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The Journal of Agricultural Sciences – Sri Lanka
Vol. 19, No 1, January 2024. Pp 107-117
https://doi.org/10.4038/jas.v19i1.9702
Application of Global Navigation Satellite System (GNSS) Based Mobile Tracking to
Improve the geo-traceability of the Mango Supply Chain: A Case Study
Y.M.P. Samarasinghe1*, C.A.K. Dissanayake2 and M.M. Herath1
Received:08th April 2022 / Accepted: 03rd November 2023
ABSTRACT
Purpose: Location tracing of agricultural supply chains is vital to ensure food safety. The mango supply
chain in Sri Lanka involves many intermediaries and lacks traceability. GNSS-based mobile tracking is a
potential technique to assure the quality of fresh produce through improved geo-traceability. Therefore,
this study aimed to identify the feasibility of mobile tracking to enhance the geo-traceability of the
‘Karthakolomban’ mango.
Research Method: Supply chains were chosen based on mango collectors in the Kurunegala District.
Movements of mango were traced by real-time tracking of supply chain actors using the software developed
to obtain the GPS location of mobile phones. Feasibility for mobile tracking was assessed using, location
data, origin, route, speed, unnecessary delays and movements.
Findings: Omaragolla mango supply chain was highly dynamic, where most of the actors, routes and
origins varied without pre-planning. Only 67% of considered supply chains were successfully tracked.
The rest was unsuccessful due to a lack of technical know-how of supply chain actors and signal failures.
Unnecessary movements yielding 13% of additional distance and cost were observed in 25% of the supply
chains. Mobile tracking enabled the identification of movements of mango, which can be used for preplanning, monitoring of the routes of actors and ultimately ensuring geo-traceability.
Research Limitations: Even though geo-traceability improves the traceability of the fruit supply chain,
there are other methods to ensure traceability. Therefore, should be tested separately as well as coupled
with the current approach.
Originality/value: The findings of the case study can be guided to implement mobile tacking for any fixed
fruits and vegetables supply chain to improve the geo-traceability aiming at the food safety of the country.
Keywords: Food safety, Real-time tracking, Supply chain actors, Traceability, Omaragolla
INTRODUCTION
The global concern for food safety and quality
has increased during past decades due to the
abundance of food scandals (Zhang et al., 2020).
Therefore, a growing demand is created for
efficient traceability systems (Aung and Chang,
2014) that ensure the confidence of consumers to
purchase food commodities. Food traceability is
defined as “the ability to guarantee that products
moving along the food supply chain are both
traced and tracked” (Bosona and Gebresenbet,
2013). ISO 8402 defines traceability as the
1*
National Institute of Post-Harvest Management, Research and
Development Center, Jayanthi Mawatha, Anuradhapura (50000),
Sri Lanka
[email protected]
2
Department of Animal and Food Sciences, Faculty of Agriculture,
Rajarata University of Sri Lanka, Anuradhapura (50000), Sri
Lanka
https://orcid.org/0000-0001-9782-664X
107
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The Journal of Agricultural Sciences – Sri Lanka, 2024, Vol. 19 No 1
“ability to trace the history, application or location
of an entity, utilizing recorded identification
throughout a complete or partial supply chain”.
Food traceability is advantageous for managerial
decision making and it enhances the coordination
between buyer and supplier (Rábade and Alfaro,
2006).
Tracing of exact location and tracking the
true history of a product throughout the entire
supply chain is an important requirement of a
good traceability system (He et al., 2009). With
technological advancement, the Geographical
Information System (GIS) has become a
powerful tool that supports logistic planning
through its capability to manage geographical
data (Caputo et al., 2003) where the concept of
geo-traceability has evolved. Here, the Global
Navigation Satellite System (GNSS) is used for
real-time tracking of the transportation system,
thereby tracking the outdoor location of products
through the supply chain (Ahmed et al., 2020;
Gnimpieba et al., 2015). According to Kandel et
al. (2011), continuous tracking of the supply chain
supports last-mile route planning, minimizing
delays, and increasing security through improved
transparency.
When considering the fruit and vegetable
supply chain in Sri Lanka, most of the fruits
and vegetables available in the market consist
of quality defects, thus leaving the majority of
customers to have less access to good quality
fruits and vegetables. Mango is one of the major
fruits in the country that has a higher demand in
the local market as well as the export market.
