SALATA: A Web Application for Visualizing Sensor Information in

Farm Fields

Nao Akayama

, Daisaku Arita

, Atsushi Shimada

and Rin-Ichiro Taniguchi

Faculty of Information Science and Electrical Engineering, Kyushu University,

744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan

Faculty of Information Systems, University of Nagasaki,

1-1-1 Manabino, Nagayo, Nishisonogi, Nagasaki, 851-2195, Japan

Keywords:

Smart Agriculture, IoT, Visualization, Sensor Information in Farm Fields, Usability, Web Application, User

Interface.

Abstract:

Semi-automated sensing and visualization of conditions and activities in farm ﬁelds have been actively pursued

in recent years. There are three types of agricultural information: sensor information, farm work information,

and plant biological information. Measuring and visualizing these agricultural information can provide valu-

able support to farm managers. In this study, we focus on sensor information and farm work information and

develop a web application named SALATA (Sharing and AccumuLating Agricultural TAcit knowledge) that

collects and shares sensor information and farm work information collected in farm ﬁelds and correlates the

information in time series. SALATA need to have intuitive operation and quick response in order that people

of various ages will use it on a daily basis. Therefore, there are two primary pages: the main page for visual-

izing simple information quickly and the analytical page for visualizing multiple pieces of information on one

page. Usability evaluation experiments are performed, showing that SALATA can be operated intuitively and

respond quickly.

1 INTRODUCTION

Japan is an island country surrounded by an irregu-

lar coastline. It is also a mountainous country with

small ﬂatlands. These geographical features bring di-

verse climates and soils to Japan. Japanese cultivation

has been hindered by the increase in scale, and spe-

cialized agriculture is also practiced in various kinds

of local conditions. These characteristics of Japanese

cultivation make it difﬁcult to accumulate cultivation

knowledge and to automatically control farm ﬁeld en-

vironments, even in a greenhouse based on cultivation

know-how. Therefore, farm managers must, by them-

selves, acquire specialized know-how for their own

farm ﬁelds.

The problem of knowledge acquisition is becom-

ing more critical owing to the rapid ageing of the

Japanese population. In mountainous farming ar-

eas, for example, 39% of farmers are over 65 years

old(Ministry of Agriculture, Forestry and Fisheries,

Japan, 2019). Many experienced farm managers are

retiring every year due to their age. In addition, many

young farm managers are leaving the farming profes-

sion owing to lack of cultivation know-how.

In recent years, rapid advances in technologies

related to the Internet of Things (IoT) have made

various sensor devices and their networks popular

and economical. Therefore, small farmers in Japan

can introduce sensor networks to their farm ﬁelds

to measure and quantify cultivation know-how. The

Japanese Government also promotes this movement.

As a result of these circumstances, we are developing

SALATA, which is a web application to facilitate the

visualization of various types of farm ﬁeld data, and

to support farm managers in acquiring important cul-

tivation know-how and awareness of causal relation-

ships among ﬁeld environments, farm work activities,

and crop conditions.

2 RELATED WORK

There are three types of agricultural information that

are of interest in this study: sensor information, farm

work information, and plant biological information.

Measuring and visualizing these agricultural data can

Nao, A., Arita, D., Shimada, A. and Taniguchi, R.

SALATA: A Web Application for Visualizing Sensor Information in Farm Fields.

DOI: 10.5220/0008987501130120

In Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020), pages 113-120

ISBN: 978-989-758-403-9; ISSN: 2184-4380

113

provide valuable support to farm managers. Wjtow-

icz et al.(W

ojtowicz et al., 2016) described the impor-

tance of sensor information, which are of three types:

satellite, airborne, and ground based. These infor-

mation sources contribute to the forecasting of crop

yield, assessment of nutritional requirements of plants

and the nutrient content in soil, and determination of

plant water demand and weed control. This optimizes

the proﬁtability of crop production and improves en-

vironmental protection. In this study, we focused on

sensor information and farm work information. There

has been much research and many developments re-

lated to visualization of sensor information and farm

work information.

