Applications of smartphone based sensors in agriculture a systematic review of research

Review Article
Applications of Smartphone-Based Sensors in Agriculture:
A Systematic Review of Research
Suporn Pongnumkul, Pimwadee Chaovalit, and Navaporn Surasvadi
National Electronics and Computer Technology Center (NECTEC), 112 Thailand Science Park, Phahonyothin Road,
Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand
Correspondence should be addressed to Suporn Pongnumkul; suporn.pongnumkul@nectec.or.th
Received 7 April 2015; Revised 8 July 2015; Accepted 9 July 2015
Academic Editor: Pietro Siciliano
Copyright © 2015 Suporn Pongnumkul et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
Smartphones have become a useful tool in agriculture because their mobility matches the nature of farming, the cost of the device
is highly accessible, and their computing power allows a variety of practical applications to be created. Moreover, smartphones are
nowadays equipped with various types of physical sensors which make them a promising tool to assist diverse farming tasks. This
paper systematically reviews smartphone applications mentioned in research literature that utilize smartphone built-in sensors
to provide agricultural solutions. The initial 1,500 articles identified through database search were screened based on exclusion
criteria and then reviewed thoroughly in full text, resulting in 22 articles included in this review. The applications are categorized
according to their agricultural functions. Those articles reviewed describe 12 farming applications, 6 farm management applications,
3 information system applications, and 4 extension service applications. GPS and cameras are the most popular sensors used in
the reviewed papers. This shows an opportunity for future applications to utilize other sensors such as accelerometer to provide
advanced agricultural solutions.
1. Introduction
Among the technologies invented in the past few decades,
smartphones have gained large market shares among var-
ious user sectors due to their usefulness, ease-of-use, and
affordability. The number of new smartphone users continues
to grow. It is estimated that, by 2016, the number of users
will be more than 2 billion people worldwide [1]. One
factor that enhances the smartphones’ ability to assist users
to perform various tasks is the numerous built-in sensors
(e.g., positioning sensors, motion sensors, and cameras
microphones). Many industries have adopted smartphones to
facilitate their work, such as health care [2, 3] and education
[4, 5]. This paper reviews the usage of smartphone sensors
in an important sector, agriculture. Inexpensive smartphones
equipped with various sensors are opening new opportunities
for rural farmers who previously had limited access to up-
to-date agricultural information (e.g., market, weather, and
crop disease news) and assistance from agricultural experts
and government extension workers. Meanwhile, farmers in
large-scale farms, who already adopt assistance from other
information technologies, can now utilize smartphone-based
sensors to increase productivity and facilitate various tasks
throughout the farming cycle.
Ensuring food security has been a global concern
throughout human history, and the global food crisis of
2007-2008 has emphasized the importance of increasing both
quantity and quality of food production [6]. Agriculture,
which is the upstream food producing sector, is, therefore,
in need of new and modern methods to ensure the world’s
food security. Traditional agriculture was usually done within
a family or a village and accumulative farming expertise
and knowledge were passed down to their future gener-
ations. Human was the main observer of field conditions
and the solution provider as problems arise. However, this
traditional method is no longer suitable, because farming
outputs depend largely on the natural surrounding conditions
(e.g., weather and water) and global warming issues (causing
frequent droughts and floods) and crop disease outbreaks are
disruptive of farming productivity. Traditional within-group
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Journal of Sensors
Volume 2015,Article ID 195308, 18 pages
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2 Journal of Sensors
knowledge and human observations alone are not sufficient
to battle the issues. Therefore, new methods for farming have
been invented, for example, precision agriculture. One key
idea of new practices in agriculture is the use of technology
to measure or monitor field and crop conditions in order
for farmers to make informed decisions in various parts of
their farming process. Sensors for continuous and automatic
measurements of different field values are one of the main
success factors for precision agriculture [7].
Dedicated devices (e.g., a SPAD meter for leaf chlorophyll
assessment [8], a Li-3100 for Leaf Area Index (LAI) measure-
ment [9], and a spectroscopy for studying soil structure and
components [10]) have been useful in agriculture but might
be difficult to access for rural farmers who do not have access
to the product providers or insufficient funds to purchase
the devices. However, some of the tasks that the dedicated
devices perform can be replicated on smartphones with built-
in sensors. This provides an easy alternative for farmers who
already own smartphones to easily download applications to
use. This paper aims at reviewing how smartphone sensors
have been used in agriculture in research literature and
illustrates the range of applications that farmers can use
for their work without having to purchase new hardware.
Additionally, to the best of our knowledge, we are the first
to review smartphone usage in agriculture with the focus
on how sensors are used. This paper also highlights the
open opportunities through which researchers and mobile
developers can further utilize smartphones to provide better
tools for agriculture.
In this survey, the authors categorize smartphone appli-
cations by their agricultural functions into four categories:
farming, farm management, information systems, and exten-
sion services. Farming applications are those that assist in
farming activities, such as disease detection and pesticide
application rate calculation. Farm management applications
facilitate users to manage overall farm resources and farming
activities more efficiently and effectively in order to achieve
better profit and farming productivity. Information system
applications include systems that provide essential infor-
mation for farmers, including farming knowledge, environ-
mental context, and market information. Extension service
applications provide assistance to the traditional agricultural
extension where field workers visit farms. Extension service
applications mostly provide channels for remote farmers to
access expert consultation and provide tools for field workers
to efficiently work while in the field.
A systematic review was conducted by a search for
research papers on SCOPUS database, yielding 1,500 initial
results. The search results were then screened by reviewing
title, abstract, and keywords for exclusion criteria: smart-
phone applications only limited to voice, SMS, or accessing
information via the Internet; primary domains of smartphone
applications not in agriculture; smartphone applications only
applied to non-plant-based agriculture; no smartphone-
based sensor used in smartphone applications; full text
articles not accessible; and articles written in languages
other than English. This process resulted in 339 potentially
relevant papers which were again reviewed more thoroughly
in full text using the same exclusion criteria. The remaining
22 papers are included in this review and are categorized into
the four categories discussed above. The papers (describing
12 farming applications, 6 farm management applications,
3 information system applications, and 4 extension services
applications) are summarized with the focus of the smart-
phone sensor usages and their functions.
The remaining sections are as follows. Section 2 reviews
the current technology of smartphone sensors. Then,
Section 3 describes the method used to obtain the articles for
this review. After that, the results are presented in Section 4
with subsections for each of the four categories. In Section 5,
we discuss, in each subsection, respectively, the current usage
of smartphone-based sensors in agriculture, their advantages
with respect to traditional tools/methods, challenges for
their usages in agriculture, and future research opportunities.
Finally, Section 6 concludes the review.
2. Smartphone Sensors and Their Use
This section reviews common sensors of recent smartphones.
Sensors are a device that measures a physical quantity and
converts to a signal which can be read and used by other
devices. An example of the traditional sensors is the ther-
mometer which converts how hot or cold the environment
is into a number, which is called the temperature. There
are many kinds of sensors available nowadays, and many
mainstream smartphones are equipped with various sensors,
including accelerometers, GPS, light sensors, temperature
sensors, gyroscope, and barometer.
The sensors embedded in smartphones can be classified
into three categories: motion sensors, environmental sensors,
and position sensors. The first category, motion sensors, pro-
vides acceleration force and rotational force measurements.
Examples of motion sensors are accelerometers, gyroscopes,
gravity sensors, and rotational vector sensors. The second
category, environmental sensors, provides measurements of
the surrounding environments. This ranges from ambient
air temperature from a thermometer and pressure from a
barometer to illumination from a photometer. The third cate-
gory, position sensors, provides measurements of the device’s
physical position. Such sensors include magnetometers, GPS,
and orientation sensors [11]. Table 1 describes the common
smartphone sensors. For the purpose of this review, we
exclude communication channels (e.g., WIFI, Bluetooth, 3G,
and NFC) and display channels from the sensor list.
Recent increases in popularity of smartphones have called
attention to researchers to explore the use of smartphone
sensors in their work. Many sensors have been applied
successfully in numerous areas. Global Positioning System
(GPS) allows applications to obtain the current location of
the device. Apart from direct uses of the location in appli-
cations (e.g., maps), GPS data have also been used to infer
transportation modes [35]. Accelerometers, which measure
the force of acceleration whether caused by the phone’s
movement or gravity in 3 axes, have been used as sensors
in fall detection [3], activity recognition [36], driving event
detection [37], and so forth. Built-in cameras (both front and
rear cameras) of recent smartphones have improved greatly
in terms of specifications over recent years. The camera

