smart
agriculture can do for the end users and how they can benefit from its use, users were introduced to a
demo dashboard with one task. Set up anything they would like to actually use. The interface offered a
technical view in the first run and simplified walk-through view in the second run. Most users picked
up simple problems like automatic watering of small tomato plots or automatic greenhouse atmosphere
regulation with opening windows and other issues they are invested in real life scenarios and can
imagine using smart agriculture for. With the base field set, deep dives and focus group experiments
could begin to determine key pain points in using smart IoT systems to successfully solve real life
problems. From additional discussions, hands-on testing, and surveys 4 main problem areas have been
discovered. These problems are listed in Table 3 along with % of impacted users and severity of this
problem blocking the users from advancing in adapting this technology.
● P1: Motivation and benefits, the smallest top discovered problem, but still impacting more than
half of selected respondents. This touches on the topic of technological literacy and benefit awareness
[7]. Users don't not know why this (IoT solution) should be better, mainly because of decades of doing
everything the old way, they could not imagine the real benefits, what does it mean for them, that
something is automated, why they should pay extra money etc. Severity of this problem is not that high,
because this problem can be easily solved. After presenting practical use case descriptions along with
ROI and time savings and other quantitative data, put to the user's context, this problem was no blocker
in task finishment chances.
● P2: Plug and play expectations. When faced with an excessive number of devices, cables and
utilities, users did not know what to actually do with it.
● P3: Device management setting up new solutions, managing existing setups and easy access to
analytics. The Introductory demo worked very technically, and users were not able to create a simple
workflow setup. This problem was a huge blocker since information overload and technical difficulties
made it impossible for users to complete the task.
● P4: Configuration. Similar to management, users did not know which values and thresholds
should be set to achieve the task goal. Which correlates with the thinking process of finding a solution
for a problem: Users want optimal soil humidity for their tomatoes, but don't know how to set the exact
values of thresholds for water pump activation etc.
To successfully handle these problems various solutions have been tested and it was discovered that
main added value lies in simplification of product design, user centered setup process with simple
walkthroughs and visual association between physical and digital representation. These ideas have been
put into the creation of a framework which is then tested on from various aspects.
Table 3
Problem areas in agricultural IoT adoption
Problem Area % Respondents Severity
P1 Motivation & benefits 60 3/5
P2 Plug & play 75 4/5
P3 Management 83 5/5
P4 Configuration 91 5/5
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Figure 1: Enhanced 4-layer architecture
Figure 2: Visualized association
3. Testing framework ideas
Proposed system design aimed at solving discovered problems based on existing research and
experiments. This system design consists of three main parts, Goal of the user, initial setup and
finishment. Proposed design simplifies the existing workflow paradigm and enhances it with user
centric approach to system design. Interaction with the system is built around real user needs and starts
with problem definition (what the user needs to achieve). To avoid decision paralysis, databases with
common use cases is utilized. Thanks to this system the user does not have to start with a blank slate,
but rather start with predefined problems which can be customized to perfectly fit end users' needs with
dynamic step-by-step setup walkthrough. This has a great potential of eliminating the severity of P3,
because the user automatically gets recommendations which devices to use, the quantity and setup based
on preselected preferences with visual aid to help further improve orientation in available devices.
Concrete example can be this: Farmer wants to have his crops automatically irrigated. Does not know
what is needed, just knows how big his plot is, what crop he cultivates, and which climate type is present
at his plot. First step introduces the user to problem selection, for example: Irrigate crops, second step
would be choice of crop, plot dimensions and geological location with additional optional requirements.