For example, MIHARAS

, developed by Nishimu

Electronics Industries Co. Ltd., Agrilog

, developed

by ITKOBO-Z Co. Ltd., and SEnviro(Trilles Oliver

et al., 2019) visualize sensor information. MIHA-

RAS provides an open-ﬁeld sensor unit, a weather

sensor unit, and a paddy ﬁeld sensor unit. The open-

ﬁeld sensor unit measures temperature, humidity, soil

moisture, soil electrical conductivity, and soil temper-

ature. The weather sensor unit measures wind direc-

tion, wind speed, rainfall, solar radiation, tempera-

ture, and humidity. The paddy ﬁeld sensor unit mea-

sures temperature, humidity, water level, water tem-

perature, and soil temperature. MIHARAS visualizes

the information from these sensors on personal com-

puters (PCs), smartphones, and tablets (hereinafter,

referred to as user devices). Agrilog is an environ-

mental monitoring service for greenhouses that can

measure temperature, humidity, CO

concentration,

solar radiation, soil temperature, soil moisture, and

soil electrical conductivity. This sensor information

is visualized by user devices, and some processed in-

formation such as an average temperature can be cal-

culated and plotted. In addition, users can share their

sensor information with each other. SEnviro can mea-

sure temperature, humidity, barometric pressure, soil

moisture, wind direction, wind speed, and rainfall.

This sensor information is visualized by a web ap-

plication in real-time. The primary aim of SEnviro

is to use disease warning models of crops in order to

alert users to the danger of crop infection. Sensing

and analyzing in real-time enable them to tackle the

infection with the appropriate treatments.

The Priva FS Reader

, developed by Priva Inc.

and Hashimoto et al.(Hashimoto et al., 2016), pro-

vides visualization of farm work information. The

Priva FS Reader is a device that can easily record

farm work information. It was developed to manage

https://www.nishimu-products.jp/miharas

https://itkobo-z.jp/agrilog

https://www.priva.com/us/products/fs-reader

a large number of laborers on a large farm ﬁeld. A

special tag is installed at the edge of the each ridge in

the farm ﬁeld, making it possible to perceive where

the farm laborer worked by scanning it with a device

each farm laborer wears. For example, by associating

with the box ID that contains the yield, it is possible

to perceive when and how many were harvested, and

by whom. In addition, crop conditions, diseases, and

pests can be easily registered and visualized on a map.

Hashimoto et al. estimated a farm laborer’s position

in a greenhouse by using a smartphone and beacons

placed in the greenhouse. At the same time, they es-

timated the farm laborer’s actions to harvest based on

the worker’s motion as measured by smartwatches,

and subsequently measured the yield. Finally, they

created a harvesting map by integrating the position

and action information. Using this, they can generate

much detailed farm work information.

Midori Cloud

, developed by SERAKU Co. Ltd.

visualizes sensor information and farm work informa-

tion on a user device by installing a sensor unit in a

greenhouse. This sensor unit measures temperature,

soil temperature, humidity, CO

concentration, and

solar radiation. In addition, the Midori Cloud also

has a function to record farm work information called

Midori Notes. However, sensor information and farm

work information are not integrated for visualization.

It is necessary to integrate and visualize sensor in-

formation and farm work information, because there

are causal relationships between them. For example,

if high solar radiation continues, the yield, or the har-

vesting work will increase, and if the greenhouse is

ventilated, the CO

concentration will change rapidly.

SALATA, our web-based application, visualizes both

sensor information and farm work information such

that farm managers can easily understand the causal

relationships between them.

3 SHARING AND

ACCUMULATING

AGRICULTURAL TAcit

KNOWLEDGE (SALATA)

We have developed SALATA to visualize sensor in-

formation and farm work information. We anticipate

that farm managers will improve the farm ﬁeld envi-

ronment by using SALATA as discussed below.