Journal of Sensors 3
Table 1: Common smartphone sensors [11, 12].
Sensor Description Common uses
Accelerometer
Measures the acceleration force in m/s2
that is
applied to each of the three physical axes (𝑥, 𝑦, and
𝑧) of a device (including gravity)
Detects motion and senses the changes in
smartphones’ orientation
Ambient temperature
sensor
Measures the ambient room temperature in degrees
Celsius (∘
C)
Monitors air temperatures
Gyroscope
Measures a device’s rate of rotation in rad/s around
each of the three physical axes (𝑥, 𝑦, and 𝑧)
Detects rotation (spin, turn, etc.)
Light sensor Measures the ambient light level (illumination) in lx Controls screen brightness
Magnetometer
Measures the ambient geomagnetic field for all three
physical axes (𝑥, 𝑦, and 𝑧) in 𝜇T
Creates a compass
Barometer Measures the ambient air pressure in hPa or mbar Monitors air pressure changes
Proximity sensor
Measures the proximity of an object in cm relative to
the view screen of a device
Detects if the phone is held up to a
person’s ear during a call
Humidity sensor
Measures the humidity of ambient environment in
percent (%)
Monitors dewpoint and absolute and
relative humidity
Global Positioning System
Measures the latitude and longitude of the current
location of the device
Uses a user’s location to show the nearby
information
Image sensor (camera) Records images and videos Captures still picture or video
Audio sensor (microphone) Measures sound in air into an electrical signal Records voices
Fingerprint identity sensor Reads a user’s fingerprint Identifies a user through touching
Moisture sensor
Identifies whether the device has been submerged in
water
Detects if a device has internal water
damage
resolutions are now comparable with dedicated hand-held
cameras. Therefore, smartphone applications can now be a
single device, which takes as input the images or videos
from built-in cameras, then uses its computation power to
perform computer vision algorithms, and produces useful
data from the input images. Examples of such applications
include indoor positioning [38], heart rate extraction using
video imaging [39], and pulse rate evaluation [40].
3. Methods
3.1. Data Sources of Systematic Review. We have adopted
PRISMA [41], which is a basis framework for reporting
systematic reviews, for our research. A literature search
following the PRISMA guideline for articles from SCOPUS
database was performed on February 2, 2015. Our goal was
to search for articles featuring sensors in smartphones and
their use in agriculture. Using the keyword “sensor(s)” in
the search criteria yielded articles related to any types of
sensors and not particularly smartphone-based sensors, and
we were given a set of results with an overwhelming amount
of articles to exclude. Therefore, we made a decision not to
include the keyword “sensor(s)” as the inclusion introduced
too many false positive articles to be later manually removed.
Besides, a usage of various smartphone components as sen-
sors in research can be quite subtle. For example, researchers
often do not explicitly point out that the use of cameras
to collect image data is “sensing” images using cameras
on smartphones. Oftentimes they are explained as utilizing
smartphones to perform certain agricultural tasks. Therefore,
we decided to include “smartphone(s)” in our search criteria
instead.
Since smartphones in the literature may be referred to by
many terms such as “mobile phone(s)” or “mobile device(s),”
we devised a set of words which can describe smartphones.
The set of keywords are the following: “mobile application,”
“mobile app,” “smartphone,” “mobile device,” and “mobile
phone.” With our goal of reviewing usage of smartphone-
based sensors in agriculture, the previous set of keywords
became search criteria in conjunction with the keyword “agri-
culture.” Specifically, the phrase ((“smartphone” OR “mobile
device” OR “mobile phone” OR “mobile app” OR “mobile
application”) AND “agriculture”) was entered into SCOPUS
search engine. The search terms focused on the terms related
to smartphones and agriculture. The search results were not
limited by language or any time constraints. The result set
from this search was then systematically reduced to only
articles featuring sensors in smartphones and their use in
agriculture by exclusion criteria.
3.2. Inclusion and Exclusion Criteria. In this systematic
review, we are interested in articles that present the use
as well as the development and evaluation of smartphone-
based sensors in agriculture. Therefore, we searched the
SCOPUS database using the keywords “agriculture” and
many variations of “smartphone(s).” The search produced a
list of articles with their use of onboard smartphone sensors,
as intended.
The search also resulted in articles that feature smart-
phones but not their sensors. For instance, smartphones

are used only as a communication means in agriculture
work, when farmers use voice call or Short Message Ser-
vice (SMS) to communicate crop prices, or when network-
enabled smartphones are solely used as devices for access-
ing agricultural-related information. We decided to exclude
those articles, as smartphones are not devised as sensors but
only as communication devices. Also, we excluded articles
that describe general systems which were not specifically
designed for agricultural use. In addition, the scope of our
review is plant farming; we therefore removed articles related
only to animal farming. Furthermore, we review usage of
smartphone-based sensors in agriculture in order to increase
farmers’ awareness of numerous applications that can be
used in their farming cycle without having to purchase new
dedicated devices. We, therefore, decided to exclude articles
in which smartphones are used only as a remote terminal
to monitor and control other external sensors, for example,
external temperature and humidity sensors as in [42, 43].
In many cases, it is still not clear from a full text review if
any built-in sensors are used in the applications. However,
we decided to include certain articles in which although the
authors did not clearly specify the sensors used, we can infer
from the functionalities of the applications such as retrieving
location data via GPS as in [27, 28, 31]. We also excluded
articles written in languages other than English and those of
which we could not access full text.
In summary, our exclusion criteria include (a) smart-
phone applications only limited to voice, SMS, or accessing
information via the Internet; (b) primary domains of smart-
phone applications not in agriculture; (c) smartphone appli-
cations only applied to non-plant-based agriculture; (d) no
smartphone-based sensor used in smartphone applications;
(e) full text articles not accessible; and (f) articles written
in languages other than English. The process of filtering
out articles based on our exclusion criteria was performed
manually.
3.3. Data Extraction. After irrelevant articles had been
excluded based on the criteria above, we read the remaining
22 articles in full text to gather relevant information. We
extracted information of what the smartphone applications
utilizing onboard sensors are, which smartphone-based sen-
sors are used, and how the sensors are used. We also accessed
the smartphone application marketplaces to extract related
data when applications are available on those markets.
3.4. Flow Diagram. The flow diagram presented in Figure 1
concludes the steps taken in our database search method
and search result filtration. First, entering the search terms
into SCOPUS search engine initially resulted in 1,500 arti-
cles. Second, the search result set was screened by title,
abstract, and keywords following the exclusion criteria in
Section 3.2, resulting in the exclusion of 1,161 articles. Third,
the remaining 339 potentially relevant articles were examined
thoroughly in full text using the same exclusion criteria.
The final result list consisted of 22 eligible articles for
comprehensive review of their contents with the focus of the
smartphone sensor usages and their functions in agriculture.
Those 22 articles, describing 25 smartphone-based sensor
applications, are categorized based on their agricultural func-
tions as follows: 9 articles describing 12 farming applications,
6 articles describing 6 farm management applications, 3
articles describing 3 information systems applications, and 4
articles describing 4 extension services applications.
4. Results
This section describes the smartphone applications from
our survey. The applications were categorized into four
categories: farming, farm management, information system,
and extension services.
4.1. Farming Applications. Farming refers to a series of agri-
cultural processes which involve various day-to-day activities
on the field, for example, sowing, weeding, fertilizing, and
making related agricultural decisions. Farming activities are
focused on how to grow plants, kill weeds/pests, identify
and correct plant diseases, apply fertilizers, and estimate
growth/yield of crops. Recent advances in smartphone appli-
cation development and an increasing availability of smart-
phones allow for some of these agricultural burdens to be
lifted and guided. For example, farmers may calculate proper
amounts of fertilizers for crop fields upon analyzing color of
crop leaves with some help from smartphone applications.
This section reviews smartphone applications under farming
category and how they may aid farmers to achieve farming
tasks.
Farmers have a number of responsibilities working in
crop fields. A handful of tasks which are performed in the
field, that is, seeding, weeding, fertilizing, and watering,
may seemingly be repetitive, mundane, and labor-intensive.
However, those tasks usually require precursory decision-
making steps prior to the actual activities in order for the
farming cycle to be effective. The previous statement is
especially true when farmers have to deal with unfamiliar new
kinds of crops [44]. Knowledge such as how to identify crop
diseases, the diseases’ exhibition locations on crops, and their
prevention and cure can save farmers’ time and costs in the
practice of farming. The following are the subcategories of
smartphone applications for farming.
Disease Detection and Diagnosis. Smartphone applications
under this subcategory are dedicated to disease detec-
tion/diagnosis in farms when utilizing sensors on smart-
phones. Prasad et al. depicted a mobile vision system which
aids in plant disease identification process [13]. The system
worked by capturing images of plant leaves being investigated
for diseases, then preprocessing those images, and transmit-
ting the processed images to remote laboratories. The image
preprocessing step was necessary for saving transmission
cost of sending diseased leaf images to plant pathologists in
remote laboratories. Leaf images were segmented by a clus-
tering algorithm into three areas: background, nondiseased
portion of the leaf, and diseased portion(s) of the leaf. Leaf
images were then cropped only to the location of the largest
diseased patch on the leaf and transmitted over any available
network to lab experts for further disease identification.
Closely related to disease detection is suggesting cure and