Furthermore, Karthakolomban is the most
common and highly demanded mango variety
in the country. However, one of the major
restrictions that demote the mango industry is
the poor quality of fresh mango which increases
the postharvest loss and other market issues
(Vidanapathirana et al., 2018; Herath et al.,
2021). Some of the quality defects are bound to
the point of origin whereas some occur between
the place of origin and the market. Hence it is
important to uniquely identify the reasons behind
those blemishes and implement solutions.
Furthermore, the mango supply chain in Sri
Lanka is mainly based on collector groups
who collect mangoes from scattered mango
cultivations all around the country and distribute
them to consumers in different areas through
wholesalers and retailers. Also, the collection
process is done without preplanning. Therefore,
the collection process involves a considerable
amount of traveling, which results directly in
reducing the postharvest quality of fresh fruits
and also adds cost to the consumer price. Thus,
tracking the location information along the
supply chain will help to identify the occurrence
of unnecessary movements and unnecessary
time delays, thereby reducing postharvest losses,
improving fruit quality and reducing added costs
through managing the transportation process.
GNSS-based real-time tracking is used in diverse
fields of applications. Goletz and Ehebrecht
(2020) used GPS loggers to track micro-informal
transportation in Dar es Salaam city of Tanzania.
Although their application was technically
successful, they faced difficulties since operators
were required to operate GPS loggers and a large
sample size couldn’t be achieved. Thus, the use
of GPS loggers might not be a successful way
of tracking the mango supply chain, where a
large number of players participate. Realini
et al., (2017) studied the precision of Nexus
9 smart devices for positioning and obtained
decimetre-level accuracy and they recommend
it for rapid-static surveys. Therefore, the use
of smart mobile phones can be a cost-effective
solution for tracking the mango supply chain.
Also, limited research pieces of evidence were
found to improve the geo-traceability of the fresh
fruits supply chain in Sri Lanka and this research
attempt would be highly important as the initial
step towards fresh fruit geo-traceability in Sri
Lanka.
Thus, this case study was conducted aiming to
identify the feasibility of using mobile tracking
as a tool to improve the geo-traceability of the
fresh mango supply chain instead of using
high-cost GPS loggers or vehicle tracking
which are not cost-effective. In this study, we
planned to identify the feasibility of optimizing
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Y.M.P. Samarasinghe, C.A.K. Dissanayake and M.M. Herath
the movements along the supply chain through
mobile tracking, to identify constraints towards
geo-traceability development, and to provide
recommendations to overcome identified
drawbacks for geo-traceability development of
Omaragolla Karthakolomban supply chain.
MATERIALS AND METHODS
Selection of Supply Chain for the Case Study
The National Institute of Post-Harvest
Management which functions under the Ministry of
Agriculture had conducted a development project
on the Improvement of supply and value chain
management practices of Mango in Sri Lanka,
during 2016-2018. Under this project, the mango
collector groups scattered throughout the country
were identified and developed by introducing
improved postharvest technologies. Omaragolla
mango processing zone in Kurunegala District
was the first collector group addressed by this
project. According to the field observations, the
community in this mango processing zone is
well-organized with their income generation,
which is mainly based on island-wide mango
collection, primary processing (mainly ripening,
sorting, grading and packaging) and marketing.
Further, according to Kularathne et al. (2018),
the Omaragolla mango processing zone handles
a large amount of mango which approximates to
around 0.096 million mango fruits per collector
per month that is supplied to the local market in
Sri Lanka. The Mango Processers’ Association
consists of 32 members with many non-member
collectors in the community. Thus, the association
alone supplies more than 3.072 million mango
fruits to the market in one month. Therefore, this
mango processing zone was selected for the case
study. From the 32 members of the collectors’
association, two mango collectors were selected
based on six criteria; willingness to participate,
highest links with growers in the collector
group (linked with more than 200 growers),
highest mango handling rate in the collector
group, regular collection within the season, and
practicing mango collection for a long time.
The supply chain taken for the study was
selected based on the two selected collectors
of the Omaragolla mango processing zone.