Based on these parameters in the language even the most rural and small farmers understand a set
of recommended devices and amounts is automatically proposed to the user along with drag and drop
interconnection visual builder. Thanks to parameter setting in previous steps, problem P4 can be also
eliminated. The same way device recommendations work on desired use cases, device configuration
for task automation can be automatically set based on these preferences. This means the user does not
have to think about anything, only, if needed to edit pre-filled configuration. This framework is then
used for designing mock prototypes of user interfaces. Walkthrough of this interface is then compared
to existing solutions on the market. For demonstration purposes ThinkSpeak IoT platform has been
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chosen as a benchmark for non-technical users to complete the desired task against newly proposed
design. Figure 4 demonstrates differences between technological vs user centric approach with gaze
plot analysis. In the case (A) of advanced technological solutions, users with little to no technology
background (majority of test subjects) had an atomic problem even understanding what they should do,
gaze plot in this case shows desperate attempts to find some orientation point or guide to successful
task completion. On the other side (B) the gaze path is far more streamlined, following the path as users
scan familiar topics instead of blank generic fields, and read additional descriptions to point the user in
desired location without any guess work and frustration.
Figure 2: (continue)
Figure 1 displays just the first step of the mock workflow, as testing progressed following steps
reproduced similar patterns. Success of individual steps was also measured. Flow was built on 3 steps:
Initial setup, device selection/ pairing, configuration. In scenario (A) 90% of users did not manage to
finish even the first step without aid, in contrast, all users were able to create their first use case in
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scenario (B). Thanks to smart recommendations and automatic configurations proposed in Figure 3,
scenario (B) had a massively better overall aid-less finishment rate. Result of this experiment could be
summarized as follows: Farmers with little technical backgrounds find it practically impossible to set
up and configure agricultural IoT solutions of any size in the manners of advanced technical toolsets.
After applying user centric approaches, the same process was greatly better in performance, not only
the end users easily configured the solution, but also gained an understanding how IoT conceptually
works, benefiting various agricultural aspects.
Figure 3: Setup system design
4. Discussion and recommendations
Primary goal of this article was to discover problems slowing adoption of IoT in smart agriculture
and test approaches with the potential to help this situation. Thanks to conducted experiments, multiple
areas that might be worth exploring further in detail to drive the smart agriculture growth further
between smaller and rural farmers were uncovered and tested. No in-depth technological solution was
proposed as it is out of scope of this article and would require more quantitative data and test subjects.
Results of this article paved a way to possible further research, which would need to be conducted to
really put these recommendations into quantitatively significant results and present real-life impact.
Figure 5 sums up 4 main recommendations for further research based on identified problems both from
existing research and conducted experiments.
4.1. Value proposition
Key starting point is motivation and awareness for the end users. There is little to no reason IoT
frameworks for smart agriculture shouldn't be marketed as any other product improving people's lives.
Marketing or landing project pages could be created, highlighting real benefits for every individual
farming use case, offering solutions for real life problems while demonstrating ease of use and benefits.
This alone might not be enough to shift industry awareness miles ahead, but it's a starting point to bring
smart agriculture to the spotlight for every farmer.
4.2. Bridged physical and virtual design
Connecting the physical and digital world has proven to be a step in the right direction. Thanks to
enhanced physical device product design corresponding with the application layer, users found it easier
to select and manage devices. It also provided a path to new understanding how IoT works leading to
new sources of technological knowledge in this sector. Instructional device design can lead to better
understating and elimination of fears of unknown technology for individual farmers.
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4.3. Automated configuration
During experiments with users, device configuration was the most intimidating step during the
whole research process. With automated configuration based on intuitively sourced data this problem
can be entirely skipped. Users can just check and edit proposed optimal configuration for desired use
cases without any technical knowledge or advanced needs of things like pump waterflow for individual
threshold values and hundreds of other possibilities. If this manual process is replaced with one click,
potential to grow its usage can be large, but how exactly should be examined in further research.
Figure 4: Prototype for walkthrough comparison
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4.4. Open framework
For these standards to be widely available and inexpensive, an open approach should be utilized.
This allows for greater experimentation, cost reductions and other initiatives driving IoT technology
further. Open framework could contain proven recommendation, app research or software platform, 3D
printing schematics parts lists and more.
Figure 5: Areas for improvement
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