1. SALATA collects sensor information and farm

work information.

https://info.midoricloud.net

SENSORNETS 2020 - 9th International Conference on Sensor Networks

114

2. SALATA visualizes sensor information and farm

work information.

3. Farm managers analyze and compare with the

other farm ﬁeld’s information or the past informa-

tion.

4. Farm managers improve the farm ﬁeld environ-

ment based on the analysis.

Moreover, it will be easy to understand the rela-

tionship between the farm ﬁeld environment and the

farm manager’s decisions by associating sensor in-

formation and farm work information in time series.

For example, if farm manager A ventilated the green-

house because of the increase in temperature. When

Farm manager B sees only the sensor information, he

can only understand that the temperature has suddenly

dropped. However, if farm work information, such

as ventilation changes, is posted by farm manager A

in SALATA, farm manager B can understand that the

temperature has suddenly dropped because farm man-

ager A has ventilated the greenhouse.

In this paper, we focus on visualizing sensor infor-

mation and indicate various functions and their eval-

uation results.

3.1 Sensor Information

In this study, we employ Midori Cloud developed by

SERAKU Co., Ltd. for farm ﬁeld sensing. The Mi-

dori Cloud sensor unit measures temperature, humid-

ity, solar radiation, soil temperature, and CO

con-

centration every two minutes. In addition to these pa-

rameters, we deal with ”humidity deﬁcit,” which is

the amount of water vapor that must be added to a

gas in order to achieve 100% humidity at the current

temperature. Humidity deﬁcit is one of the dominant

factors affecting the rate of photosynthesis. Humidity

deﬁcit D[g/m

] is calculated from temperature T [

◦

and humidity H[%]. The formula for calculating hu-

midity deﬁcit D is shown in Equation 1 below.

D =

(100 − H) × 217 × 6.1078 × 10

(7.5×T /(T +237.3))

(T + 273.15) × 100

(1)

where H and T represent the humidity and Celsius

temperature, respectively.

3.2 User Interface

SALATA must ensure intuitive operation and quick

response. This is because it is assumed that people

of various ages will use it on a daily basis. It seems

that there are farm managers of various ages and some

Figure 1: The main page of SALATA.

people are not familiar with user devices. SALATA

must have a user interface that can be operated intu-

itively by farm managers who are not familiar with

user devices. To that end, we uniﬁed SALATA’s con-

ﬁguration. In addition, it is assumed that SALATA

is used daily because farm managers usually do farm

work every day. In order for farm managers to use

SALATA daily, SALATA must offer a user-friendly

experience. To that end, we minimized the amount of

data loaded at once.

There are two primary pages displayed by

SALATA: the main page and an analytical page. If

the user wants simple information quickly, we recom-

mend the use of the main page. If the user wants to

view multiple pieces of information on one page, we

recommend the use of the analytical page. The main

page simply displays sensor information and farm

work information. This page displays real-time data

and dynamic graphs. On the analytical page, the user

can observe sensor information. This page displays

multiple-series graphs and parallel display graphs.

SALATA: A Web Application for Visualizing Sensor Information in Farm Fields

115

Figure 2: Dynamic graphical displays.

3.2.1 Real-time Data

The main page of SALATA is shown in Fig.1. At the

top of the page, there are buttons showing the cat-

egories of sensor information; the latest data is dis-

played on each button. The user can change the cat-

egory of the graph by clicking these buttons. For ex-

ample, when the user clicks the solar radiation button,

the graph switches from temperature data to solar ra-

diation data. Moreover, the items ”Field” and ”Sen-

sor” displayed above these buttons indicate the source

of the sensor information displayed on the main page.

The user can change the graph displayed on the main

page by switching the ”Field” or ”Sensor” item.