Growers in contact with these collectors were
identified in Anuradhapura and Kurunegala
Districts. Therefore, the growers were selected
from these two districts for the study based on
convenience, willingness to participate, and
applicability of mobile tracking at the operative
stage of the supply chain. The main wholesalers
of these two collectors were in the Colombo
district. Therefore, wholesalers were selected
from Colombo. Retailers were thus identified by
forward tracing through the supply chain, who
were in the Gampaha district.
Implementation of a Mobile Tracking System
Different types of location and route tracking
technologies are available at present (mobile
tracking, use of GPS loggers, vehicle tracking)
(Tian, 2016; Kandel et al., 2011; Al Rashed et al.,
2013). The mobile tracking method was applied
for this pilot study due to its cost-effectiveness
and it can be implemented by using already
available smartphones with supply chain actors.
Mobile tracking software was developed that is
capable of tracking the GPS location of the mobile
phone through a consultant thereby capable
of tracking the movement of the supply chain
actor. The developed app comprised a mobile
application and a web application. The web
application allows the system administrator to
view the traveling paths, distances, locations, and
time that was spent in each location. The mobile
application collects location information of the
registered mobile devices. Thus, this application
was installed on the players’ smartphones, and
players were asked to enable the location service
of the phone and the data connection during the
operation of the supply chain. Collected location
information is passed from the mobile application
to the web application. Actors of the selected
supply chain were trained to achieve accurate
functioning of the mobile app. Mobile tracking
was implemented in the mango harvesting season
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The Journal of Agricultural Sciences – Sri Lanka, 2024, Vol. 19 No 1
from May to July.
The physical movement of the mango was
tracked by following the path of the supply chain
actor through mobile tracking. The tracking was
done at three (03) stages of the supply chain; i.e.
Grower to the collector through the collector,
collector to wholesaler through the transport
service provider, and whole seller to retailer
through the retailer. Location tracking data was
verified by random tracking through phone calls.
Mobile tracking software resulted in point data
that contained location and time information
only. Therefore, tracking information was
processed with Microsoft Excel and ArcGIS 10.0
software. Origin of the product, route followed,
speed of movements, unnecessary time delays,
and unnecessary movements were derived for
further analysis. Point data were converted to line
data attributed to the location and time. Speed
calculation was performed in Microsoft Excel
since it was not a straightforward procedure in
ArcGIS. Later these speed data were exported
to ArcGIS. Mapping was done to visualize the
findings.
Feasibility and drawbacks of mobile tracking
were identified in improving the geo-traceability
of the selected ‘Omaragolla Karthakolomban
Mango Supply Chain’ through personal
communication. Thereafter, suggestions were
provided to improve the functionality of the
mobile tracking to improve the geo-traceability
of fresh produce.
RESULTS AND DISCUSSIONS
Characterization of
Mango Supply Chain
Selected
Omaragolla
The selected “Omaragolla Karthakolomban
Supply Chain” (Figure 01) provided fresh mango
for the local market. The study revealed unique
functions played by different actors. Selected
collectors collected mangoes from different
areas of the country in both ‘mango seasons’,
the first season during May-July, and the second,
during November-February. Mainly, mini trucks
(with 1000 kg payload capacity) were used for
the transportation of mangoes from the field
to collecting centers. Mango harvesting was
conducted depending on the availability of
mango. On average, mango collection was done
in 20 days per month within the season. The
average distance travelled per day was around
100 km and it ranged from 20 km to 200 km
per day. This traveling added around 15,000.00
LKR to the value chain per month as fuel cost.
In addition to this, the transport cost was further
increased when hired vehicles and hired laborers
were used.
When considering Karthakolomban growers in
the selected districts, they were predominantly
scattered home garden-level growers and a few
numbers of orchard-level growers. Growers
who participated in the study got involved in
crop management practices but not in harvesting
operations. The collector was responsible
for harvesting, transportation of mangoes to
the collecting center, primary processing of
mangoes, and distribution of mangoes to the
wholesale market concerning the selected supply
chain. This characteristic can be used positively
for future reformations of the supply chain to fix
the best path to different farms by considering the
distance and convenience. Hired laborers were
used by the collectors for the above-mentioned
operations. Own vehicles or hired vehicles were
used by the collector to transport mangoes from
the field to the collectors’ pack house in the
Omaragolla area.