1,161 articles excluded based on title, abstract, and keywords
1,500 articles identified based on searches performed on SCOPUS database
339 potentially relevant articles reviewed in full text
22 articles included in the systematic review, discussing 25 smartphone-based agriculture applications in 4
categories:
317 full text articles excluded, with the following reasons:
(a) Only limited to the application of voice or SMS functionalities
(b) Only limited to accessing information on the web/network using network-
enabled mobile devices
(c) Non-plant-based agriculture, for example, livestock, animal husbandry, fish, and avian
(d) Main focus domain not in agriculture
(e) No smartphone-based sensors used, or smartphone only used for
controlling external sensors
(A) Meet exclusion criteria (n = 275)
(C) Full text not in English (n = 18)
(1) Applications for farming (n = 9, m = 12)
(2) Applications for farm management (n = 6, m = 6)
(3) Applications for information systems (n = 3, m = 3)
(4) Applications for extension services (n = 4, m = 4)
(B) Inability to access full text (n = 24)
n: number of articles; m: number of applications
Figure 1: Flow diagram of the systematic review of usage of smartphone-based sensors in agriculture.
prevention for the diseases. Wu and Chang proposed a mobile
application Magri, which utilized pest/disease information
previously self-reported by farmers [25]. Magri’s report mod-
ule generated advice for the cure of pests, as well as pest alerts
in nearby areas using GPS.
Fertilizer Calculator. Applying fertilizer is an important
farming activity with a potential to greatly affect farm
productivity. Decisions on which chemicals to apply and
their crop-specific appropriate quantities need to be made by
farmers. Sumriddetchkajorn reviewed a few mobile device-
based optical applications for agriculture [16], one of which
was a smartphone-based color estimator dedicated for rice
leaves’ chlorophyll evaluation BaiKhao. The application and
its newer released version BaiKhaoNK evaluated the color
level of rice leaves and recommended required amounts of
nitrogen fertilizer for applying to the rice field [8, 16, 17].
Soil Study. Soil is another major component in farming
which has a great impact on the success of agriculture.
Farmers equipped with soil data receive an advantage in
farming, including in precision agriculture. There are a
number of smartphone applications in the literature that
utilized smartphone sensors in studying soil for agricultural
purpose. In [14], mobile phones were used as soil color
sensors. Soil color information was read from images taken by
built-in digital cameras on smartphones and processed using
image processing techniques to transform RGB color space
into colors in XYZ and HVC spaces. The color conversion
was conducted solely on mobile phones and needed no
other external software. Another smartphone application,
Soil Indicators for Scottish Soils, SIFSS, was useful for soil
study in agriculture [15]. Farmers received soil’s detailed
information, for example, pH, soil carbon, N, P, and K, based
on their locations in Scotland. SOCiT, also an application
aimed at users in Scotland, gave information about soil’s
carbon content based on users’ geographical positions [15].
The location information could be used to look up environ-
mental contexts such as climate and elevation of the area.
Then, a mathematical model predicted topsoil organic matter
and carbon content based on geographical data along with
accompanied soil images.
Water Study. Water quality affects farming and agriculture
in nearby regions. A project in Scotland, iDee, developed
a smartphone application dedicated to encourage users to
submit information of water conditions, that is, water level,
water clarity, obstruction in river, algae cover, temperature,
nonnative plants in water, and accompanying photographs of
the River Dee [15]. Other users in the area could then view
those water conditions data.
Crop Water Needs Estimation. Farmers also need to make
decisions on the amounts of water their crops need. Crop
water requirements depend on various conditions: crop
types, season, climate, and growth stages of crops [45].
Crops lose water through transpiration, while nearby soil,
water, and canopy lose water through evaporation. This water
loss process is collectively called evapotranspiration [45].
Crop water needs are analyzed to supplement water loss
in order for crops to grow according to farmers’ needs. A
smartphone application called PocketLAI helped farmers in

determining Leaf Area Index (LAI), which is a key factor to
calculate crop water requirements [9]. PocketLAI estimated
LAI using an indirect method based on sensors available on
modern smartphones. Two sensors were main components
of this application. Image sensors (cameras) were used to
take pictures of leaf canopy to estimate Leaf Area Index.
Accelerometers were used to obtain the angle of smartphones
as the devices were rotating in order to estimate LAI when
the gap fraction was precisely measured at 57.5∘
. The angle
57.5∘
was a directional view angle that had been shown in
the agricultural research to allow the estimation of LAI to be
free from leaf angle distribution and leaf degree clumping [9].
Once water requirements were determined from Leaf Area
Index, farmers could adjust their watering/irrigation tools
accordingly. Closely related to LAI calculation is brightness
analysis which estimates light brightness intercepted by
plants. In [8, 16], an application was developed to analyze
brightness using cameras on smartphones. The application
of brightness analysis was for estimating light radiation on
plants. Also, Molina-Mart´ınez et al. presented a mobile appli-
cation, RaGPS, for devices with Windows Mobile operating
system to calculate the extraterrestrial solar radiation and
other parameters related to solar position in real-time based
on the GPS data [26]. These radiation parameters were
useful for estimating crop water needs and validating weather
data provided by the agricultural weather stations. Their
experiments showed that RaGPS could estimate radiation
parameters very similarly to the ones from the weather station
in clear-sky days.
Crop Produce Readiness Analysis. An innovative use of
smartphone-based sensors is to determine ripeness of fruits.
In [8, 16], an application was developed utilizing smartphone
cameras. Pictures of fruits under white and UV-A light
sources were taken to determine ripeness levels for green
fruits. Farmers could integrate the system into their farms
by separate fruits of different ripeness levels into piles before
sending them to markets. With the help of computer vision
techniques, this process could be done in bulk rather than
farmers manually inspecting each fruit.
Smartphone applications under farming category support
farming processes of planting crop by sensing the environ-
ment using various sensors. Observations can be in the form
of snapped pictures, specific locations in the farm, colors of
soil, water, and plant leaves, and light. Main target users of
the applications are farmers who are involved with exhaustive
farming activities. This type of application aims at improving
farm productivity, oftentimes by analyzing agricultural spec-
imens, supporting agricultural decisions, and solving task-
specific problems. Smartphone applications which are under
farming category often share a common trait of being highly
computational as evident in [8, 9, 13, 14, 16, 17], compared with
smartphone applications in other categories.
4.2. Farm Management Applications. This subsection reviews
the literature on applications of smartphone-based sensors
for farm management. Research papers in this category
consider farming issues from a system perspective. Specifi-
cally, smartphone applications in the papers discussed here
focus on a whole farm system in contrast to the farming
applications discussed in the previous subsection where
each farming process, for example, disease identification and
prevention, fertilizing, and applying pesticide, was more con-
sidered. The main objective of the smartphone applications
in this category is to facilitate users to manage overall farm
resources and activities more efficiently and effectively in
order to achieve their goals, for example, to obtain maximum
profit and farming productivity.
Traditionally, farmers might rely solely on paper forms
and desktop computers to record, track status, and manage
farm resources and farming activities. While they are on the
field, they might keep field records, for example, amount of
pesticide and fertilizer used, locations and types of weeds
and disease found, and yields at each field on paper forms,
and then they enter the collected field data to a computer
when they are back at the office. Those data are later used for
report generation and various analyses (e.g., cost analysis) to
improve overall farming productivity. However, this conven-
tional data collecting approach often results in nonintegrated
data, making it difficult to extract valuable information from
such data. The recent development of smartphone technology
has transformed those farm management tasks in many
aspects. The mobile nature of the smartphone allows users
to access it anywhere anytime. Farmers can easily carry
a smartphone with them to the field to record field data
and manage farm resources right at the field. Moreover,
smartphone-based sensors such as microphone, camera,
GPS, accelerometer, and several others can tremendously
ease farm journaling and other farm management tasks.
For example, farmers can take photos of crops planted and
weeds found on the field with GPS coordinates. To label the
photos taken, besides typing, some applications use speech
recognition technology to aid users, for example, MapIT [19].
GPS technology also enables farmers to track equipment
and vehicles at different parts of a farm in real-time. In
addition, a common feature of smartphone applications in
the farm management category is that most applications are
connected to cloud servers where all farm data are integrated,
analyzed, and visualized in different formats to support deci-
sion making of farm managers/owners in order to improve
farming operations. The field data collected via a smartphone
are usually synchronized to the cloud servers and are easily
accessible from any devices connected to the Internet.
Water Management. Farm management applications assist
users to manage various farm resources. Water is one of
the crucial resources for crop growing. Better water resource
management means better yields and lower farm operating
costs. Researchers have proposed and developed smartphone
applications for effective water resource management. To
design effective water management strategies, water flow
data is necessary for daily operations of irrigation channels.
In general, to obtain such data, it requires measurement
stations, which are expensive, resulting in sparse and low
quality data used for water management decision making.
Smartphones allow water flow measurement tasks to be done
much easier at a lower cost, as it does not require permanent
station installation. L¨uthi et al. [18] designed and developed