3.2.2 Dynamic Graphical Displays

SALATA uses amCharts

for graphical displays. am-

Charts is a javascript-based graph creation library

suitable for dynamic graphical displays. There are

two graphs(Fig.2) on the main page: the graph with a

white background at the top is the main graph, and the

graph with a gray background at the bottom is the sub-

graph. If the amount of data loaded for these graph is

large, the response will be slow. To avoid this, we set

a loading period T [min] and an interval of data sub-

sampling as t[min] (T ≥ t ≥ 10). Then, the amount of

data loaded at once is n, where n satisﬁes the follow-

ing Equation 2.

https://www.amcharts.com

n =

(2)

If the data interval in the database is smaller than

t, we take the average for t minutes. For example, the

Midori Cloud sensor units measure every two min-

utes, but if t = 10, the amount of data will be one ﬁfth

of the total data. In this way, SALATA can respond

quickly.

To provide ﬂexible viewing of the loaded data,

there are three ways to change the period displayed

in the main graph: the zoom, sub graph, and calen-

dar buttons. First, the zoom button at the bottom right

of the main graph can specify the exact period. We

can determine an arbitrary value of this button. In this

study, we decided on values of 3 hours, 12 hours, 1

day, 3 days, and 1 week. If the user clicks the 3 hours

button, the loaded data from the last 3 hours is dis-

played in the main graph. Second, the sub graph be-

low main graph can ﬂexibly change the period. The

period displayed in the main graph is changed by slid-

ing the bar on the side of the selection range in the

sub-graph or by dragging the selection range. Third,

the calendar button at the bottom left of the main

graph can specify any day of all periods measured by

the sensors. By specifying a date from this calendar

button, a user can load the data for the past T minutes

from the speciﬁed date and display the data from the

speciﬁed date in the main graph. As a result, the user

can change the period displayed in the main graph.

In addition, units, minimum values and maximum

SENSORNETS 2020 - 9th International Conference on Sensor Networks

116

values of range, and colors have been uniﬁed under

a category so that users can operate SALATA intu-

itively. The correspondence table is shown in Table

1. The minimum and maximum values were deter-

mined to be values that can cover all values except

the outliers from the data set collected by Hashimoto

et al(Hashimoto et al., 2016).

Table 1: Unit, Minimum, Maximum and Color by a Cate-

gory.

Category Unit Min Max Color

Temperature

◦

C 0 40 #E50000

Humidity % 0 100 #3A10F2

Humidity Deﬁcit g/m

0 20 #43B929

ppm 350 700 #68645A

Soil Temp

◦

C 0 40 #9A031E

Solar Radiation klux 0 150 #FF6000

3.2.3 Multiple-series Graphs

In a multiple-series graph(Fig.3), multiple categories

of multiple sensors can be displayed on a single

graph. This view is suitable for investigating the re-

lationship between categories on a time series. For

example, when an anomaly is detected in a humidity

deﬁcit measurement, it is easier to determine whether

it is being caused by temperature or humidity, by

displaying the temperature, humidity, and humidity

deﬁcit in the multiple-series graph.

The method for creating a multiple-series graph is

as follows. First, the user can transition to the analyt-

ical page by clicking ”Analyze” at the top of the main

page(Fig.1). When the user clicks the ”Create a new

graph” button on the analytical page, a mode selec-

tion window appears, which allows the user to enter

options for the multiple-series graph. There are three

options: farm ﬁeld, period, and categories. When the

user selects a farm ﬁeld, the category ﬁeld changes

to the sensor information corresponding to the farm

ﬁeld. When the user speciﬁes a period, the interval

of data sub-sampling is automatically determined ac-

cording to the period. This is done to avoid a slow

response due to an excessive amount of loaded data.

In this study, data are sub-sampled by the average so

that the number of samples becomes 150 or less.

3.2.4 Parallel Graphical Displays

The created multiple-series graphs are shown in

parallel(Fig.4). Speciﬁcally, the user can display a

multiple-series graph in parallel by creating another

multiple-series graph. This allows the user to com-

pare the available information with those of other

farm ﬁelds or with past data.