After completing pack house operations,
mangoes were transported to the wholesale
market (Manning market in Pettah, Colombo)
through a transport service provider on a hired
basis. The wholesaler was a commission-based
broker that participated in the mango supply
chain, from whom the roadside retailers acquire
mangoes for their retail outlets. The supermarket
chain in the country was also linked to the chain
where mangoes were directly transported by
collectors to supermarket collecting centers,
though that amount is proportionally minute.
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Y.M.P. Samarasinghe, C.A.K. Dissanayake and M.M. Herath
Application of Mobile Tracking for Omaragolla
Supply Chain and the Feasibility to Improve the
Geo-traceability
Two types of travels could be identified in the
selected chains: movements of mango and
movements of supply chain actors (Fig. 01). Fig.
02 visualizes the supply chain in a spatial context
and depicts the movement of each supply chain
actor along the supply chain.
Mobile tracking didn’t help to track indoor
processes (sorting, grading, artificial ripening,
and packaging) practiced at the pack houses that
take an average of 2 days of duration. Therefore,
other technologies such as barcoding, QR
coding, RFID, Internet of Things technology
(IoT), and blockchain technology (He et al.,
2009; Tsang et al., 2019; Tian, 2016; Hsu, et al.,
2008) can be combined to improve the reliability
in the future. Helo and Shamsuzzoha, (2020)
proposed a system that integrates the latest
technologies (RFID, IoT, GPS and blockchain)
for real-time tracking, authentic data sharing,
and facilitating transactions in supply chains.
This kind of well-developed system can provide
complete traceability from start to end. However,
implementing such a sophisticated traceability
Figure 01:
system is far ahead related to the Omaragolla
Mango supply chain. Especially, since it is not
well defined from farm to pack house. Also, the
awareness of traceability among players and the
presence of principle traceability requirements
such as data recording, product identification,
data integration, and accessibility (Islam and
Cullen, 2021) were not observed among players.
Therefore, traceability development of the
selected chain has to be approached with a simple,
cost-effective, and user-friendly approach where
mobile tracking is a good initiative that can be
implemented with an already available network
of smart mobile phones.
When considering mango collection by selected
collectors, the majority of the mangoes are
collected from rural areas with poor road
conditions that may cause heavy losses.
However, preplanning of the collection process
can be used to avoid roads with poor condition
thereby minimizing the distance travelled
through poor condition roads. Elik et al. (2019)
also stated that the unavailability of appropriate
transportation means, poor road condition and
inefficient logistics management causes losses of
perishables through the supply chain.
Movement of mango and supply chain actors in ‘Omaragolla Karthakolomban Supply
Chain’ selected for the case study (ORCH = Orchard; HG = Home Garden; Trpt=
Transporter; Coll= Collector)
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The Journal of Agricultural Sciences – Sri Lanka, 2024, Vol. 19 No 1
Figure 02:
Map of Omaragolla Karathakolomban supply chain with the spatial distribution of
selected supply chain entities
Both advantages and disadvantages were
identified regarding the use of mobile phones for
supply chain movement tracking. Mobile tracking
system enabled identification of location and
time aspects of the Omaragolla mango supply
chain. Thereby it was able to derive the start
of the journey, and the route followed and also
Figure 03:
to detect unnecessary movements and mango
collection points and other stops within the
journey, destination, and duration that the supply
chain had operated (Fig. 3. and Fig. 4.). These
capabilities can be positively used in improving
supply chain traceability.
Omaragolla Karthakolomban chain functioned from 01st July 2019 to 4th July 2019 based
on collecting center 1
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Y.M.P. Samarasinghe, C.A.K. Dissanayake and M.M. Herath
Figure 04:
Omaragolla Karthakolomban chain functioned from 3rd July 2019 to 6th July 2019 based
on collecting center 2
Ten complete supply chains were planned to be
tracked. However, only 50% were successful due
to technical constraints and mobile signal issues.
were identified to improve the geo-traceability of
mango fruits. These constraints and problems are
discussed below.
All the observed supply chains consisted of a day
mango collection process. Thus it had taken 3
days on average to move mangoes from grower
to retailer. However, both collectors stated that
collection may take more than one day depending
on the distance traveled and availability of mangos
in the areas traveled. Nevertheless, unnecessary
prolonging of the collection process may cause
quality deterioration (Aung and Chang, 2014).