an Android application for measuring open-channel flow.
The application estimates the water level, surface velocity,
and discharge rate by analyzing a short video recorded by
a smartphone of the water flow between two control points
with a known distance. To estimate the water level, the
algorithm relied on a separation line of image segments with
and without optical flow using a sequence of images. This
technique can be done because pixels in the dry parts of the
image generally remain unchanged over time, while pixels
on the water are subject to constant change. The surface
velocity estimation was implemented by a modified method
of the standard Particle Image Velocimetry (PIV) method.
The preliminary results reveal that the accuracy of the water
level, surface velocity, and runoff data obtained via their
smartphone application is about 5% of data obtained from a
commercial radar sensor.
HR Management and Farm Activity Journaling. Human
resources play an important role in farming productivity,
especially for farms in developing countries where agri-
cultural systems rely more heavily on manual labor than
machinery. The ability to keep records of farm workers’
activities such as weeding, fertilizing, and harvesting in each
area of the farm at specific time, schedule farm workers to
different jobs, and communicate work plans to farmers is
essential for effective farm management. Murakami et al. and
Murakami proposed iFarm, a system designed to assist in
recording field data and communicating work plans, using
smartphones, a cloud server, and web browsers [27, 28].
Farmers in the field use an application on smartphones to
record field data and send it to a cloud server. Farmers in the
head office can retrieve the information via a web browser.
The information can then be used to form a work plan and
communicate back to field farmers immediately as a task list
on their smartphones. The task list can be location-aware.
Although the authors did not state it explicitly, the location-
aware task list can be implemented using GPS. Sharma
et al. developed a machine learning algorithm application
to automatically detect farm workers’ activities (e.g., har-
vesting, bed making, standstill, and walking) based on data
collected from various sensors on smartphones carried by
farm workers while working in the farm [29]. Based on GPS
coordinates, the working pattern of farmers and frequency
of visiting certain plot of the farm can be inferred. The
usage of different machines, for example, tractor and chopper,
can be determined by sound data collected via microphone.
The data from 3-axis accelerometer are used to identify
various body movements. Such accelerometer data are also
tagged with timestamp and the GPS coordinates. The authors
investigated the accuracies of multiple classifier algorithms
including Naive Bayes, Linear Discriminant Analysis (LDA),
and 𝑘-Nearest Neighbor (𝑘-NN) in detecting various farming
activities. In addition, as different placements of the phone
result in different sensing data, they also studied the detection
of placement of the phone, trouser pocket and upper arm,
using the same three classifiers. Their experiment showed
that the LDA algorithm outperformed the other two in
terms of both activity detection and mobile phone placement
identification. These detected farm workers’ activity data are
useful for monitoring and improving the overall productivity
of the farm.
Vehicle Monitoring. Vehicle monitoring and location track-
ing are another promising feature of recent applications of
smartphone-based sensors. Liu and Koc introduced SafeDriv-
ing, an iOS application to detect a tractor rollover and
report emergency event [30]. Based on a tractor’s physi-
cal parameters and data from smartphone built-in sensors
including accelerometer, gyroscope, and GPS, the application
conducts a signal processing and uses a mathematical model
to calculate the stability index of the tractor. Once the stability
index drops below a certain threshold, a warning appears on
the phone screen. When the index is zero indicating a rollover
or accident condition, the application sends an email with the
date, time, and GPS coordinates of the accident location to
emergency contacts.
The applications discussed in this subsection so far
mainly target an individual farm manager/farm owner/
farmer at a farm level. However, farm management not only
occurs at an individual farm level; there are also available
applications that aim to manage collective farmland resour-
ces. These are tools to support decision making of top govern-
ment officials or policy makers to achieve an optimal solution
at a macro level, for example, an application to manage farm-
lands and irrigated areas covering a large region of a country.
Agricultural Land Management. The aggregated geo-data of
agricultural areas are useful for monitoring purpose and to
support decision making of policy makers. Frommberger et
al. developed MapIT, a crowdsourcing tool for collecting
geographic information of small objects and agricultural
areas [19]. To collect the geo-data, a user takes a photo of the
interested area/object and draws an outline of the object on
a smartphone screen. Through speech recognition or typing,
the user can label the object and then upload it to a server for
further analyses. To obtain the original size and shape of the
object, their implementation relied on the Douglas-Peucker
algorithm to firstly simplify the outline drawn by the user.
Then, based on the data from built-in inertial sensors, the
distance of the object from the camera, and GPS location,
the coordinates in the photo are projected to real-world
coordinates, yielding a geo-object of exact original geometry.
4.3. Information System Applications. Information is a key
factor in making effective decisions in all industrial sectors
including agricultural sector. It is widely recognized that
information can help farmers increase agricultural produc-
tivity. Up-to-date information about prices as well as market
demands helps farmers in choosing the type and amount of
crops to grow and where to sell their products, to maximize
profits. Prompt crop disease warnings and predictions allow
farmers to take actions to minimize damage. Updated farm-
ing knowledge educates farmers of new ways to improve their
crop yields. While such information may be available, some
of the main issues faced by farmers, especially small-scale
farmers, are that they are not able to meet the expected yield
quality, supply, and demand at the market level due to the lack

of information visibility at the time of decision making. The
fact that farmers are not able to make optimal decisions at
different stages of the farming life cycle results in a huge
impact on farmers’ revenue [46]. Innovative information
system for agriculture is an effective solution for this problem.
This section discusses three papers on this topic.
Information Localization. In order to provide relevant infor-
mation to farmers, Jain et al. proposed a hybrid architecture
which combines mobile and web technology and uses the
location of mobile device to improve the information sent
to farmers [31]. The main purpose of this paper is to design
a system that can provide the localized information which
is very crucial at one location but may be irrelevant to
other locations. A database is designed to couple information
with mapped location. When a farmer uses their location-
enabled mobile device, the mobile application sends the
location data to the server and receives localized agricultural
information. This helps farmers receive relevant information
without having to read through enormous amount of data.
While the authors did not specify which sensors to be used,
the location data can be retrieved using GPS.
Pest and Disease Information. In contrast to the previous
solution which provides a general framework for agricultural
information, two papers have discussed solutions for pest and
disease management. Rafoss et al. used GPS-enabled mobile
phones as a way to fight fire blight in Norway in SMILEX
system [32]. As mobile phones connected to the Internet
provide a way to quickly receive information and report
disease outbreaks, stakeholders (e.g., farmers, policy makers,
and field workers) can take appropriate actions to minimize
the damage. The application was implemented for mobile
devices to be able to display disease outbreak reports on a map
and users can also report or edit disease outbreaks. GPS is
used to access current location of the mobile phone to retrieve
the nearby map and the information. While the case study
was conducted on the fire blight survey in Norway, the system
is applicable for disseminating most crop disease outbreak
information. Similarly, Suen et al. proposed VillageTree which
is a mobile application that provides intelligent pest man-
agement solutions [20]. VillageTree gathers pest incidence
reports from farmers using crowdsourcing approach. Then,
the incidence reports which include location data and images
taken of crop disease are processed in their server using
spatial-temporal analytic and image recognition algorithms.
Finally, their system produces warning data and sends alerts
and relevant solutions to farmers for them to take preventive
measures.
Farmer’s information needs can be separated into three
stages: know-how, context of planting, and market informa-
tion [47]. Know-how includes information about crop choices
and seed varieties. The knowledge of new crops or seed
variety that improve yields is beneficial in improving rev-
enues. Context of planting is information about weather, plant
protection, and cultivation practices. The nature of context
information is dynamic (e.g., weather changes everyday and
pest outbreaks that happen irregularly) and has high impacts
on crop yields. Market information, which refers to market
prices, market demands, and logistics to sell products, is
essential in turning products into revenues. The applications
discussed above provide innovative solutions, but only for a
subset of the information needs. Reference [31] is a general
solution which is applicable to all stages, while [20, 32]
provide solutions that take crowdsourcing approaches for
context stage.
The nature of smartphone applications in this category
is a channel for receiving and sending information, which
requires less computation and information entry on the
smartphone than the applications in previous categories.
While it is less obvious how sensors could be used for
applications in this category, GPS has been successfully used
to enhance the information exchange in the three papers
described.
4.4. Extension Service Applications. In many countries
around the world, agriculture is the main economic driver,
but farmers are still poor and undereducated. Governments
provide agricultural extensions as a service that reaches out
to farmers to provide assistance, for example, educating
farmers of new crops, helping farmers diagnose crop disease,
and registering farms to provide subsidy for disaster-affected
areas. Conventionally, agricultural extension services were
provided by extension workers and experts who visited farms
to give farming advice and solutions to specific problems,
which required traveling and were resource intensive.
Smartphones and information technology have recently
made the process easier and more accessible. This section
reviews four research papers that facilitate extension services.
Pest and Disease Inspections by Experts. Agricultural exten-
sion services include the activities that provide personalized
information and solution to rural farmers, one of which is
to assist farmers in diagnosis of pest or diseases found in
the farmer’s plants, so that proper solutions or actions can
be taken to alleviate the problems. A few researchers have
explored the use of information technology and smartphones
to enhance this area of agricultural extensions. Jhunjhunwala
et al. implemented a voice-based service through call center
to provide personalized solution to 1,200 farmers in Tami
Nadu, India [21]. This project was inspired by the success
of call center business model in modern industries, which
provides personalized solution to customers who face techni-
cal problems and seek help through their mobile phones. In
call center web technology, customer information is logged in
their database and some FAQ pages allow relatively untrained
call center operators to assist with the customer’s problems.
Indian Agricultural Advisory System (AAS) was built by
using the call center technology. Farmers can call in to seek
help. Farmers can also send pictures taken from their mobile
phones to the central server to allow call center personnel
and experts to visually inspect their problems. Location data
from the mobile device informs call center operators and
experts about where the problem occurs. Similarly, Saha et al.
explored the design and implementation of an Android-
based prototype, m-Sahayak, to allow experts to diagnose
and treat plant’s (and people’s) disease remotely [22]. When
farmers face diseased plants, they can take photos or videos of