4 EXPERIMENT

In this experiment, we gave human subjects a set of

tasks based on actual use to evaluate the usability of

the dynamic display graph, the multiple-series graph,

and the parallel display graph.

4.1 Experimental Method

This experiment was conducted on 14 subjects aged

19 to 26 who had previously never used SALATA.

All of the subjects have operated personal computers

(PCs) and smartphones with graphical user interfaces

on a daily basis. The data set used in this study con-

sists of sensor information on temperature, humidity,

concentration, soil temperature, and solar radia-

tion observed in a tomato farm ﬁeld in Fukuoka City

from September 19, 2017 to February 1, 2018. In ad-

dition to these data, we also handled humidity deﬁcit

calculated from the temperature and humidity. The

parameters related to the loading of data for the dy-

namic display graph were set to t = 10 and T = 10080

(1 week). As a result, n = 1080.

The 14 subjects were divided into two groups.

One group was given only tasks and no lecture, and

the other group was given tasks after a lecture on how

to use SALATA. Subjects of both groups practiced for

three minutes before doing the ﬁrst task. There were

three types of tasks, and we measured the time re-

quired for completing each task. Task 1 is related to

the dynamic display graph on the top page, such as

”Please display the graph of solar radiation on De-

cember 13, 2018 on the top page.” Task 2 is related

to the multiple-series graph and the parallel display

graph on the analytical page, such as ”Please display

graphs of temperature and solar radiation from De-

cember 1, 2018 to December 31, 2018, and January

1, 2019 to January 31, 2019 so that you can compare

temperature and solar radiation.” Task 3 is to ﬁnd an

anomaly value of CO

assuming actual use, such as

”Normally, a CO

generator runs to maintain the CO

concentration in the greenhouse above a certain level.

In this data set, there are several days when this equip-

ment does not run.Please ﬁnd a day when this equip-

ment does not run by using the analysis page.” Both

subject groups performed Task 1 and Task 2 ﬁrst (ﬁrst

trial). After an interval of 30 or more minutes, the

subjects performed Task 1 and Task 2 again (second

trial). The ﬁrst trial and the second trial are same ex-

cept for the dates and categories. After those tasks,

Task 3 was performed, and ﬁnally, we conducted a

questionnaire.

The questionnaire was created based on the web

usability scale (WUS)(Nakagawa et al., 2001) and

SALATA: A Web Application for Visualizing Sensor Information in Farm Fields

117

Figure 3: Multiple-series graphs.

Figure 4: Parallel graphical displays.

modiﬁed for this experiment. WUS is a questionnaire

to evaluate the Web Usability developed in Japanese,

because the existing questionnaire (QUIS(Chin et al.,

1988) and SUMI(Kirakowski and Corbett, 1993)) was

developed in English and it is difﬁcult to adminis-

ter it in Japan. There are seven evaluation factors in

WUS: favorability, usefulness, reliability of contents,

operability, composition, visibility, and response. In

this experiment, we evaluated SALATA with ﬁve fac-

tors: operability, composition, visibility, response,

and functionality. There are two questions for each

evaluation factor, as listed below (5 means an invert

scale).

1. Operability

• This website is simple and easy to use.

• I immediately understood how to use this web-

site.

2. Composition

• This website is consistent.

• The structure of this website makes it is easy to

understand the available content.

3. Visibility

• The graphs and charts on this website are difﬁ-

cult to see. 5

• Using this website tires my eyes. 5

4. Response

• This website returns a quick response to my ac-

tions.

• When using this website, items may not be dis-

played correctly. 5

5. Functionality

• The functionality of this website is meets ex-

pectations.

• It took a long time to get the information I

wanted. 5

The scoring method was in accordance with WUS.