The dynamic nature of the selected Omaragolla
Karthakolomban supply chain was a major barrier
to achieving geographical traceability via mobile
tracking. Mango collection was done in an adhoc manner where different growers were linked
to the supply chain without prior notice. Hence, it
was unable to identify defined growers. This was
mainly due to the unavailability of a sufficient
number of contract growers and variations in
the availability of mangos. Also, Possibilities
were identified to overlap collection routes from
different collector groups within the Maravilla
mango processing zone creating inefficiencies.
Therefore, an extensive database that includes
information on all the growers, in real-time is
required to be prepared before achieving the geotraceability.
With the findings of the study, it was able to
identify 6-time segments (Table 01.) that support
decision-making on minimizing unnecessary
time expenses. However, identification of time
spent at the retailer and thereby the total time
taken from farm to plate was not possible through
mobile tracking alone.
Feasibility Analysis to Use Mobile Tracking for
Omaragolla Supply Chain
Although the introduced mobile tracking system
was successful in tracking the physical movement
of players, several constraints and problems
Apart from this, the mango collection process
was done often through hired laborers where the
person was changed dynamically based on labor
availability. This greatly constrained the mobile
tracking of the Omaragolla Karthakolomban
chain since location tracking was based on the
mobile phone of the person traveling.
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Table 01:
Time taken to move between different stages in the supply chain.
Supply chain entity
GR
Time segment
T1
T2
T3
T4
T5
T6
Day of the chain
1st
1st
1st to 3rd
3rd
3rd and 4th
4th
6hr
6 hr and 35
min
1 hr and 34
min
Time duration spent
Coll
WS
2 hrs and 24 3 hr and 21 48 hr and 38
min
min
min
RT
GR – grower, Coll – collector, WS – wholesaler, RT – retailer, T1 – time spent at grower from arriving at grower to departure, T2 – the
time from grower to collector, T3- time spent at the collector, T4 – time from collector to wholesaler, T5 – time spent at wholesaler, T6 time from wholesaler to retailer
Selected Omaragolla mango supply chain
supplied fresh mango to the market where no major
processing activities were involved. However,
the mixing and splitting of mango lots collected
from various geographical locations constrained
the geographical traceability by disabling the
identification of the exact origin. Olsen and
Borit (2018) have emphasized the importance
of having a mechanism to identify each batch, a
mechanism to record transformations and identify
unit attributes. Therefore, the Omaragolla mango
supply chain should be improved by considering
the above facts to attain geographical traceability
in the future.
However, for the selected supply chain a very basic
level product identification method was identified
at the level of collector. The collector displayed
his name/brand which is called ‘Vilasan’. The
importance of this is that the wholesaler and
the retailer can identify the collector that is
responsible for the set of mangoes. However,
recalling was impossible up to the grower level
since many mixing and splitting operations
are done at the collector level without keeping
track. Hence, the use of product identification
technologies is needed to improve the traceability
of the Omaragolla Karthakolomban chain. The
use of product identification technologies, that
identify the product or batch with a unique code
to be shared within the chain, is an essential
requirement for tracing the history and tracking
the physical location of products (Opara, 2003;
Kelepouris et al., 2007). Therefore, a simple
product identification technique such as simple
tagging or barcoding can be used as the initial
step, despite the highly complicated technologies
like Radio Frequency Identification (RFID)
technologies.
Geo-traceability information will not be
meaningful
without
other
conventional
traceability data. Hence, record keeping is
mandatory to forward the pass of information
through the supply chain and to be able to trace
it back. However, Record keeping of traceable
information was not observed in the selected
chain which was identified as a shortcoming.
Hence, the identification of traceable resource
units (TRUs) (Dabbene et al., 2014), the use of
suitable product identification technology (Khan
et al., 2018), record keeping on transformations
(Hu et al., 2013), and sharing of necessary
information (Anica-Popa, 2012) are mandatory
requirements. These requirements should be
fulfilled starting from the grower and across
other intermediate entities within the supply
chain followed by mobile tracking of movements
for geo-traceability improvement.