the plants and use audio to record their query using their local
spoken language. The photos, videos, and audios are sent to
the server for agricultural scientist to use the data to provide
proper solutions accordingly by call or SMS. In another paper
proposed by Jagyasi et al., mKRISHI’s phone application
allows a farmer to send their query using text, voice, pictures,
and videos in a similar manner with m-Sahayak [24]. While
mKRISHI’s full system uses external sensors embedded in
farms (such as humidity and temperature sensors) to further
inform experts of field’s context, this part goes beyond the
scope of our survey.
As most research found in this area was done in develop-
ing countries, the target users’ level of literacy was a concern
and the use of a lot of textual data and advanced features
of smartphones for extension services in research has been
limited. Mobile applications that provide remote extension
services (similar to [21, 22, 24]) are still popularly functioning
using voice and SMS (e.g., [21]). However, this group of paper
is excluded from this survey because the papers do not utilize
smartphone features beyond voice and SMS communication.
Yet the remaining results still show that smartphone sensors
can enhance extension services. The sensors that have been
used in this category are cameras, microphones, and GPS.
Camera functionality is used to enhance the communication
between farmers and experts, because camera is easy to use
and images help communicate problems better than word
descriptions and allow experts to quickly and accurately
diagnose problems. Microphones allow farmers to record
their query as spoken words, which provide an alternative for
them when forming queries. GPS adds location as additional
information for experts.
Tools for Extension Workers. A different way that smartphones
can facilitate extension services is to provide a convenient
tool for extension workers. In land plot identification, con-
ventionally, technical staffs go to a farmer’s plot with multiple
devices (e.g., a GPS, a camera, and an electronic recorder to
record data such as the plot code, photograph identification,
or date) and management of the devices becomes a tedious
task. The staffs have to make sure that the battery of each
device is sufficient beforehand and have to switch between
devices while working in the field. Mesas-Carrascosa et al.
proposed GeoFoto, an Android-based mobile application that
assists field workers in land plot identification, which reduces
the number of tools the extension worker has to carry into
the field [23]. GPS and image data captured from the phone
are sent to the central office as field workers visit farms.
The data is then analyzed to verify that the correct field is
visited. While these tools are not directly used by farmers,
they help extension workers in providing extension services
more efficiently.
5. Discussion
5.1. Summary of Current Usage of Smartphone Sensors. This
section discusses how smartphone sensors are used in agri-
cultural research. Table 2 summarizes the papers that use
smartphone sensors that were described in the previous
section. The most popular sensors in agriculture applications
are cameras (used by 16 applications) and GPS (used by
14 applications). Other sensors that are applied include
microphone, accelerometer, and gyroscope.
As a large number of smartphone applications in Table 2
utilize cameras and GPS, we examine further into both
hardware sensors. GPS are mostly used for location-aware
applications, which send/receive essential environmental
information for agriculture (such as pests and diseases [20,
25, 32], weather, soil [15], water [15], and radiation [26]),
farm management information [27–29], locations of accident
[30], locations of fields [23], and locations of farmers [29],
based on users’ locations. The aforementioned location-
based information seems to be able to tolerate some levels
of location inaccuracy as environmental information or
locations of farmers/fields/accidents can be reported in the
vicinity of meters. Therefore, GPS on smartphones, even low-
priced ones, represent an ample choice of GPS hardware
specifications.
As for image sensors on smartphones, requirements of
cameras’ technical specifications may be analyzed in two
ways. Cameras can be used to take pictures or videos which
are then sent and stored as a whole on servers or on the
cloud for future reference or further inspection [15, 21–24].
Cameras can also be used to take pictures or videos to be
image-processed further [8, 9, 13–20]. The former requires
images/videos to be in a sufficiently good enough quality
for human inspection. The latter, however, calls for higher
quality image sensors for the purpose of image processing.
Furthermore, smartphones expected to perform onboard
image processing need to meet certain computing power.
While some papers report image resolution in which the
applications were tested (e.g., 5-megapixel in [9], up to 8-
megapixel in [13]), some researchers specify processing power
requirement [9]. Whether these applications are able to assist
farmers, farm managers, and government workers effectively
depends on smartphones’ specifications.
5.2. Advantages of Using Smartphone-Based Sensors with
respect to Traditional Methods in Agriculture. This section
compares the utilization of smartphone-based sensors in
agriculture to that of traditional tools and methods. Table 3
gives a synopsis of advantages of using smartphone-based
sensors over traditional methods for each smartphone appli-
cation. The applications are ordered by categories previously
described in Section 4.
5.3. Challenges of Using Smartphone-Based Sensors in Agricul-
ture. This section explores challenges in the design, develop-
ment, and implementation of using sensors on smartphone
to assist agricultural decisions and management. Challenges
that may arise when developing agricultural applications
using smartphone-based sensors are divided into challenges
due to the nature of the sensors and due to the nature
of the applications. First, technical challenges of utilizing
smartphones’ onboard sensors instead of dedicated sensors
are discussed. Then, we examine challenges of building
smartphone applications to meet agricultural requirements.