Each questionnaire item was scored on a scale of one

to ﬁve. In case of an inverted scale, the corrected

score was achieved by subtracting the original score

from six. Each factor score was calculated as the av-

erage of the scores of its two questionnaire items.

4.2 Results

Fig.5 shows the time taken to complete Task 1 in the

ﬁrst and second trials. In the ﬁrst trial, the differ-

ence between results with and without a lecture was

SENSORNETS 2020 - 9th International Conference on Sensor Networks

118

Figure 5: Result of the times to complete Task 1 in the ﬁrst

and second trials with and without a lecture.

Figure 6: Result of the times to complete Task 2 in the ﬁrst

and second trials with and without a lecture.

remarkable. The average without a lecture is 32.3 sec-

onds, the average with a lecture is 23.0 seconds; the

difference is thus seen to be 9.3 seconds. However, in

the second trial, the average was 17.6 seconds with-

out a lecture and 18.0 seconds with a lecture, and the

difference is almost negligible. Hence, the results in-

dicate that there was little dependence on the lecture.

In other words, the dynamic display graph can be op-

erated intuitively.

Fig.6 shows the ﬁrst and second trials required for

completing Task 2 with and without a lecture. In Task

2, there was almost no difference between the trials

with and without a lecture. The ﬁrst trial is 68.7 sec-

onds on average without a lecture and 64.4 seconds on

average with a lecture. The second trial is 59.7 sec-

onds on average without a lecture and 54.0 seconds

on average with a lecture. Hence, the multiple-series

graph and the parallel display graph can be operated

intuitively.

For Task 3, 12 out of 14 subjects were able to an-

swer correctly. Both subjects who could not answer

correctly were in groups without a lecture. Except for

those who did not answer correctly, the time required

for completing Task 3 with and without a lecture is

shown in Fig.7. The average completion time with-

out a lecture was 251.4 seconds, and that with a lec-

Figure 7: Result of the time to complete Task3 with and

without a lecture, except for those who did not answer cor-

rectly.

Figure 8: Result of the questionnaire and the average score

of WUS.

ture was 233.4 seconds. The answer rate of the group

without a lecture was lower. The time taken to com-

plete the task for the group was longer. This may be

due to the subjects not knowing that the data interval

of a multiple-series graph is automatically determined

according to the speciﬁed period. In addition, as a

whole, it took a long time to complete this task. From

this result, we found that a system which automati-

cally detects an anomaly from sensor information and

informs farm managers is necessary.

The result of the questionnaire and the average

score of WUS are shown in Fig.8. There is no av-

erage WUS score concerning functionality because

functionality is our original evaluation factor. Over-

all, SALATA was evaluated higher than the average

score for WUS, that is, the usability of SALATA is

high. This may be because SALATA has an intuitive

user interface, as indicated by the Task 1 and Task 2

results. In addition, since the score related to response

time was high, it showed SALATA responds quickly.

SALATA: A Web Application for Visualizing Sensor Information in Farm Fields

119

5 CONCLUSION

In this study, we introduced SALATA, a web applica-

tion for visualizing sensor information and farm work

information in farm ﬁelds. We explained and eval-

uated SALATA’s function of for displaying various

graphs of sensor information. The experimental re-

sults showed its high usability supported by the in-

tuitive user interface with quick response. Proposed

future work includes the following:

• Development and evaluation of functions of

searching, ﬁltering, and sorting for farm work in-

formation

• Development of a function to display graphs of

processed data such as cumulative temperature

• Development of a function to allow searching

similar graphs

In addition, we are planning to apply SALATA to

an agricultural high school to support lectures. Col-

lecting and visualizing sensor information and farm

work information can help students in ﬁnding causal

relationships between them.

ACKNOWLEDGEMENTS

This work was supported by JSPS KAKENHI Grant

Number JP18H04117 and JP17H01768.

We would like to thank Mr. Yano, Itoshima Agri-

cultural High School, Japan.

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