Omaragolla mango supply chain consists of a
high degree of logistic uncertainty, which may
adversely affect the supply chain efficiency by
creating an extra cost and an extra distance to the
supply chain. According to Sanchez-Rodrigues
et al (2010), Extra distance increases fuel usage
thereby increasing the cost and CO2 emission
whereas extra time creates inefficiencies by not
fully utilizing available vehicle resources. Hence,
it is mandatory for future planning of mango
collection to minimize the uncertainty.
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Y.M.P. Samarasinghe, C.A.K. Dissanayake and M.M. Herath
CONCLUSION
The case study revealed that mobile tracking was
technically possible as an application of geotraceability in the selected chain. However, it was
not feasible to improve the geo-traceability of the
selected Omaragolla mango supply chain due to
several reasons; unavailability of fixed chains,
unavailability of fixed actors, logistic uncertainty,
and mixing and splitting of mango lots collected
from various geographical locations. Therefore,
mobile tracking can be implemented on already
available defined supply chains as an initial step.
Later on, more supply chains can be included
in the system. Simultaneously, an extensive
database is to be developed to enclose all the
information on the mango fresh fruit supply
chain including locational information and other
important attributes.
Meanwhile, mobile tracking has a huge feasibility
to be used in fixed fruits and vegetable supply
chains to improve as a cost-effective means of
tracking
Conflict of Interest
The authors declare that there is no conflict of
interest.
REFERENCES:
Ahmed, F., Phillips, M., Phillips, S. and Kim, K.Y. (2020). Comparative study of seamless asset
location and tracking technologies. Procedia Manufacturing, 51, 1138-1145. https://doi.
org/10.1016/j.promfg.2020.10.160
Al Rashed, M.A., Oumar, O.A. and Singh, D. (2013). A real-time GSM/GPS-based tracking system
based on a GSM mobile phone. In: Second International Conference on Future Generation
Communication Technologies (FGCT 2013). 65-68. DOI: 10.1109/fgct.2013.6767186
Anica-Popa, I. (2012). Food traceability systems and information sharing in the food supply
chain. Management & Marketing, 7(4), 749. Retrieved from https://www.proquest.com/
docview/1270850421?pq-origsite=gscholar&fromopenview=true (ISSN) 18420206
Aung, M. M. and Chang, Y. S. (2014). Traceability in a food supply chain: Safety and quality
perspectives. Food control, 39, 172-184. https://doi.org/10.1016/j.foodcont.2013.11.007
Bosona, T. and Gebresenbet, G. (2013). Food traceability as an integral part of logistics management
in food and agricultural supply chain. Food control, 33(1), 32-48. https://doi.org/10.1016/j.
foodcont.2013.02.004
Caputo, A. C., Pelagagge, P. M. and Scacchia, F. (2003). Integrating transport systems in supply chain
management software tools. Industrial Management & Data Systems, 103 (7), 503-515. https://
doi.org/10.1108/02635570310489205
Dabbene, F., Gay, P. and Tortia, C. (2014). Traceability issues in food supply chain
management: A review. Biosystems engineering, 120, 65-80. https://doi.org/10.1016/j.
biosystemseng.2013.09.006
Elik, A., Yanik, D.K., Istanbullu, Y., Guzelsoy, N.A., Yavuz, A. and Gogus, F. (2019). Strategies to
reduce post-harvest losses for fruits and vegetables. International Journal of Scientific and
Technological Research, 5(3), 29-39. DOI: 10.7176/jstr/5-3-04
115
The Journal of Agricultural Sciences – Sri Lanka, 2024, Vol. 19 No 1
Gnimpieba, Z.D.R., Nait-Sidi-Moh, A., Durand, D. and Fortin, J. (2015). Using Internet of Things
technologies for a collaborative supply chain: Application to tracking of pallets and containers.
Procedia Computer Science, 56, 550-557. https://doi.org/10.1016/j.procs.2015.07.251
Goletz, M. and Ehebrecht, D. (2020). How can GPS/GNSS tracking data be used to improve our
understanding of informal transport? A discussion based on a feasibility study from Dar es Salaam.