Table2:Summaryofsmartphone-basedsensorsinagriculturalcontext.
SensorsCategoryArticleYearApplicationnameFeatures
CamerasFarming[13]2014n/aPicturesofleavesaretaken,thenprocessed,andsenttolabsforfurtherdiseasediagnosis
CamerasFarming[14]2013n/aCamerasfunctionassoilcolorsensors
CamerasFarming[15]2013iDeePhotographsoftheRiverDeearecapturedforwaterconditionassessment
CamerasFarming[15]2013SOCiT
Photographofthesoilofinterestiscapturedfortopsoilorganicmatterandcarboncontent
prediction
CamerasFarming[8,16]2013n/aBrightnessisanalyzedforestimatinglightradiationonplants
CamerasFarming[8,16]2013BaiKhaoNKPicturesofriceleavesaretakentoanalyzechlorophyllcontents
CamerasFarming[8,16]2013n/a
PicturesoffruitsunderwhiteandUV-Alightsourcesaretakentodetermineripenesslevels
forgreenfruits
CamerasFarming[9]2013PocketLAI
ImagesofleafcanopyareacquiredtoestimateLeafAreaIndex(LAI),whichinturngives
cropwaterrequirements
CamerasFarming[17]2012BaiKhaoPicturesofriceleavesaretakentoanalyzechlorophyllcontents
CamerasFarmmanagement[18]2014n/a
Ashortvideoofwaterflowistakenandthenanalyzedtoestimatethewaterlevel,surface
velocity,anddischargerate
CamerasFarmmanagement[19]2013MapITImagesofobject/areaofinterestarecapturedforgeographicinformationcollectingpurpose
CamerasInformationsystems[20]2014VillageTree
Farmerstakepicturesofpestandsendtothecentralserver,wheretheimagesareprocessed
toanalyzetypesofpests
CamerasExtensionservices[21]2013
AgriculturalAdvisory
System(AAS)
Imagesofanyinfestationorpotentialproblemarecapturedandsenttotheoperator’s
dashboardinreal-timeusingPestandDiseaseImageUpload(PDIU)feature,because
sometimesitisdifficulttofindwordstodescribetheproblems
CamerasExtensionservices[22]2012m-Sahayak
Imagesandvideosofthediseasedplantsarecapturedandsenttothecentralserverfor
agriculturalscientiststoinspectandprovidesolution
CamerasExtensionservices[23]2012GeoFoto
Imagesoflandplotsaretakenbytechnicalstaffsandsenttothecentralofficeasa
verificationthatthelandhasbeenvisited
CamerasExtensionservices[24]2011mKRISHIImagesandvideoscanbesentalongwithafarmer’squerytoanexperttoseekadvice
GPSFarming[25]2013MagriGPSlocationsareusedtogeneratelocation-awarepest/diseasealertstofarmers
GPSFarming[15]2013SIFSS
Soilcharacteristicsandinformation,suchaspH,soilcarbon,N,P,andK,aredelivered
basedonusers’GPSlocationsinScotland
GPSFarming[15]2013iDee
Userscanviewlocation-basedriverconditionsintheRiverDeecatchmentbasedontheir
GPSpositions
GPSFarming[15]2013SOCiT
User’slocationsareusedtoimplyelevation,climate,andgeologybasedonavailabledigital
mapsandthentopredicttopsoilorganicmatterandcarboncontent
GPSFarming[26]2011RaGPS
BasedonGPSdata,theapplicationcalculatestheextraterrestrialsolarradiationandother
parametersrelatedtosolarpositioninreal-time
GPSFarmmanagement[27,28]2013,2014iFarmTheapplicationsendslocation-awaretaskliststofarmersonthefield

Table2:Continued.
SensorsCategoryArticleYearApplicationnameFeatures
GPSFarmmanagement[29]2013n/a
Workingpatternsoffarmersandfrequencyofvisitingcertainplotofthefarmcanbe
inferredbasedonGPSlocationsinthefield
GPSFarmmanagement[30]2013SafeDrivingGPScoordinatesoftractorrolloveraccidentsarereported
GPSFarmmanagement[19]2013MapITImagesofobject/areaofinterestaremappedforgeographicinformationcollectingpurpose
GPSInformationsystems[31]2014n/a
Locationoftheuserissenttothecentralserver,whichsendsonlytherelevantinformation
aboutthatlocationbacktotheuser
GPSInformationsystems[20]2014VillageTree
Applicationusercanreportpestoutbreakwiththedevicelocation.Theinformationis
processedandapredictionissenttouserswhoselocationmightbeinvadedbythepest
GPSInformationsystems[32]2010SMILEX
Fieldworker’sfireblightreportfromthefieldiscoupledwiththelocationdata,sothe
incidentreportcanbeshownonthemapforbetterfireblightmanagement
GPSExtensionservices[21]2013
System(AAS)
FieldworkersuseGPSdatatorecordfarmlocationswhenregisteringfarmers.Spatialdata
(farmplotswithwaterbodies,drainageandirrigationchannels)areincludedinthefarmer’s
record
GPSExtensionservices[23]2012GeoFotoGPSdataisusedtoverifythatthecorrectlandisvisited
MicrophoneFarmmanagement[29]2013n/a
Theapplicationdeterminesusageofdifferentagriculturalmachines(e.g.,tractorand
chopper)bysounddata
MicrophoneFarmmanagement[19]2013MapIT
Microphoneisusedtoannotateimagesofobject/areaofinterestviaspeechrecognitionfor
geographicinformationcollectingpurpose
MicrophoneExtensionservices[22]2012m-Sahayak
Farmerscanusethemicrophonetorecordtheirvoicedquestionstosendtoagricultural
scientists
MicrophoneExtensionservices[24]2011mKRISHIAudioqueriescanberecordedandsenttoexperts
AccelerometerFarming[9]2013PocketLAI
Theapplicationobtainstheanglesofsmartphonesasthedevicesarerotatinginorderto
estimateLAIwhenthegapfractionispreciselymeasuredat57.5∘
,adirectionalviewangle
thathasbeenshownintheagriculturalresearchtoallowtheestimationofLAItobefree
fromleafangledistributionandleafdegreeclumping
AccelerometerFarmmanagement[29]2013n/a
Accelerometerisusedtodetectvariousbodymovementsinordertoidentifyfarmworkers’
activities
AccelerometerFarmmanagement[30]2013SafeDriving
Accelerometerisusedtoobtainarollangleandpitchangleofthetractorinordertodetecta
rolloverevent
GyroscopeFarmmanagement[30]2013SafeDriving
Gyroscopeisusedtoobtainarollangleandpitchangleofthetractorinordertodetecta
rolloverevent
Inertialsensorsa
Farmmanagement[19]2013MapIT
Basedonthedatafrominertialsensors,thedistanceoftheobjectfromthecamera,andGPS
location,thecoordinatesinthephotoareprojectedtoreal-worldcoordinates,yieldinga
geo-objectofexactoriginalgeometry
a
Thepaperdidnotmentionthespecificnameofthesensorsused.Generally,theterminertialsensorsisusedtocollectivelyrefertoaccelerometersandgyroscopes[33].

Table3:Comparisonofusingsmartphone-basedsensorsandtraditionaltools/methodsinagriculture.
ApplicationnameFunctionTraditionaltools/methodsAdvantagesofsmartphone-basedsensors
Farming
Diseasedetectionanddiagnosis
n/a[13]
Userssnapphotosofdiseasedleaves.Picturesarepro-
cessedandsenttolabsforfurtherdiagnosis
(a)Expertdiagnosiswiththenakedeye.(b)Lab
diagnosistechniquessuchasmicroscopy,culturing,
biochemicaltests,andELISA
(a)Farmerswithnoexpertiseindisease
identificationcanperformthediagnosis.
(b)Farmersreceivefastdiagnosis
comparedtosendingsamplesofdiseases
tolabs
Magri[25]
Usersreportinformationaboutpestdiseasesandreceive
advicefromMagriregardingthepests
Consultwithpestexperts
Farmerscanreceivelocalizedpest
information
Fertilizercalculator
Baikhao[8,16],
BaikhaoNK[17]
Usingpicturesofriceleaves,theapplicationsanalyze
chlorophyllcontentsandtheamountofnitrogenfertil-
izerneededforthericefield
(a)VisualinspectionusingLeafColorChart(LCC)(b)
SPADanalyzer
(a)Theycanbemoreaccurateandmore
reliablethanvisualinspection.a
(b)They
arecheaperthanpriceySPADanalyzers
Soilstudy
n/a[14]
Munsellsoilcolorisdeterminedusingimagesensorsin
ordertosuggestthestructureandcomponentsofsoil
(a)VisualinspectionusingMunsellsoilcolorchart.(b)
Traditionalinstruments,forexample,colorimetersand
spectrometers
(a)Itisnotsubjecttoaccuracyproblemsb
asinvisualinspection.(b)Using
smartphonesasproximalsensorsis
simpler,cheaper,faster,lesslaborious,
andmoreflexiblethancomplexand
costlyinstruments
SIFSS[15]
SIFSSgivesusersaccesstothefullsoildatasetfromthe
SoilSurveyofScotland
RequestinginformationmanuallyfromtheSoilSurvey
ofScotland
Itisasimplerandfasterwaytoaccess
information
SOCiT[15]
SOCiTpredictstopsoilorganicmatterandcarboncon-
tentfrompicturestakenagainstacolorcorrectioncard
andotherenvironmentalcharacteristics,forexample,
elevation,climate,andgeology
Wetanddrycombustionmethods(whichare
analyticallyaccuratebutexpensive)
SoilanalysisusingSOCiTisquickerand
morecost-effective
Waterstudy
iDee[15]
iDeeprovidesavenueforriverconditionreportingand
viewingforcommunitiesaroundRiverDeecatchment
inScotland
Manuallyexchanginginformationwithinthe
communitiesviatraditionalcommunicationchannels,
forexample,surveysandforms
Itisasimplerandfasterwaytoaccess
information
Cropwaterneedsestimation
n/a[8,16]
Thesmartphoneimagesensorisusedtomonitorthe
brightnessofthelightradiatingonplants
Luxmeter
Smartphonesnowadaysarehighly
availableandubiquitous
PocketLAI[9]
PocketLAIdeterminesLeafAreaIndex(LAI),whichisa
keyfactortocalculatecropwaterrequirements
(a)DirectmethodsmeasuringLAIdirectlyonleaves
gatheredfromplantsc
orbyimageprocessingofleaf
images.(b)Indirectmethodsd
estimatingLAIfromgap
fractione
(a)PocketLAIisnotdestructivetothe
plantsbecauseitusesanindirectmethod.
(b)PocketLAIischeaperandmore
portablethanusuallyexpensivededicated
devices
RaGPS[26]
RaGPScalculatessolarradiationandequivalentevapo-
rationfromsolarpositionusingGPSinsmartphones
Obtainingsuchdatafromnearbyagriculturalweather
station
Itcanestimatecropwaterneedswhere
thereisnoweatherstationnearby

Table3:Continued.
Cropproducereadinessanalysis
n/a[8,16]
Smartphoneimagesensorsserveasatwo-dimensional
sensormeasuringripenesslevelofbananas
(a)Manualestimationwiththenakedeye.(b)
Quantitativeestimationmethods,includinganalyses
formechanical,chemical,andaromaticcomponents,
someofwhicharedestructivetothefruititself[34]
Itisanondestructivemethod,whichis
idealforpreservingtheactualfruits
Farmmanagement
Watermanagement
n/a[18]
Theapplicationestimatesthewaterlevel,surfaceveloc-
ity,anddischargeratebyanalyzingashortvideoofthe
waterflowbetweentwocontrolpointswithaknown
distance
Toobtainsuchwaterflowdata,ittraditionallyrequires
expensivemeasurementstations,resultinginsparse
andlowqualitydatausedforwatermanagement
decisionmaking
(a)Smartphonesfacilitatethe
measurementtaskstobedonemuch
easieratlowercosts(verylowinitialcosts
andnomaintenancecost).(b)Userscan
takewaterflowmeasurementanywhere.
(c)Lesstimeisrequiredtotake
measurementsanddatacollectioniseasy
viaSMS
HRmanagementandfarmactivityjournaling
iFarm[27,28]
iFarmallowsfarmersinthefieldtorecordfielddataand
communicatewithfarmersintheofficethatcanforma
workplanandcommunicatebacktofieldfarmersimme-
diatelyasalocation-awaretasklistontheirsmartphones
Writingfielddataonpaperandlatermanuallyentering
thosedataintoacomputerattheoffice
(a)Traditionalfielddatacollection
approachesoftenresultinnonintegrated
data,makingitdifficulttoextract
valuableinformationfromdata.(b)
Farmerscanrecordfielddataandmanage
farmresourcesrightatthefield.(c)GPS
makesalocation-specificdatacollection
andalocation-awaretasklistcreation
possible
n/a[29]
Usingdatafromaccelerometer,GPS,andmicrophone
sensors,theapplicationautomaticallydetectsfarm
workers’activities,forexample,harvesting,bedmaking,
standstill,andwalking
Variouskindsofdedicatedbodywearablesensorshave
beendevelopedtodetecthumanactivitiesf
Comparedwithconventionalbody
wearablesensors,smartphonesaremore
integrated,hencereducingusers’
rejectionduetothepooraestheticvalue
andintrusivenessofthetraditional
devices
Vehiclemonitoring
SafeDriving[30]
Basedondatafromaccelerometer,gyroscope,andGPS,
SafeDrivingdetectsatractorrolloverandreportsan
emergencyevent
RollOverProtectiveStructures(ROPS)arerequiredby
theUSOccupationalSafetyandHealthAdministration
(OSHA)tobeinstalledonallagriculturaltractorsto
protectoperatorsfromrollovers[30]
AlthoughROPScanreducetheseverity
oftherolloveraccidents,theydonot
provideanemergencyreportingtorescue
teamsasinthesmartphoneapplication
Agriculturallandmanagement
MapIT[19]
MapITisacrowdsourcingapplicationforcollecting
geographicinformationofsmallobjectsandsmallagri-
culturalareas.Ausertakesaphotoofanareaanddraws
anoutlineoftheareaonasmartphonescreen
Tomaplargeobjects,itusuallyrequiresauserto
physicallytraversethegeographicobjectwithaGPS
device.Sometoolsusesatelliteimagesasbaselinedata
Withthetraditionalmethods,mapping
smallobjectsisatime-consumingtask
sinceGPSisusuallyunreliableatthis
levelofdetail,whichrequires
considerableefforttorevisetheoutline
data.MapITisacamera-based
applicationthatisaccurate,easy-to-use,
andsuitableforrelativelysmallobjects

Table3:Continued.
Informationsystem
Informationlocalization
n/a[31]
Farmerscanaccessagriculturalinformationthatis
relevanttotheirlocationviamobileandwebtechnology
Farmerscanaccessinformationviamobileandweb
technologybuthavetomanuallysortthrough
informationthatarenotrelevanttothem(e.g.,language
notinthelocaldialect,diseaseoutbreakindistantarea)
Locationinformationfromfarmers’
mobilephoneallowsonlyrelevant
informationtobeshowntofarmersand
reducesinformationoverload
Pestanddiseaseinformation
SMILEX[32]
Fieldworker’sfireblightreportsfromthefieldareshown
onthemapforbetterfireblightmanagement
Alertsandwarningshavebeendistributedoverthe
internetsinceearly1990s,buttheuseofmodern
technology(e.g.,GPS)hasbeenlimited
Smartphoneenablesreal-timereports
fromthefieldwithlocationinformation
usingbuilt-inGPS.Thisallowsbetter
managementofeventsandbetter
informationdissemination
VillageTree[20]
Userscansendimagesalongwithlocationsofpest
incidentstobeprocessedandalertotherrelevantusers
Pestreport,inspection,andwarninghavetraditionally
beendonemanually,whichisslowandtedious
Pestimagesandlocationsfrom
smartphoneallowthesystemtodetect
pestandwarnotheruserswhomightbe
affected
Extensionservice
Pestanddiseaseinformationbyexperts
System(AAS)[21]
Farmerscancallthiscallcentertoseekpersonalized
solutionfortheirfarmingproblems.Imagestakenfrom
theircameraandlocationfortheirphonescanprovide
informationforthecallcenterabouttheproblem
Thispapertargetsfarmersinruralareaswherethereare
notenoughexpertstoprovidesolutions.Therefore,
traditionalmethodistheinsufficientexpertfieldvisits
Smartphoneallowsfarmerstobetter
communicatetheirproblemstoexpert
remotelyandreceivepromptand
personalizedsolution
m-Sahayak[22]
Farmerscantakephotosorvideosoftheplantsand
useaudiotorecordtheirqueryusingtheirlocalspoken
language
Telephoneservicesforagriculturalsolutionexistforthe
targetusersofthispaper,butcommunicationfailure
occursduetofarmers’inabilitytodescribetheirdisease
Smartphonesensors(microphonesand
cameras)assistfarmersin
communicatingtheirproblems
mKRISHI[24]
Farmerscanuseaudio-visualfacilitiesonamobile
phonetoarticulatetheirqueriestoexpertswithminimal
useoftextg
Traditionaltoolsandmethodsaresimilartothoseof
m-Sahayak[22]
Smartphonesensors(microphonesand
cameras)assistfarmersin
communicatingtheirproblems
Toolsforextensionworker
GeoFoto[23]
Fieldworkersusethisapplicationinlandplot
identification
Fieldworkersmanagemultiplespecializeddevicesand
manuallytransferdatabetweendevices
Smartphoneapplicationprovidesasingle
devicetodothesametaskasmultiple
devices,whichmakesmanagementand
datatransfereasierandfaster
a
LCCischeap,butthechartislikelytodeteriorateovertime,causingthereadingofthericeleafcolorleveltobeincorrect.
b
Thisisdueto“illuminationconditions,samplecharacteristics,andobserver’ssensitivities”[14].
c
AnexampleofdedicatedinstrumentsisLi-3100C.
d
TheseincludesystemsusingdirectsunlightsuchasTRACandDEMON,differentmodelsofceptometerssuchastheSUNSCANortheAccuPAR,andsensorssuchastheLAI-2000orLAI-2200PlantCanopy
Analyzers.
e
Aviewableproportionofskyfrombeneaththecanopy(orthatofsoilfromtopofthecanopy).
f
However,thosededicatedsensorsstillhavebeentargetingurbanapplications,forexample,usingbodywornsensorstomeasurephysicalactivityformotivatingmoreworkout,orforfalldetectionandprevention.
Theworkin[29]aimsatutilizingsmartphone-basedsensorstodetecthumanactivitiesinthecontextofruralfarming.
g
Whilethispaperdiscussestheuseofexternalsensorstocreatecontextualdataforexpertstoprovidemoreinformedsolution,thisreviewfocusesonthepartofpaperthatutilizesinternalsensors.