Journal of Transport Geography, 88, 102305. https://doi.org/10.1016/j.jtrangeo.2018.08.015
He, W., Tan, E. L., Lee, E. W. and Li, T. Y. (2009). A solution for integrated track and trace in the
supply chain based on RFID & GPS. In: 2009 IEEE Conference on Emerging Technologies &
Factory Automation. IEEE. 1-6. DOI:10.1109/etfa.2009.5347146
Helo, P. and Shamsuzzoha, A.H.M. (2020). Real-time supply chain—A blockchain architecture for
project deliveries. Robotics and Computer-Integrated Manufacturing, 63, 101909. https://doi.
org/10.1016/j.rcim.2019.101909
Herath, M.M., Dissanayake, C.A.K., Wasala, W.M.C.B., Somakanthan, N. and Gunawardhane, C.R.
(2021). The effect of improved postharvest practices on the postharvest losses and quality of
mango fruit in Sri Lanka’s supply chain. Journal of Horticulture and Postharvest Research,
4(4), 509-520. https://10.22077/jhpr.2021.4442.1222
Hsu, Y.C., Chen, A.P. and Wang, C.H. (2008). An RFID-enabled traceability system for the supply
chain of live fish. In: 2008 IEEE International Conference on Automation and Logistics. IEEE.
81-86. DOI: 10.1109/ical.2008.4636124
Hu, J., Zhang, X., Moga, L.M. and Neculita, M. (2013). Modeling and implementation of the vegetable
supply chain traceability system. Food Control, 30(1), 341-353. https://doi.org/10.1016/j.
foodcont.2012.06.037
Islam, S. and Cullen, J.M. (2021). Food traceability: A generic theoretical framework. Food Control,
123,107848. https://doi.org/10.1016/j.foodcont.2020.107848
Kandel, C., Klumpp, M., and Keusgen, T. (2011). GPS-based track and trace for transparent and
sustainable global supply chains. In: 2011 17th International Conference on Concurrent
Enterprising. IEEE. 1-8.
Kelepouris, T., Pramatari, K. and Doukidis, G. (2007). RFID‐enabled traceability in the food
supply chain. Industrial Management & data systems, 107(2), 183-200. https://doi.
org/10.1108/02635570710723804
Khan, S., Khan, M.I., Haleem, A. and Shuaib, M., (2018). Selection of traceable technology in the
food supply chain. In: IOP conference series: Materials science and engineering, Volume 404
(1), IOP Publishing. 012010. DOI:10.1088/1757-899x/404/1/012010
Kularathne, T.M., Dissanayake, C.A.K., Sivashankar, P.and Herath, M.M. (2018). Study on role and
empowerment of women involved in the mango supply chain in Omaragolla mango processing
zone. In: International research symposium on postharvest technology, National Institute of
Post-Harvest Management, Sri Lanka, 57-62.
116
Y.M.P. Samarasinghe, C.A.K. Dissanayake and M.M. Herath
Olsen, P. and Borit, M. (2018). The components of a food traceability system. Trends in Food Science
& Technology, 77, 143-149. https://doi.org/10.1016/j.tifs.2018.05.004
Opara, L.U. (2003). Traceability in agriculture and food supply chain: a review of basic concepts,
technological implications, and future prospects. Journal of Food Agriculture and Environment.1,
101-106.
Rábade, L.A. and Alfaro, J.A. (2006). Buyer–supplier relationship’s influence on traceability
implementation in the vegetable industry. Journal of Purchasing and Supply Management,
12(1), 39-50. https://doi.org/10.1016/j.pursup.2006.02.003
Realini, E., Caldera, S., Pertusini, L. and Sampietro, D. (2017). Precise GNSS positioning using smart
devices. Sensors, 17(10), 2434. https://doi.org/10.3390/s17102434
Sanchez‐Rodrigues, V., Potter, A. and Naim, M.M. (2010). The impact of logistics uncertainty on
sustainable transport operations. International Journal of Physical Distribution & Logistics
Management, 40 (1/2), 61-83. https://doi.org/10.1108/09600031011018046
Tian, F. (2016). An agri-food supply chain traceability system for China based on RFID & and
blockchain technology. In: 2016 13th international conference on service systems and service
management (ICSSSM), IEEE, 1-6. DOI:10.1109/icsssm.2016.7538424
Tsang, Y.P., Choy, K.L., Wu, C.H., Ho, G.T.S. and Lam, H.Y. (2019). Blockchain-driven IoT for f