5.3.1. Challenges due to the Nature of the Sensors. Cameras on
recent smartphones might be of high specifications, but for
some applications they might still be inadequate in quality.
Applications that use cameras as sensors for image processing
run into trouble when lighting and angles of the objects
are not appropriate. Therefore, this poses a challenge for
application developers or researchers. In order to use camera
sensors as light analyzers, Sumriddetchkajorn describes using
an external diffuser on top of the smartphone camera to
control the lighting condition [8]. Similarly, Confalonieri
et al. utilize accelerometer on smartphones to ensure that the
gap fraction is captured when smartphones are at the correct
angle (57.5∘
to be exact).
In this case, smartphone application developers may
compare performances between using smartphone sensors
and other methods [8, 9]. Usually the nonsmartphone meth-
ods are a more direct way to perform the tasks but require
more specific expensive equipment, such as a spectroscopy
for soil study [10].
GPS data could be erroneous. While the specific usages
that have been reviewed in this paper do not require high
accuracy and can tolerate some errors, some of the possible
applications could be challenging in the context of smart-
phone GPS. For example, research suggests that modern
dedicated GPS equipment used in precision agriculture can
be as accurate as in centimeters [48]. A precise detection
of locations allows farmers to be equipped with a decision
support system. Coupled with sensing agricultural variables,
such as moisture levels, soil organic matter, and nutrition,
precise location detection by GPS enables variability within
farm management. Researchers and developers need to
define the scope of their applications in order to specify
required hardware specifications on the sensors.
5.3.2. Challenges in the Design and Development of Agri-
cultural Applications. Another challenge for mobile devel-
opers and researchers in creating practical smartphone-
based applications for agriculture is user interface design.
In general, the requirements and behaviors of smartphone
users are different across farming communities. For example,
rural farmers in developing countries might be constrained
by low level of literacy and lack of exposure to software
interfaces, which probably make the user interface designs
for large-scale farms in developed countries inappropriate for
them. It is necessary for smartphone application developers
to realize this difference and customize the interface design
to their target users. Applications which target rural farmers
need to have highly intuitive interfaces; using a lot of text
as interface might be incomprehensible or difficult for them.
Although this fact has been taken into account in the
development process of some applications such as in [20],
many applications still do not concern this aspect.
Some applications that were reviewed pose some chal-
lenges due to the nature of the approaches taken by the
application. Saha et al. [22] and Jain et al. [31] took the
crowdsourcing approach to assist with the management of
pest and disease outbreaks. While this approach allows
rapid and distributed data to be inputted to the system,
which has advantages over traditional centralized approach,
the nature of crowdsourcing approach requires checking of
the correctness of the data. The papers did not specify any
correctness checking which could pose a problem for the
model for pest prediction [22] and the alerts that are sent
could be less credible when users make error in reporting.
5.4. Future Research Opportunities. This section synthesizes
future research opportunities from the papers that were
reviewed. Future research directions are grouped into cate-
gories as follows.
Exploration of Unused Sensors. There are many built-in
sensors in smartphones which have yet to be explored in
the context of agriculture. Physical sensors, for example,
barometer and humidity sensor, can be rather easily extended
to the agricultural field by performing their original intended
functions of environmental sensing. This leaves an open
opportunity for researchers and developers to invent useful
applications that are enabled or enhanced by these sensors.
Extension of Sensor Usage from Existing Research. Moreover,
the sensors that have already been explored can be further
explored by replicating previous techniques to smartphones.
Computer vision techniques have been studied in the context
of agriculture extensively, many of which were proposed as
offline computation, for example, weed identification [49]
and cotton foreign matter inspection [50]. The current state
of server computational power can allow some of these
algorithms to be deployed where images are taken using
smartphone cameras, sent to server for processing, and the
results are returned to senders on their mobile phones.
Additionally, smartphone accelerometers have been
proven useful in other fields such as for daily physical activity
recognition [51] and for prevention of major health hazards
such as fall detection [3]. However, the approaches have been
limitedly applied to agricultural field. Similar approaches
can be applicable to recognize various farming activities and
to provide further assistance in farm journaling. The work
in [29] utilized sensing data from accelerometer to detect
farming activities including standstill, walking, bed-making,
and harvesting, but indeed there are open opportunities to
apply similar techniques to detect other farming activities
such as sowing, watering, and spraying.
Improvement of Algorithms to Process Sensing Data. In addi-
tion to using accelerometer data to detect various types
of farming activities, researchers can further improve the
algorithms to detect if a farmer performs a certain farming
activity in an appropriate manner or not. For example,
utilizing data from accelerometer, an algorithm might detect
from a farmer’s hand movement if he/she is sprinkling
fertilizer in a proper way. If not, the application could give
a real-time recommendation to the farmer to correct his/her
hand movement [29].
Smartphone Battery Usage Reduction. We noticed that most
of the papers reviewed here did not consider the battery
efficient aspect in the design and development process of
their applications. Indeed, one nature of farm work is that

farmers are usually away from power source, preventing them
from recharging their phones once they run out of battery.
Therefore, design and development of a battery efficient
application is crucial for the practicality of any smartphone
application and could be another future direction for research
in this area [29].
Appropriate User Interface Design for the Target Users. As
mentioned in Section 5.3.2, the wide range of target users
for agricultural sector creates a challenge for developing
appropriate mobile user interfaces. Intuitive interfaces for
respective user groups are important for the success of
smartphone applications [20]. Level of literacy and familiarity
with gadgets are two of the key factors to consider in
smartphone application design.
Scalable and Sustainable Business Model and Longitudinal
Study. The majority of research papers covered in this review
proposed new ideas and invented new systems to solve
problems that arise in agriculture for a certain group of users.
While they provide a great solution, most of the solutions are
tested in small scale and for a short period of time. It would be
useful to extend the proposed solutions spatially and tempo-
rally. However, extending a research prototype to a scalable
and sustainable solution requires new business models [21].
The success of such models would allow longitudinal studies
to be conducted where data is gathered for the same subjects
repeatedly over a period of time, which allows us to better
understand how effective and sustainable a solution is over a
period of time.
Solutions for External Factors Affecting the Effectiveness
of Smartphone Applications. Multiple problems have been
reported in using smartphone applications, for example, poor
mobile network coverage [23, 32]. While it is expected that
network coverage will be continually improved, providing a
solution that adapts to network coverage would be beneficial.
That is, an application would make use of the Internet when it
is available but also allows users to perform their task without
the Internet and synchronize with the appropriate database
when there is network connection again.
5.5. Limitations. The scope of this systematic survey includes
only smartphone applications published in the research
literature. While, from our brief searches, there are many
interesting applications on the markets both in the App Store
and the Play Store, we decided to exclude them from the
survey because it is difficult to accurately and thoroughly
understand the functionality and how the sensors were used
in the applications simply from the descriptions provided by
application developers. In contrast, research papers provide
detailed description, methodology, and evaluation of the
work that was done. While we did our best attempt to
review all potentially relevant articles, we could not access 24
papers in full text. Therefore, this survey does not claim to
contain the most comprehensive list of invented smartphone
applications for agriculture which utilize onboard sensors.
However, our systematic survey method, despite yielding
a bounded set of results, was further analyzed extensively on
application features and sensors.
6. Conclusions
This paper presented the state-of-the-art survey of the
research literature in how smartphone sensors have been
used in agriculture, without the needs for external sensors.
The total of 22 articles describing 25 smartphone-based
agricultural applications were found to be within our scope.
Cameras and GPS were the most widely used sensors. Out
of 25 applications, cameras and GPS were used in 16 and
14 applications, respectively. Other sensors (microphones,
accelerometer, and gyroscope) were used by a few applica-
tions each.
We may draw a few conclusions from our systematic
review. First, there is an availability of a number of helpful
smartphone applications for target farmers. Most of these
applications are easily accessible provided that target users
have access to basic smartphones, because most of the
reviewed applications devise two sensors, cameras and GPS,
and because the two sensors are available in almost all
basic smartphones. Second, researchers and smartphone
application developers reading our review paper may have
an impression of research gap regarding which agricultural
applications to develop using smartphone-based sensors, as
discussed in Section 5.4. Third, governments and agricultural
agencies can respond to how even the most basic sensors on
smartphones can be used in agriculture. Since agricultural
work is context-based, which is primarily distinguishable
by different geographical locations, smartphone applications
already available in one scope of context can be developed to
fit other crops or countries or regions.
Conflict of Interests
The authors declare that there is no conflict of interests
regarding the publication of this paper.
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